List of References on Evolutionary Multiobjective Optimization

Carlos A. Coello Coello

        ccoello@cs.cinvestav.mx

CINVESTAV-IPN Departamento de Computación Av. IPN # 2508 Col. San Pedro Zacatenco México, D.F. 07300 MEXICO

June 22, 2017

[1] Gupta S.K. Aatmeeyata. Simulation and Optimization of an Industrial Nylon 6 Reactor: A Review. Polymer-Plastics Technology And Engineering, 37(2):201–239, 1998.

[2] M. Abachizadeh and M. Tahani. An Ant Colony Optimization Approach to Multi-objective Optimal Design of Symmetric Hybrid Laminates for Maximum Fundamental Frequency and Minimum Cost. Structural and Multidisciplinary Optimization, 37(4):367–376, January 2009.

[3] A. Abakarov, Y. Sushkov, S. Almonacid, and R. Simpson. Multiobjective Optimization Approach: Thermal Food Processing. Journal of Food Science, 74(9):E471–E487, November-December 2009.

[4] Babak Abbasi, Shahram Shadrokh, and Jamal Arkat. Bi-objective Resourceconstrained Project Scheduling with Robustness and Makespan Criteria. Applied Mathematics and Computation, 180(1):146–152, September 1 2006.

[5] Fazel Abbasi and Seyed Mehdi Hosseini. Optimal DG Allocation and Sizing in Presence of Storage Systems Considering Network Configuration Effects in Distribution Systems. IET Generation Transmission & Distribution, 10(3):617–624, February 18 2016.

[6] H.A. Abbass. An economical cognitive approach for bi-objective optimization using bliss points, visualization and interaction. Soft Computing, 10(8):687–698, June 2006.

[7] Hussein Abbass. Pareto-Optimal Approaches to Neuro-Ensemble Learning. In Yaochu Jin, editor, Multi-Objective Machine Learning, pages 407–427. Springer. Studies in Computational Intelligence, Volume 16, Berlin, 2006.

[8] Hussein A. Abbass. A Memetic Pareto Evolutionary Approach to Artificial Neural Networks. In The Australian Joint Conference on Artificial Intelligence, pages 1–12, Adelaide, Australia, December 2001. Springer. Lecture Notes in Artificial Intelligence Vol. 2256.

[9] Hussein A. Abbass. An Evolutionary Artificial Neural Networks Approach for Breast Cancer Diagnosis. Artificial Intelligence in Medicine, 25(3):265– 281, 2002.

[10] Hussein A. Abbass. The Self-Adaptive Pareto Differential Evolution Algorithm. In Congress on Evolutionary Computation (CEC’2002), volume 1, pages 831–836, Piscataway, New Jersey, May 2002. IEEE Service Center.

[11] Hussein A. Abbass. Pareto Neuro-Evolution: Constructing Ensemble of Neural Networks Using Multi-objective Optimization. In Proceedings of the 2003 Congress on Evolutionary Computation (CEC’2003), volume 3, pages 2074– 2080, Canberra, Australia, December 2003. IEEE Press.

[12] Hussein A. Abbass. Speeding Up Backpropagation Using Multiobjective Evolutionary Algorithms. Neural Computation, 15(11):2705–2726, November 2003.

[13] Hussein A. Abbass. An Inexpensive Cognitive Approach for Biobjective Optimization using Bliss Points and Interaction. In Parallel Problem Solving from Nature - PPSN VIII, pages 712–721, Birmingham, UK, September 2004. Springer-Verlag. Lecture Notes in Computer Science Vol. 3242.

[14] Hussein A. Abbass and Sameer Alam ad Axel Bender. MEBRA: Multiobjective Evolutionary-Based Risk Assessment. IEEE Computational Intelligence Magazine, 4(3):29–36, August 2009.

[15] Hussein A. Abbass and Kalyanmoy Deb. Searching under Multi-evolutionary Pressures. In Carlos M. Fonseca, Peter J. Fleming, Eckart Zitzler, Kalyanmoy Deb, and Lothar Thiele, editors, Evolutionary Multi-Criterion Optimization. Second International Conference, EMO 2003, pages 391–404, Faro, Portugal, April 2003. Springer. Lecture Notes in Computer Science. Volume 2632.

[16] Hussein A. Abbass and Ruhul Sarker. Simultaneous Evolution of Architectures and Connection Weights in ANNs. In The Artificial Neural Networks and Expert Systems Conference (ANNES’01), pages 16–21, Dunedin, New Zealand, November 2001.

[17] Hussein A. Abbass and Ruhul Sarker. The Pareto Differential Evolution Algorithm. International Journal on Artificial Intelligence Tools, 11(4):531–552, 2002.

[18] Hussein A. Abbass, Ruhul Sarker, and Charles Newton. PDE: A Paretofrontier Differential Evolution Approach for Multi-objective Optimization Problems. In Proceedings of the Congress on Evolutionary Computation 2001 (CEC’2001), volume 2, pages 971–978, Piscataway, New Jersey, May 2001. IEEE Service Center.

[19] M. F. Abbod, D. A. Linkens, and M. Mahfouf. Multi-Objective Genetic Optimization for Self-Organizing Fuzzy Logic Control. In Proceedings of UKACC Control’98, pages 1575–1580, University of Wales Swansea, UK, september 1998. IEE.

[20] N.R. Abburi and U.S. Dixit. Multi-objective optimization of multipass turning processes. International Journal of Advanced Manufacturing Technology, 32(9–10):902–910, April 2007.

[21] M. Lahanas abd D. Baltas and N. Zamboglou. A hybrid evolutionary algorithm for multi-objective anatomy-based dose optimization in high-dose-rate brachytherapy. Physics in Medicine and Biology, 48(3):399–415, February 7 2003.

[22] Mostafa I.H. Abd-El-Barr and Salman A. Khan. Design and analysis of a fault tolerant hybrid mobile scheme. Information Sciences, 177(12):2602– 2620, June 15 2007.

[23] Y.L. Abdel-Magid and M.A. Abido. Optimal Multiobjective Design of Robust Power System Stabilizers Using Genetic Algorithms. IEEE Transactions on Power Systems, 18(3):1125–1132, August 2003.

[24] Hossam Abdelgawad, Baher Abdulhai, and Mohamed Wahba. Multiobjective Optimization for Multimodal Evacuation. Transportation Research Record, 2196:21–33, 2010.

[25] Gh Abdollahi and M. Meratizaman. Multi-objective approach in thermoenvironomic optimization of a small-scale distributed CCHP system with risk analysis. Energy and Buildings, 43(11):3144–3153, November 2011.

[26] Wahabou Abdou, Christelle Bloch, Damien Charlet, and François Spies. Adaptive Multi-Objective Genetic Algorithm using Multi-Pareto-Ranking. In 2012 Genetic and Evolutionary Computation Conference (GECCO’2012), pages 449–456, Philadelphia, USA, July 2012. ACM Press. ISBN: 978-1-4503-1177-9.

[27] Wahabou Abdou, Christelle Bloch, Damien Charlet, and François Spies. Multi-Pareto-Ranking Evolutionary Algorithm. In Jin-Kao Hao and Martin Middendorf, editors, Evolutionary Computation in Combinatorial Optimization, 12th European Conference, EvoCOP 2012, pages 194–205, Málaga, Spain, April 11-13 2012. Springer. Lecture Notes in Computer Science Vol. 7245.

[28] Wahabou Abdou, Adrien Henriet, Christelle Bloch, Dominique Dhoutaut, Damien Charlet, and Francois Spies. Using an evolutionary algorithm to optimize the broadcasting methods in mobile ad hoc networks. Journal of Network and Computer Applications, 36(6):1794–1804, November 2011.

[29] Salwani Abdullah, Hamza Turabieh, Barry McCollum, and Paul McMullan. A Multi-objective Post Enrolment Course Timetabling Problems: A New Case Study. In 2010 IEEE Congress on Evolutionary Computation (CEC’2010), pages 435–441, Barcelona, Spain, July 18–23 2010. IEEE Press.

[30] Hassan K. Abdulrahim and Fuad N. Alasfour. Multi-Objective Optimisation of hybrid MSF-RO desalination system using Genetic Algorithm. International Journal of Exergy, 7(3):387–424, 2010.

[31] E. Abele and M. Fujara. Simulation-Based Twist Drill Design and Geometry Optimization. CIRP Annals-Manufacturing Technology, 59(1):145–150, 2010.

[32] Manuel Blanco Abello, Lam Thu Bui, and Zbignew Michalewicz. An Adaptive Approach for Solving Dynamic Scheduling with Time-varying Number of Tasks - Part I. In 2011 IEEE Congress on Evolutionary Computation (CEC’2011), pages 1703–1710, New Orleans, Lousiana, USA, 5-8 June 2011. IEEE Service Center.

[33] Manuel Blanco Abello, Lam Thu Bui, and Zbignew Michalewicz. An Adaptive Approach for Solving Dynamic Scheduling with Time-varying Number of Tasks - Part II. In 2011 IEEE Congress on Evolutionary Computation (CEC’2011), pages 1711–1718, New Orleans, Lousiana, USA, 5-8 June 2011. IEEE Service Center.

[34] Manuel Blanco Abello, Zbigniew Michalewicz, and Lam Thu Bui. A reactiveproactive approach for solving dynamic scheduling with time-varying number of Tasks. In 2012 IEEE Congress on Evolutionary Computation (CEC’2012), pages 1132–1141, Brisbane, Australia, June 10-15 2012. IEEE Press.

[35] M. A. Abido. A Niched Pareto Genetic Algorithm for multiobjective environmental/economic dispatch. International Journal of Electrical Power & Energy Systems, 25(2):97–105, February 2003.

[36] M. A. Abido. Multiobjective particle swarm for environmental/economic dispatch problem. In 2007 Conference Proceedings IPEC, Vols 1-3, pages 1385– 1390, Singapore, Singapore, December 03-06 2007. IEEE. ISBN 978-981-05-9423-7.

[37] M. A. Abido. Multiobjective Particle Swarm Optimization for enviromental/economic dispatch problem. Electric Power System Research, 79(7):1105– 1113, July 2009.

[38] M. A. Abido and N. A. Al-Ali. Multi-Objective Differential Evolution for Optimal Power Flow. In International Conference on Power Engineering, Energy and Electrical Drives (POWERENG ’09), pages 101–106, Lisbon, Portugal, March 18-20 2009. IEEE Computer Society.

[39] M. A. Abido and N. A. Al-Ali. Multi-Objective Optimal Power Flow Using Differential Evolution. Arabian Journal for Science and Engineering, 37(4):991–1005, June 2012.

[40] M. A. Abido and J. M. Bakhashwain. Optimal VAR dispatch using a multiobjective evolutionary algorithm. International Journal of Electrical Power & Energy Systems, 27(1):13–20, January 2005.

[41] M. A. Abido and Ashraf M. Elazouni. Multiobjective Evolutionary Finance-Based Scheduling: Entire Projects’ Portfolio. Journal of Computing in Civil Engineering, 25(1):85–97, January-February 2011.

[42] M.A. Abido. A new multiobjective evolutionary algorithm for environmental/economic power dispatch. In Power Engineering Society Summer Meeting, volume 2, pages 1263–1268. IEEE, 2001.

[43] M.A. Abido. Environmental/economic power dispatch using multiobjective evolutionary algorithms: A comparative study. In 2003 IEEE-Power-Engineering-Society General Meeting, pages 920–925, Toronto, Canada, July 13-17 2003. IEEE Press. ISBN 0-7803-7989-6.

[44] M.A. Abido. Multiobjective Evolutionary Algorithms for Electric Power Dispatch Problem. IEEE Transactions on Evolutionary Computation, 10(3):315– 329, June 2006.

[45] M.A. Abido. Multiobjective Optimal VAR Dispatch Using Strength Pareto Evolutionary Algorithm. In 2006 IEEE Congress on Evolutionary Computation (CEC’2006), pages 2745–2751, Vancouver, BC, Canada, July 2006. IEEE.

[46] M.A. Abido. Two-Level of Nondominated Solutions Approach to Multiobjective Particle Swarm Optimization. In Dirk Thierens, editor, 2007 Genetic and Evolutionary Computation Conference (GECCO’2007), volume 1, pages 726–733, London, UK, July 2007. ACM Press.

[47] M.A. Abido. Multiobjective particle swarm optimization for optimal power flow problem. In 2008 12th International Middle East Power System Conference, pages 485–489, Aswan, Egypt, March 12-15 2008. IEEE Press. ISBN 978-1-4244-1933-3.

[48] M.A. Abido. Multiobjective particle swarm optimization with nondominated local and global sets. Natural Computing, 9(3):747–766, September 2010.

[49] M.A. Abido. Multiobjective Particle Swarm Optimization for Optimal Power Flow Problem. In Bijaya Ketan Panigrahi, Yuhui Shi, and Meng-Hiot Lim, editors, Handbook of Swarm Intelligence. Concepts, Principles and Applications, pages 241–268. Springer-Verlag, Berlin, Germany, 2011. ISBN 978-3-642-17389-9.

[50] M.A. Abido and J.M. Bakhashwain. A Novel Multiobjective Evolutionary Algorithm for Optimal Reactive Power Dispatch Problem. In Proceedings of the 2003 10th IEEE International Conference on Electronics, Circuits and Systems, 2003 (ICECS 2003), volume 3, pages 1054–1057. IEEE, December 2003.

[51] Mohammad A. Abido. Multiobjective Evolutionary Algorithms for Electric Power Dispatch Problem. In Christine L. Mumford and Lakhmi C. Jain, editors, Computational Intelligence Collaboration, Fusion and Emergence, Studies in Computational Intelligence (SCI), pages 47–82. Springer, Berlin, 2009. ISBN 978-3-642-01799-5.

[52] Lixin Tang abnd Xianpeng Wang. A Hybrid Multiobjective Evolutionary Algorithm for Multiobjective Optimization Problems. IEEE Transactions on Evolutionary Computation, 17(1):20–45, February 2013.

[53] M.A. Abo-Sinna, Y. Yousria Abo-Elnaga, and A.A. Mousa. An interactive dynamic approach based on hybrid swarm optimization for solving multiobjective programming problem with fuzzy parameters. Applied Mathematical Modelling, 38(7-8):2000–2014, April 1 2014.

[54] M. Abouhamze and M. Shakeri. Multi-objective stacking sequence optimization of laminated cylindrical panels using a genetic algorithm and neural networks. Composite Structures, 81(2):253–263, November 2007.

[55] Mohamed Abouhawwash and Kalyanmoy Deb. Karush-Kuhn-Tucker Proximity Measure for Multi-Objective Optimization Based on Numerical Gradients. In 2016 Genetic and Evolutionary Computation Conference (GECCO’2016), pages 525–532, Denver, Colorado, USA, 20-24 July 2016. ACM Press. ISBN 978-1-4503-4206-3.

[56] R. Aboulaich, R. Ellaia, and S. El Moumen. The Mean-Variance-CVaR model for Portfolio Optimization Modeling using a Multi-Objective Approach Based on a Hybrid Method. Mathematical Modelling of Natural Phenomena, 5(7):103–108, 2010.

[57] Ajith Abraham, Crina Grosan, Sang Yong Han, and Alexander Gelbukh. Evolutionary Multiobjective Optimization Approach for Evolving Assemble of Intelligent Paradigms for Stock Market Modeling. In Alexander Gelbukh, Álvaro de Albornoz, and Hugo Terashima-Marín, editors, MICAI 2005: Advances in Artificial Intelligence, pages 673–681, Monterrey, México, November 2005. Springer. Lecture Notes in Artificial Intelligence Vol. 3789.

[58] Ajith Abraham and Lakhmi Jain. Evolutionary Multiobjective Optimization. In Ajith Abraham, Lakhmi Jain, and Robert Goldberg, editors, Evolutionary Multiobjective Optimization. Theoretical Advances and Applications, pages 1–6. Springer, USA, 2005.

[59] Ajith Abraham, Lakhmi Jain, and Robert Goldberg, editors. Evolutionary Multiobjective Optimization. Theoretical Advances and Applications. Springer, USA, 2005. ISBN 1-85233-787-7.

[60] Ajith Abraham, Hongbo Liu, Crina Grosan, and Fatos Xhafa. Nature Inspired Meta-heuristics for Grid Scheduling: Single and Multi-objective Optimization Approaches. In Fatos Xhafa and Ajith Abraham, editors, Metaheuristics for Scheduling in Distributed Computing Environments, pages 247–272. Springer. Studies in Computational Intelligence Vol. 146, Berlin, Germany, 2008.

[61] J.A.R. Abraham and I.C. Parmee. Extraction of Emerging Multi-Objective Design Information from COGA Data. In I.C. Parmee, editor, Adaptive Computing in Design and Manufacture VI, pages 221–233, London, 2004. Springer.

[62] Ahmad Abubaker, Adam Baharum, and Mahmoud Alrefaei. Automatic Clustering Using Multi-Objective Particle Swarm and Simulated Annealing. Plos One, 10(7), July 1 2015. Article Number: e0130995.

[63] Ahmed R. Abul’Wafa. Optimization of economic/emission load dispatch for hybrid generating systems using controlled Elitist NSGA-II. Electric Power Systems Research, 105:142–151, December 2013.

[64] Giovanni Acampora, Hisao Ishibuchi, and Autilia Vitiello. A Comparison of Multi-Objective Evolutionary Algorithms for the Ontology Meta-Matching Problem. In 2014 IEEE Congress on Evolutionary Computation (CEC’2014), pages 413–420, Beijing, China, 6-11 July 2014. IEEE Press. ISBN 978-1-4799-1488-3.

[65] Adnan Acan and Ahmet Unveren. Evolutionary Multiobjective Optimization with a Segment-Based External Memory Support for the Multiobjective Quadratic Assignment Problem. In 2005 IEEE Congress on Evolutionary Computation (CEC’2005), volume 3, pages 2723–2729, Edinburgh, Scotland, September 2005. IEEE Service Center.

[66] Merin Achankunju, R. Pushpalakshmi, and A. Vincent Antony Kumar. Particle Swarm Optimization based secure QoS clustering for Mobile Ad hoc Network. In 2013 International Conference on Communications and Signal Processing (ICCSP 2013), pages 315–320, Melmaruvathur, India, April 3-5 2013. IEEE Press. ISBN 978-1-4673-4866-9.

[67] Jesús Antonio Acosta Sarmiento. Aprendizaje de Particiones Difusas para Razonamiento Inductivo. PhD thesis, Departament d’Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Barcelona, Spain, December 2006. (In Spanish).

[68] M. Rezaei Adaryani and A. Karami. Artificial bee colony algorithm for solving multi-objective optimal power flow problem. International Journal of Electrical Power & Energy Systems, 53:219–230, December 2013.

[69] J. Adeyemo and F.A.O. Otieno. Multi-Objective Differential Evolution Algorithm for Solving Engineering Problems. Journal of Applied Sciences, 9(20):3652–3661, 2009.

[70] J. A. Adeyemo and O. O. Olofintoye. Evaluation of Combined Pareto Multiobjective Differential Evolution on Tuneable Problems. Internatioanl Journal of Simulation Modelling, 13(3):276–287, September 2014.

[71] Josiah Adeyemo, Faizal Bux, and Fred Otieno. Differential evolution algorithm for crop planning: Single and multi-objective optimization model. International Journal Of The Physical Sciences, 5(10):1592–1599, September 4 2010.

[72] Josiah Adeyemo and Fred Otieno. Differential Evolution algorithm for solving multi-objective crop planning model. Agricultural Water Management, 97(6):848–856, June 2010.

[73] A. M. Adham, N. Mohd-Ghazali, and R. Ahmad. Performance optimization of a microchannel heat sink using the Improved Strength Pareto Evolutionary Algorithm (SPEA2). Journal of Engineering Thermophysics, 24(1):86–100, January 2015.

[74] M. A. Adibi, M. Zandieh, and M. Amiri. Multi-objective scheduling of dynamic job shop using variable neighborhood search. Expert Systems With Applications, 37(1):282–287, January 2010.

[75] Salem Adra. Optimisation techniques for gas turbine engine control systems. Master’s thesis, Department of Computer Science, The University of Sheffield, UK, 27 August 2003.

[76] Salem F. Adra, Tony J. Dodd, Ian A. Griffin, and Peter J. Fleming. Convergence Accelaration Operator for Multiobjective Optimization. IEEE Transactions on Evolutionary Computation, 13(4):825–847, August 2009.

[77] Salem F. Adra and Peter F. Fleming. A Diversity Management Operator for Evolutionary Many-Objective Optimisation. In Matthias Ehrgott, Carlos M. Fonseca, Xavier Gandibleux, Jin-Kao Hao, and Marc Sevaux, editors, Evolutionary Multi-Criterion Optimization. 5th International Conference, EMO 2009, pages 81–94. Springer. Lecture Notes in Computer Science Vol. 5467, Nantes, France, April 2009.

[78] Salem F. Adra and Peter J. Fleming. Progressive diversity management in evolutionary multiobjective optimisation. In 2010 IEEE Congress on Evolutionary Computation (CEC’2010), pages 3852–3859, Barcelona, Spain, July 18–23 2010. IEEE Press.

[79] Salem F. Adra and Peter J. Fleming. Diversity Management in Evolutionary Many-Objective Optimization. IEEE Transactions on Evolutionary Computation, 15(2):183–195, April 2011.

[80] Salem F. Adra, Ian Griffin, and Peter J. Fleming. Hybrid Multiobjective Genetic Algorithm with a New Adaptive Local Search Process. In Hans-Georg Beyer et al., editor, 2005 Genetic and Evolutionary Computation Conference (GECCO’2005), volume 1, pages 1009–1010, New York, USA, June 2005. ACM Press.

[81] Salem F. Adra, Ian Griffin, and Peter J. Fleming. A Comparative Study of Progressive Preference Articulation Techniques for Multiobjective Optimisation. In Shigeru Obayashi, Kalyanmoy Deb, Carlo Poloni, Tomoyuki Hiroyasu, and Tadahiko Murata, editors, Evolutionary Multi-Criterion Optimization, 4th International Conference, EMO 2007, pages 908–921, Matshushima, Japan, March 2007. Springer. Lecture Notes in Computer Science Vol. 4403.

[82] Salem F. Adra, Ian Griffin, and Peter J. Fleming. An Informed Convergence Accelerator for Evolutionary Multiobjective Optimiser. In Dirk Thierens, editor, 2007 Genetic and Evolutionary Computation Conference (GECCO’2007), volume 1, pages 734–740, London, UK, July 2007. ACM Press.

[83] Salem F. Adra, Ian Griffin, and Peter J. Fleming. A Convergence Acceleration Technique for Multiobjective Optimisation. In Chi-Keong Goh, Yew-Soon Ong, and Kay Chen Tan, editors, Multi-Objective Memetic Algorithms, chapter 9, pages 183–205. Springer, Studies in Computational Intelligence, Vol. 171, Berlin, Germany, 2009. ISBN 978-3-540-88050-9.

[84] Salem F. Adra, Ahmed I. Hamody, Ian Griffin, and Peter J. Fleming. A Hybrid Multi-Objective Evolutionary Algorithm Using an Inverse Neural Network for Aircraft Control System Design. In 2005 IEEE Congress on Evolutionary Computation (CEC’2005), volume 1, pages 1–8, Edinburgh, Scotland, September 2005. IEEE Service Center.

[85] Salem F. Adra, Mariam Kiran, Phil McMinn, and Neil Walkinshaw. A Multiobjective Optimisation Approach For The Dynamic Inference and Refinement of Agent-Based Model Specifications. In 2011 IEEE Congress on Evolutionary Computation (CEC’2011), pages 2237–2244, New Orleans, Louisiana, USA, 5–8 June 2011. IEEE Service Center.

[86] Salem Fawaz Adra. Improving Convergence, Diversity and Pertinency in Multiobjective Optimisation. PhD thesis, Department of Automatic Control and Systems Engineering, The University of Sheffield, UK, October 2007.

[87] S.F. Adra, I.A. Griffin, and P.J. Fleming. An Adaptive Memetic Algorithm for Enhanced Diversity. In I.C. Parmee, editor, Adaptive Computing in Design and Manufacture 2006. Proceedings of the Seventh International Conference, pages 251–254, Bristol, UK, April 2006. The Institute for Peoplecentred Computation.

[88] Jeroen C.J.H. Aerts, Marjan van Herwijnen, and Theodor J. Stewart. Using Simulated Annealing and Spatial Goal Programming for Solving a Multi Site Land Use Allocation Problem. In Carlos M. Fonseca, Peter J. Fleming, Eckart Zitzler, Kalyanmoy Deb, and Lothar Thiele, editors, Evolutionary Multi-Criterion Optimization. Second International Conference, EMO 2003, pages 448–463, Faro, Portugal, April 2003. Springer. Lecture Notes in Computer Science. Volume 2632.

[89] Frederico G. Guimar aes, Reinaldo M. Palhares, Felipe Campelo, and Hajime Igarashi. Design of mixed h-2/h infinity control systems using algorithms inspired by the immune system. Information Sciences, 177(20):4368–4386, October 2007.

[90] Zouhaler Affi, Badreddine El-Kribi, and Lotfi Romdhane. Advanced mechatronic design using a multi-objective genetic algorithm optimization of a motor-driven four-bar system. Mechatronics, 17(9):489–500, November 2007.

[91] F. Afsari, M. Eftekhari, E. Eslami, and P.-Y. Woo. Interpretability-based fuzzy decision tree classifier a hybrid of the subtractive clustering and the multi-objective evolutionary algorithm. Soft Computing, 17(9):1673–1686, September 2013.

[92] A. Afshar, F. Sharifi, and M.R. Jalali. Non-dominated archiving multi-colony ant algorithm for multi-objective optimization: Application to multi-purpose reservoir operation. Engineering Optimization, 41(4):313–325, April 2009.

[93] A. Afshar, A. Kasaeian Ziaraty, A. Kaveh, and F. Sharifi. Nondominated Archiving Multicolony Ant Algorithm in Time-Cost Trade-Off Optimization. Journal of Construction Engineering and Management, 135(7):668–674, July 2009.

[94] Abbas Afshar and Habib Fathi. Fuzzy multi-objective optimization of financebased scheduling for construction projects with uncertainties in cost. Engineering Optimization, 41(11):1063–1080, November 2009.

[95] Abbas Afshar and Hamideh Kazemi. Multi objective calibration of large scaled water quality model using a hybrid particle swarm optimization and neural network algorithm. KSCE Journal of Civil Engineering, 16(6):913– 918, September 2012.

[96] Abbas Afshar, Hamideh Kazemi, and Motahareh Saadatpour. Particle Swarm Optimization for Automatic Calibration of Large Scale Water Quality Model (CE-QUAL-W2): Application to Karkheh Reservoir, Iran. Water Resources Management, 25(10):2613–2632, August 2011.

[97] Arshad Afzal and Kwang-Yong Kim. Multi-Objective Optimization of a Passive Micromixer Based on Periodic Variation of Velocity Profile. Chemical Engineering Communications, 202(3):322–331, March 4 2015.

[98] Ehsan Afzalan and Mahmood Joorabian. Emission, reserve and economic load dispatch problem with non-smooth and non-convex cost functions using epsilon-multi-objective genetic algorithm variable. International Journal of Electrical Power & Energy Systems, 52:55–67, November 2013.

[99] Alexandros Agapitos and Simon M. Lucas. Evolving a Statistics Class Using Object Oriented Evolutionary Programming. In Marc Ebner, Michael O’Neill, Anikó Ekárt, Leonardo Vanneschi, and Anna Isabel Esparcia-Alcázar, editors, Genetic Programming, 10th European Conference, EuroGP 2007, pages 291–300, Valencia, Spain, April 2007. Springer. Lecture Notes in Computer Science Vol. 4445.

[100] Alexandros Agapitos, Julian Togelius, and Simon M. Lucas. Multiobjective techniques for the Use of State in Genetic Programming applied to Simulated Car Racing. In 2007 IEEE Congress on Evolutionary Computation (CEC’2007), pages 1562–1569, Singapore, September 2007. IEEE Press.

[101] A. Agarwal, U. Tewary, F. Pettersson, S. Das, H. Saxen, and N. Chakraborti. Analysing blast furnace data using evolutionary neural network and multiobjective genetic algorithms. Ironmaking & Steelmaking, 37(5):353–359, July 2010.

[102] Aaditya Agarwal and Santosh K. Gupta. Jumping gene adaptations of NSGA-II and their use in the multi-objective optimal design of shell and tube heat exchangers. Chemical Engineering Research & Design, 86(A2):123–139, February 2008.

[103] Aaditya Agarwal and Santosh K. Gupta. Multiobjective optimal design of heat exchanger networks using new adaptations of the elitist nondominated sorting genetic algorithm, NSGA-II. Industrial & Engineering Chemistry Research, 47(10):3489–3501, May 21 2008.

[104] Akash Agarwal, Frank Pettersson, Arunima Singh, Chang Sun Kong, Henrik Saxén, Krishna Rajan, Shuichi Iwata, and Nirupam Chakraborti. Identification and Optimization of AB_{2} Phases Using Principal Component Analysis, Evolutionary Neural Nets, and Multiobjective Genetic Algorithms. Materials and Manufacturing Processes, 24(3):274–281, March 2009.

[105] Manoj Agarwal, Nitin Agrawal, Shikhar Sharma, Lovekesh Vig, and Naveen Kumar. Parallel multi-objective multi-robot coalition formation. Expert Systems With Applications, 42(21):7797–7811, November 30 2015.

[106] Manoj Agarwal, Naveen Kumar, and Lovekesh Vig. Non-additive multiobjective robot coalition formation. Expert Systems with Applications, 41(8):3736–3747, June 15 2014.

[107] Manoj Agarwal, Lovekesh Vig, and Naveen Kumar. Multi-objective Robot Coalition Formation for Non-additive Environments. In Sabina Jeschke, Honghai Liu, and Daniel Schilberg, editors, Intelligent Robotics and Applications, 4th International Conference, ICIRA 2011, pages 346–355. Springer. Lecture Notes in Artificial Intelligence Vol. 7101, Aachen, Germany, December 6-8 2011.

[108] P. Agarwal and A.M. Raich. Design and optimization of steel trusses using genetic algorithms, parallel computing, and human-computer interaction. Structural Engineering and Mechanics, 23(4):325–337, July 2006.

[109] Pranab Agarwal. Conceptual design of long-span trusses using multi-stage heuristics. Master’s thesis, Texas A&M University, College Station, Texas, USA, 2005.

[110] Elson Agastra, Leonardo Lucci, Renzo Nesti, Giuseppe Pelosi, and Stefano Selleri. Modified NSGA-II Algorithm for Multiobjective Optimization of Compact High-Efficiency Square Horns. International Journal of RF and Microwave Computer-Aided Engineering, 21(2):174–181, March 2011.

[111] Elson Agastra, Giuseppe Pelosi, Stefano Selleri, and Ruggero Taddei. Taguchi’s method for multi-objective optimization problems. International Journal of RF and Microwave Computer-Aided Engineering, 23(3):357–366, May 2013.

[112] Varun Aggarwal and Una-May O’Reilly. COSMO: A Correlation Sensitive Mutation Operator for Multi-Objective Optimization. In Dirk Thierens, editor, 2007 Genetic and Evolutionary Computation Conference (GECCO’2007), volume 1, pages 741–748, London, UK, July 2007. ACM Press.

[113] Eleni Aggelogiannaki and Haralarnbos Sarimveis. Simulated annealing algorithm for prioritized multiobjective optimization-implementation in an adaptive model predictive control configuration. IEEE Transactions on Systems, Man, and Cybernetics Part B–Cybernetics, 37(4):902–915, August 2007.

[114] Hadi Aghassi, Sedigheh Nader Abadi, and Emad Roghanian. A Multiobjective Genetic Algorithm for Optimization Time-Cost Trade-off Scheduling. In Dickson Lukose, Abdul Rahim Ahmad, and Azizah Suliman, editors, Knowledge Technology, Third Knowledge Technology Week, KTW 2011, pages 356–359. Springer. Communications in Computer and Information Science Vol. 295, Kajang, Malaysia, July 18-22 2011.

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[1578] Chih-Ming Chen, Ying ping Chen, Tzu-Ching Shen, and John K. Zao. Optimizing Degree Distributions in LT Codes by Using the Multiobjective Evolutionary Algorithm Based on Decomposition. In 2010 IEEE Congress on Evolutionary Computation (CEC’2010), pages 3635–3642, Barcelona, Spain, July 18-23 2010. IEEE Press.

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[1580] Chiu-Hung Chen and Jyh-Horng Chou. Multiobjective Optimization of Airline Crew Roster Recovery Problems Under Disruption Conditions. IEEE Transactions on Systems Man Cybernetics-Systems, 47(1):133–144, January 2017.

[1581] Chiu-Hung Chen, Ta-Yuan Chou, Tung-Kuan Liu, Jyh-Horng Chou, and Chung-Nan Lee. Optimization of Short-Haul Airline Crew Pairing Problems Using a Multiobjective Genetic Algorithm. International Journal of Innovative Computing Information and Control, 6(9):3943–3959, September 2010.

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[1588] Chyi-Tsong Chen and Hung-I. Chen. Multi-objective Optimization Design of Plate-fin Heat Sinks Using a Direction-based Genetic Algorithm. Journal of the Taiwan Institute of Chemical Engineers, 44(2):257–265, March 2013.

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[1590] Enhong Chen and Feng Wang. Dynamic Clustering Using Multi-objective Evolutionary Algorithm. In Yue Hao et al., editor, Computational Intelligence and Security. International Conference, CIS 2005, pages 73–80, Xi’an, China, December 2005. Springer, Lecture Notes in Artificial Intelligence Vol. 3801.

[1591] Fei Chen, Kosuke Sekiyama, Ferdinando Cannella, and Toshio Fukuda. Optimal Subtask Allocation for Human and Robot Collaboration Within Hybrid Assembly System. IEEE Transactions on Automation Science and Engineering, 11(4):1065–1075, October 2014.

[1592] Gary Yu-Hsin Chen and Ju-Chieh Lo. Dynamic Facility Layout with Multi-Objectives. Asia-Pacific Journal of Operational Research, 31(4), August 2014. Article Number: 1450027.

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[1594] Hanning Chen, Ma Lian Bo, and Yunlong Zhu. Multi-hive Bee Foraging Algorithm for Multi-objective Optimal Power Flow Considering the Cost, Loss, and Emission. International Journal of Electrical Power & Energy Systems, 60:203–220, September 2014.

[1595] Hanning Chen, Yunlong Zhu, Lianbo Ma, and Ben Niu. Multiobjective RFID Network Optimization Using Multiobjective Evolutionary and Swarm Intelligence Approaches. Mathematical Problems in Engineering, 2014. Article Number: 961412.

[1596] Hao Chen, John A. Clark, Siraj A. Shaikh, Howard Chivers, and Philip Noble. Optimising IDS Sensor Placement. In Fifth International Conference on Availability, Reliability and Security (ARES’2010), pages 315–320, Krakow, Poland, 15-18 February 2010. IEEE Computer Society Press.

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[1598] Ho-Wen Chen, Shu-Kuang Ning, Ruey-Fang Yu, and Ming-Sung Hung. Optimizing the Monitoring Strategy of Wastewater Treatment Plants by Multiobjective Neural Networks Approach. Environmental Monitoring and Assessment, 125(1-3):325–332, February 2007.

[1599] Hou-Zun Chen, Yue-Yu Zhang, Xingao Gong, and Hongjun Xiang. Predicting New TiO2 Phases With Low Band Gaps by a Multiobjective Global Optimization Approach. Journal of Physical Chemistry C, 118(5):2333–2337, February 6 2014.

[1600] Hsin-Kai Chen, Cheng-Yuan Lin, and Jian-Hung Chen. A Multi-objective Evolutionary Approach for Cloud Service Provider Selection Problems with Dynamic Demands. In Anna I. Esparcia-Alcázar et al., editor, Applications of Evolutionary Computation, 17th European Conference, EvoApplications 2014, pages 841–852. Springer. Lecture Notes in Computer Science Vol. 8602, Granada, Spain, April 23-25 2014.

[1601] Huanhuan Chen. Diversity and Regularization in Neural Network Ensembles. PhD thesis, School of Computer Science, University of Birmingham, UK, October 2008.

[1602] Huanhuan Chen and Xin Yao. Evolutionary Multiobjective Ensemble Learning Based on Bayesian Feature Selection. In 2006 IEEE Congress on Evolutionary Computation (CEC’2006), pages 267–274, Vancouver, BC, Canada, July 2006. IEEE.

[1603] Huanhuan Chen and Xin Yao. Multiobjective Neural Network Ensembles Based on Regularized Negative Correlation Learning. IEEE Transactions on Knowledge and Data Engineering, 22(12):1738–1751, December 2010.

[1604] H.W. Chen and N.B. Chang. Decision Support for Allocation of Watershed Pollution Load Using Grey Fuzzy Multiobjective Programming. Journal of the American Water Resources Association, 42(3):725–745, June 2006.

[1605] J.H. Chen, H.M. Chen, and S.Y. Ho. Design of Nearest Neighbor Classifiers: Multi-objective Approach. International Journal of Approximate Reasoning, 40(1-2):3–22, July 2005.

[1606] Jiajia Chen, Yongsheng Ding, Yaochu Jin, and Kuangrong Hao. A Synergetic Immune Clonal Selection Algorithm Based Multi-Objective Optimization Method for Carbon Fiber Drawing Process. Fibers and Polymers, 14(10):1722–1730, October 2013.

[1607] Jian Chen, Jie Jia, Yingyou Wen, Dazhe Zhao, and Jiren Liu. A Genetic Approach to Channel Assignment for Multi-radio Multi-channel Wireless Mesh Networks. In 2009 ACM SIGEVO Summit on Genetic and Evolutionary Computation (GEC’2009), pages 39–46, Shanghai, China, June 12-14 2009. ACM Press. ISBN 978-1-60558-326-6.

[1608] Jian Chen, Jie Jia, Yingyou Wen, Dazhe Zhao, and Jiren Liu. Modeling and Extending Lifetime of Wireless Sensor Networks Using Genetic Algorithm. In 2009 ACM SIGEVO Summit on Genetic and Evolutionary Computation (GEC’2009), pages 47–54, Shanghai, China, June 12-14 2009. ACM Press. ISBN 978-1-60558-326-6.

[1609] Jian-Hung Chen. Theory and Applications of Efficient Multi-Objective Evolutionary Algorithms. PhD thesis, College of Information and Electrical Engineering of the Feng Chia University, Taichung, Taiwan, R.O.C., 2004.

[1610] Jian-Hung Chen. Simultaneous Optimization of Production Planning and Inspection Planning for Flexible Manufacturing Systems. In Dirk Thierens, editor, 2007 Genetic and Evolutionary Computation Conference (GECCO’2007), volume 2, pages 1928–1935, London, UK, July 2007. ACM Press.

[1611] Jian-Hung Chen, David E. Goldberg, Shinn-Ying Ho, and Kumara Sastry. Fitness Inheritance in Multi-Objective Optimization. In W.B. Langdon, E. Cantú-Paz, K. Mathias, R. Roy, D. Davis, R. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M.A. Potter, A.C. Schultz, J.F. Miller, E. Burke, and N. Jonoska, editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2002), pages 319–326, San Francisco, California, July 2002. Morgan Kaufmann Publishers.

[1612] Jian-Hung Chen and Shinn-Ying Ho. Multi-Objective Evolutionary Optimization of Flexible Manufacturing Systems. In Lee Spector, Erik D. Goodman, Annie Wu, W.B. Langdon, Hans-Michael Voigt, Mitsuo Gen, Sandip Sen, Marco Dorigo, Shahram Pezeshk, Max H. Garzon, and Edmund Burke, editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2001), pages 1260–1267, San Francisco, California, 2001. Morgan Kaufmann Publishers.

[1613] Jian-Hung Chen, Shinn-Ying Ho, and David E. Goldberg. Quality-Time Analysis of Multi-Objective Evolutionary Algorithms. In Hans-Georg Beyer et al., editor, 2005 Genetic and Evolutionary Computation Conference (GECCO’2005), volume 2, pages 1455–1462, New York, USA, June 2005. ACM Press.

[1614] Jianyong Chen, Qiuzhen Lin, and Qingbin Hu. A Novel Clonal Algorithm for Multiobjective Optimization. In 2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing, pages 613–616, Shanghai, China, December 21-22 2008. IEEE Computer Society.

[1615] Jianyong Chen, Qiuzhen Lin, and Qingbin Hu. Application of Novel Clonal Algorithm in Multiobjective Optimization. International Journal of Information Technology & Decision Making, 9(2):239–266, March 2010.

[1616] Jianyong Chen, Qiuzhen Lin, and Zhen Ji. A Hybrid Immune Multiobjective Optimization Algorithm. European Journal of Operational Research, 204(2):294–302, July 16 2010.

[1617] Jianyong Chen, Qiuzhen Lin, and Zhen Ji. Chaos-based Multi-objective Immune Algorithm with a Fine-grained Selection Mechanism. Soft Computing, 15(7):1273–1288, July 2011.

[1618] Jie Chen, Bin Xin, Zhihong Peng, Lihua Dou, and Juan Zhang. Optimal Contraction Theorem for Exploration-Exploitation Tradeoff in Search and Optimization. IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans, 39(3):680–691, May 2009.

[1619] Jing Chen, Yan Lin, Jun Zhou Huo, Ming Xia Zhang, and Zhuo Shang Ji. Optimal Ballast Water Exchange Sequence Design Using Symmetrical Multitank Strategy. Journal of Marine Science and Technology, 15(3):280–293, September 2010.

[1620] Jing Chen, Yan Lin, Jun Zhou Huo, Ming Xia Zhang, and Zhuo Shang Ji. Optimization of Ship’s Subdivision Arrangement for Offshore Sequential Ballast Water Exchange Using a Non-dominated Sorting Genetic Algorithm. Ocean Engineering, 37(11-12):978–988, August 2010.

[1621] Jing Chen, Yan Lin, Junzhou Huo, Mingxia Zhang, and Zhuoshang Ji. Optimization of Ships’ Diagonal Ballast Water Exchange Sequence Using a Multiobjective Genetic Algorithm. Journal of Ship Research, 54(4):257–267, December 2010.

[1622] Jinzhu Chen, Guolong Chen, and Wenzhong Guo. A Discrete PSO for Multiobjective Optimization in VLSI Floorplanning. In Zhihua Cai, Zhenhua Li, Zhuo Khang, and Yong Liu, editors, Advances in Computation and Intelligence, 4th International Symposium, ISCA 2009, pages 400–410. Springer, Lecture Notes in Computer Science Vol. 5821, Huangshi, China, October 2009.

[1623] Juan Chen, Lihong Xu, and Changliang Yuan. IPGA Based Multi-Objective Compatible Control Algorithm and its Application in Oversaturated Adjacent Intersection Control. In 2007 IEEE Congress on Evolutionary Computation (CEC’2007), pages 3187–3194, Singapore, September 2007. IEEE Press.

[1624] Jun Chen and Mahdi Mahfouf. A Population Adaptive Based Immune Algorithm for Solving Multi-objective Optimization Problems. In Hughes Bersini and Jorge Carneiro, editors, Artificial Immune Systems, 5th International Conference, ICARIS 2006, Proceedings, pages 280–293, Oeiras, Portugal, September 2006. Springer-Verlag, Lecture Notes in Computer Science Vol. 4163.

[1625] Lei Chen and Hai-Lin Liu. A Region Decomposition-Based Multi-Objective Particle Swarm Optimization Algorithm. International Journal of Pattern Recognition and Artificial Intelligence, 28(8), December 2014. Article Number: 1459009.

[1626] Lei Chen, Jiali Qiu, Guoyuan Wei, and Zhenyao Shen. A Preference-based Multi-objective Model for the Optimization of Best Management Practices. Journal of Hydrology, 520:356–366, January 2015.

[1627] Li Chen, James McPhee, and William W.G. Yeh. A Diversified Multiobjective GA for Optimizing Reservoir Rule Curves. Advances in Water Resources, 30(5):1082–1093, May 2007.

[1628] Liang-Hsuan Chen and Cheng-Hsiung Chiang. Multi-Objective Optimization in Reliability System Using Genetic Algorithm and Neural Network. Asia-Pacific Journal of Operational Research, 25(5):649–672, October 2008.

[1629] M. W. Chen and A. M. S. Zalzala. A Genetic Approach to Motion Planning of Redundant Mobile Manipulator Systems Considering Safety and Configuration. Journal of Robotic Systems, 14(7):529–544, July 1997.

[1630] Min-Rong Chen, Yong-Zai Lu, and Genke Yang. Multiobjective Optimization Using Population-based Extremal Optimization. Neural Computing and Applications, 17(2):101–109, March 2008.

[1631] Min-Rong Chen and Yong-Zal Lu. A Novel Elitist Multiobjective Optimization Algorithm: Multiobjective Extremal Optimization. European Journal of Operational Research, 188(3):637–651, August 1 2008.

[1632] Min-Rong Chen, Yong zai Lu, and Gen ke Yang. Multiobjective Extremal Optimization with Applications to Engineering Design. Journal of Zhejiang University SCIENCE A, 8(12):1905–1911, November 2007.

[1633] Qian Chen and Sheng-Uei Guan. Incremental Multiple Objective Genetic Algorithms. IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, 34(3):1325–1334, June 2004.

[1634] Qin Chen, Cheng Fu, and Qingfu Zhang. On Performance of Decomposition-Based MOEAs in Noisy Environment. In 2015 IEEE Congress on Evolutionary Computation (CEC’2015), pages 3412–3417, Sendai, Japan, 25-28 May 2015. IEEE Press. ISBN 978-1-4799-7492-4.

[1635] Qiong Chen, Shengwu Xiong, and Hongbing Liu. Evolutionary Multiobjective Optimization Algorithm Based on Global Crowding Diversity Maintenance Strategy. In 2009 ACM SIGEVO Summit on Genetic and Evolutionary Computation (GEC’2009), pages 803–806, Shanghai, China, June 12-14 2009. ACM Press. ISBN 978-1-60558-326-6.

[1636] S. L. Chen, R. J. Jiao, and M. M. Tseng. Evolutionary Product Line Design Balancing Customer Needs and Product Commonality. CIRP Annals-Manufacturing Technology, 58(1):123–126, 2009.

[1637] Shahar Chen, David Amid, Ofer M. Shir, Lior Limonad, David Boaz, Ateret Anaby-Tavor, and Tobias Schreck. Self-Organizing Maps for Multi-Objective Pareto Frontiers. In S. Carpendale, W. Chen, and S. Hong, editors, 2013 IEEE Symposium on Pacific Visualization (PACIFICVIS), pages 153–160, Sydney, Australia, February 27-March 01 2013. IEEE. ISBN 978-1-4673-4797-6.

[1638] Shao-Wen Chen and Tsung-Che Chiang. Evolutionary Many-objective Optimization by MO-NSGA-II with Enhanced Mating Selection. In 2014 IEEE Congress on Evolutionary Computation (CEC’2014), pages 1397–1404, Beijing, China, 6-11 July 2014. IEEE Press. ISBN 978-1-4799-1488-3.

[1639] Shih-Pin Chen and Ming-Jiun Tsai. Time-cost Trade-off Analysis of Project Networks in Fuzzy Environments. European Journal of Operational Research, 212(2):386–397, July 16 2011.

[1640] Stephen Chen. Locust Swarms – A New Multi-Optima Search Technique. In 2009 IEEE Congress on Evolutionary Computation (CEC’2009), pages 1745–1752, Trondheim, Norway, May 2009. IEEE Press.

[1641] Stephen Chen and James Montgomery. A Simple Strategy to Maintain Diversity and Reduce Crowding in Particle Swarm Optimization. In Dianhui Wang and Mark Reynolds, editors, AI 2011: Advances in Artificial Intelligence, 24th Australasian Joint Conference, pages 281–290, Perth, Australia, December 5-8 2011. Springer. Lecture Notes in Artificial Intelligence Vol. 7106.

[1642] Stephen Chen and James Montgomery. Selection Strategies for Initial Positions and Initial Velocities in Multi-Optima Particle Swarms. In 2011 Genetic and Evolutionary Computation Conference (GECCO’2011), pages 53– 60, Dublin, Ireland, July 12-16 2011. ACM Press.

[1643] T.Y. Chen and H.C. Chen. Mixed-discrete Structural Optimization Using a Rank-niche Evolution Strategy. Engineering Optimization, 41(1):39–58, January 2009.

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[1645] W.-J. Chen, C.-C. Hsu, and Y.-L. Yang. Improving Roughness Quality of end Milling Al 7075-T6 Alloy with Taguchi Based Multiobjective Quantum Behaved Particle Swarm Optimisation Algorithm. Materials Research Innovations, 18:647–653, May 2014.

[1646] Wang Chen, Yan jun Shi, and Hong fei Teng. A Generalized Differential Evolution Combined with EDA for Multi-objective Optimization Problems. In De-Shuang Huang, Donald C. Wunsch II, Daniel S. Levine, and Kang-Hyun Jo, editors, Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, 4th International Conference on Intelligent Computing (ICIC’2008), pages 140–147. Springer, Lecture Notes in Computer Science, Vol. 5227, Shanghai, China, September 15-18 2008. ISBN 978-3-540-85983-3.

[1647] Wei-Mei Chen, Hsien-Kuei Hwang, and Tsung-Hsi Tsai. Maxima-finding Algorithms for Multidimensional Samples: A Two-phase Approach. Computational Geometry-Theory and Applications, 45(1-2):33–53, January-February 2012.

[1648] Wei-Neng Chen and Jun Zhang. A Preference-Based Bi-Objective Approach to the Payment Scheduling Negotiation Problem with the Extended r-Dominance and NSGA-II. In 2012 Genetic and Evolutionary Computation Conference (GECCO’2012), pages 1063–1070, Philadelphia, USA, July 2012. ACM Press. ISBN: 978-1-4503-1177-9.

[1649] Wenliang Chen, Chonghua Yao, and Xiwu Lu. Optimal Design Activated Sludge Process by Means of Multi-Objective Optimization: Case Study in Benchmark Simulation Model 1 (BSM1). Water Science and Technology, 69(10):2052–2058, 2014.

[1650] Xianming Chen. Pareto Tree Searching Genetic Algorithm: Approaching Pareto Optimal Front by Searching Pareto Optimal Tree. Technical Report NK-CS-2001-002, Department of Computer Science, Nankai University, Tianjin, China, 2001.

[1651] Xiaomin Chen and Ramesh K. Agarwal. Optimization of Wind Turbine Blade Airfoils Using a Multi-Objective Genetic Algorithm. Journal of Aircraft, 50(2):519–527, March-April 2013.

[1652] Xu Chen, Wenli Du, and Feng Qian. Multi-Objective Differential Evolution with Ranking-Based Mutation Operator and its Application in Chemical Process Optimization. Chemometrics and Intelligent Laboratory Systems, 136:85–96, August 15 2014.

[1653] Y. L. Chen and Y. L. Ke. Multi-Objective VAr Planning for Large-Scale Power Systems Using Projection-Based Two-Layer Simulated Annealing Algorithms. IEE Proceedings-Generation Transmission and Distribution, 151(4):555–560, July 2004.

[1654] Yan Chen, Masakuni Narita, Masashi Tsuji, and Sangduk Sa. A Genetic Algorithm Approach to Optimization for the Radiological Worker Allocation Problem. Health Physics, 70(2):180–186, February 1996.

[1655] Yee Ming Chen and Wen-Shiang Wang. Environmentally Constrained Economic Dispatch Using Pareto Archive Particle Swarm Optimisation. International Journal of Systems Science, 41(5):593–605, 2010.

[1656] Yen-Liang Chen and Xiang-Han Chen. An Evolutionary PageRank Approach for Journal Ranking with Expert Judgements. Journal of Information Science, 37(3):254–272, June 2011.

[1657] Yi Chen and Matthew P. Cartmell. Multi-Objective Optimisation on Motorised Momentum Exchange Tether for Payload Orbital Transfer. In 2007 IEEE Congress on Evolutionary Computation (CEC’2007), pages 987–993, Singapore, September 2007. IEEE Press.

[1658] Yi Chen, Yong Ma, Zheng Lu, Lixia Qiu, and Jin He. Terahertz Spectroscopic Uncertainty Analysis for Explosive Mixture Components Determination Using Multi-objective Micro-genetic Algorithm. Advances in Engineering Software, 42(9):649–659, September 2011.

[1659] Yidong Chen, Xiaodong Shi, Changle Zhou, and Qingyang Hong. A Word Alignment Model Based on Multiobjective Evolutionary Algorithms. Computers & Mathematics with Applications, 57(11-12):1724–1729, June 2009.

[1660] Yikai Chen, Shiwen Yang, and Zaiping Nie. Improving Conflicting Specifications of Time-modulated Antenna Arrays by Using A Multiobjective Evolutionary Algorithm. International Journal of Numerical Modelling-Electronic Networks Devices and Fields, 25(3):205–215, May-June 2012.

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[1662] Yu Chen, Xiufen Zou, and Weicheng Xie. Convergence of Multi-objective Evolutionary Algorithms to a Uniformly Distributed Representation of the Pareto Front. Information Sciences, 181(16):3336–3355, August 15 2011.

[1663] Yun Chen and Hanhong Zhu. PSO Heuristics Algorithm for Portfolio Optimization. In Ying Tan, Yuhui Shi, and Kay Chen Tan, editors, Advances in Swarm Intelligence, First International Conference, ICSI 2010, pages 183– 190. Springer. Lecture Notes in Computer Science Vol. 6145, Beijing, China, June 12-15 2010.

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[1665] Yuning Chen and Jin-Kao Hao. The Bi-Objective Quadratic Multiple Knapsack Problem: Model and Heuristics. Knowledge-Based Systems, 97:89–100, April 1 2016.

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[1668] C. T. Cheng, X. Y. Wu, and K. W. Chau. Multiple Criteria Rainfall-runoff Model Calibration Using a Parallel Genetic Algorithm in a Cluster of Computers. Hydrological Sciences Journal-Journal Des Sciences Hydrologiques, 50(6):1069–1087, December 2005.

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[1671] Fangqi Cheng and Feifan Ye. A Two Objective Optimisation Model for Order Splitting Among Parallel Suppliers. International Journal of Production Research, 49(10):2759–2769, 2011.

[1672] Fangqi Cheng, Feifan Ye, and Jianguo Yang. Multi-objective Optimization of Collaborative Manufacturing Chain with Time-sequence Constraints. International Journal of Advanced Manufacturing Technology, 40(9-10):1024– 1032, February 2009.

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[1675] Hsueh-Chien Cheng, Tsung-Che Chiang, and Li-Chen Fu. Multiobjective Permutation Flowshop Scheduling by an Adaptive Genetic Local Search Algorithm. In 2008 Congress on Evolutionary Computation (CEC’2008), pages 1596–1602, Hong Kong, June 2008. IEEE Service Center.

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[1678] Hui Cheng, Jiannong Cao, Xingwei Wang, Sajal K. Das, and Shengxiang Yang. Stability-aware Multi-metric Clustering in Mobile Ad Hoc Networks with Group Mobility. Wireless Communications & Mobile Computing, 9(6):759–771, June 2009.

[1679] Jian-Hung Cheng, Hung-Ming Chen, and Shinn-Ying Ho. Design of Nearest Neighbor Classifiers Using an Intelligent Multi-objective Evolutionary Algorithm. In Chengqi Zhang, Hans W. Guesgen, and Wai K. Yeap, editors, PRI-CAI 2004: Trends in Artificial Intelligence. 8th Pacific Rim International Conference on Artificial Intelligence, pages 262–271, Auckland, New Zealand, August 2004. Springer-Verlag. Lecture Notes in Artificial Intelligence Vol. 3157.

[1680] Jin Cheng, Zhenyu Liu, and Jianrong Tan. Multiobjective Optimization of Injection Molding Parameters Based on Soft Computing and Variable Complexity Method. International Journal of Advanced Manufacturing Technology, 66(5-8):907–916, May 2013.

[1681] Jixang Cheng, Gexiang Zhang, Zhidan Li, and Yuquan Li. Multi-objective Ant Colony Optimization Based on Decomposition for Bi-objective Traveling Salesman Problems. Soft Computing, 16(4):597–614, April 2012.

[1682] Jixiang Cheng, Gary G. Yen, and Gexiang Zhang. A Many-Objective Evolutionary Algorithm With Enhanced Mating and Environmental Selections. IEEE Transactions on Evolutionary Computation, 19(4):592–605, August 2015.

[1683] Jixiang Cheng, Gexiang Zhang, Fabio Caraffini, and Ferrante Neri. Multicriteria Adaptive Differential Evolution for Global Numerical Optimization. Integrated Computer-Aided Engineering, 22(2):103–117, 2015.

[1684] Li-Hua Cheng, Ping-Chung Wu, and Junghui Chen. Numerical Simulation and Optimal Design of AGMD-Based Hollow Fiber Modules for Desalination. Industrial & Engineering Chemistry Research, 48(10):4948–4959, May 20 2009.

[1685] Min-Yuan Cheng and Duc-Hoc Tran. Two-Phase Differential Evolution for the Multiobjective Optimization of Time-Cost Tradeoffs in Resource-Constrained Construction Projects. IEEE Transactions on Engineering Management, 61(3):450–461, August 2014.

[1686] Peng Cheng. A Tunable Constrained Test Problems Generator for Multiobjective Optimization. In Proceedings of the 2008 Second International Conference on Genetic and Evolutionary Computing (WGEC’2008), pages 96–100, Washington, DC, USA, September 2008. IEEE Computer Society. ISBN 978-0-7695-3334-6.

[1687] Peng Cheng, Jeng-Shyang Pan, and Chun-Wei Lin. Use EMO to Protect Sensitive Knowledge in Association Rule Mining by Removing Items. In 2014 IEEE Congress on Evolutionary Computation (CEC’2014), pages 1108–1115, Beijing, China, 6-11 July 2014. IEEE Press. ISBN 978-1-4799-1488-3.

[1688] Ran Cheng, Yaochu Jin, and Kaname Narukawa. Adaptive Reference Vector Generation for Inverse Model Based Evolutionary Multiobjective Optimization with Degenerate and Disconnected Pareto Fronts. In António Gaspar-Cunha, Carlos Henggeler Antunes, and Carlos Coello Coello, editors, Evolutionary Multi-Criterion Optimization, 8th International Conference, EMO 2015, pages 127–140. Springer. Lecture Notes in Computer Science Vol. 9018, Guimarães, Portugal, March 29 - April 1 2015.

[1689] Ran Cheng, Yaochu Jin, Kaname Narukawa, and Bernhard Sendhoff. A Multiobjective Evolutionary Algorithm Using Gaussian Process-Based Inverse Modeling. IEEE Transactions on Evolutionary Computation, 19(6):838–856, December 2015.

[1690] Ran Cheng, Yaochu Jin, Markus Olhofer, and Bernhard Sendhoff. A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization. IEEE Transactions on Evolutionary Computation, 20(5):773–791, October 2016.

[1691] Ran Cheng, Markus Olhofer, and Yaochu Jin. Reference Vector Based a posteriori Preference Articulation for Evolutionary Multiobjective Optimization. In 2015 IEEE Congress on Evolutionary Computation (CEC’2015), pages 939–946, Sendai, Japan, 25-28 May 2015. IEEE Press. ISBN 978-1-4799-7492-4.

[1692] Runwei Cheng, Mitsuo Gen, and Shmuel S. Oren. An Adaptive Hyperplane Approach for Multiple Objective Optimization Problems with Complex Constraints. In Darrell Whitley, David Goldberg, Erick Cantú-Paz, Lee Spector, Ian Parmee, and Hans-Georg Beyer, editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2000), pages 299–306, Las Vegas, Nevada, July 10-12 2000. Morgan Kaufmann. ISBN 1-55860-708-0.

[1693] Shan Cheng and Min-You Chen. Multi-objective Reactive Power Optimization Strategy for Distribution System with Penetration of Distributed Generation. International Journal of Electrical Power & Energy Systems, 62:221– 228, November 2014.

[1694] Shan Cheng, Min-You Chen, and Gang Hu. Convergence Enhanced Multiobjective Particle Swarm Optimization with Introduction of Quorum-Sensing Inspired Turbulence. In Ying Tan, Yuhui Shi, and Carlos A. Coello Coello, editors, Advances in Swarm Intelligence, 5th International Conference, ICSI 2014, pages 394–403. Springer. Lecture Notes in Computer Science Vol. 8794, Hefei, China, October 17-20 2014.

[1695] Shan Cheng, Min you Chen, Rong jong Wai, and Fang zong Wang. Optimal Placement of Distributed Generation Units in Distribution Systems Via an Enhanced Multi-objective Particle Swarm Optimization Algorithm. Journal of Zhejiang University-Science C-Computers & Electronics, 15(4):300–311, April 2014.

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[1698] Shueh-Hen Cheng, Hsi-Jen Chen, Hsuan Chang, Cheng-Kai Chang, and Yi-Ming Chen. Multi-objective Optimization for Two Catalytic Membrane Reactors - Methanol Synthesis and Hydrogen Production. Chemical Engineering Science, 63(6):1428–1437, March 2008.

[1699] Shuo Cheng, Jianhua Zhou, and Mian Li. A New Hybrid Algorithm for Multi-Objective Robust Optimization with Interval Uncertainty. Journal of Mechanical Design, 137(2), February 2015. Article Number: 021401.

[1700] Ji cheng Liu, Su li Yan, and Jian xun Qi. A hybrid particle swarm optimization approach with neural network and set pair analysis for transmission network planning. Journal of Central South University of Technology, 15:321–326, December 2008.

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[1705] Hossein Rajabalipour Cheshmehgaz, Mohamad Ishak Desa, and Antoni Wibowo. A flexible three-level logistic network design considering cost and time criteria with a multi-objective evolutionary algorithm. Journal of Intelligent Manufacturing, 24(2):277–293, April 2013.

[1706] Hossein Rajabalipour Cheshmehgaz, Mohamad Ishak Desa, and Antoni Wibowo. An effective model of multiple multi-objective evolutionary algorithms with the assistance of regional multi-objective evolutionary algorithms: VIP-MOEAs. Applied Soft Computing, 13(5):2863–2895, May 2013.

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[1708] Hossein Rajabalipour Cheshmehgaz, Habibollah Haron, Farahnaz Kazemipour, and Mohamad Ishak Desa. Accumulated risk of body postures in assembly line balancing problem and modeling through a multi-criteria fuzzy-genetic algorithm. Computers & Industrial Engineering, 63(2):503–512, September 2012.

[1709] Hossein Rajabalipour Cheshmehgaz, Habibollah Haron, and Abdollah Sharifi. The review of multiple evolutionary searches and multi-objective evolutionary algorithms. Artificial Intelligence Review, 43(3):311–343, March 2015.

[1710] Hossein Rajabalipour Cheshmehgaz, Md. Nazrul Islam, and Mohammad Ishak Desa. A polar-based guided multi-objective evolutionary algorithm to search for optimal solutions interested by decision-makers in a logistics network design problem. Journal of Intelligent Manufacturing, 25(4):699–726, August 2014.

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[1714] Remy Chevrier, Arnaud Liefoogheb, Laetitia Jourdan, and Clarisse Dhaenens. Solving a Dial-a-ride Problem With a Hybrid Evolutionary Multi-objective Approach: Application to Demand Responsive Transport. Applied Soft Computing, 12(4):1247–1258, April 2012.

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[1722] S. C. Chiam, K. C. Tan, and A. Al Mamun. Investigating technical trading strategy via an multi-objective evolutionary platform. Expert Systems with applications, 36(7):10408–10423, September 2009.

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[1726] Swee Chiang Chiam, Kay Chen Tan, and Abdullah Al Mamun. Molecular Dynamics Optimizer. In Shigeru Obayashi, Kalyanmoy Deb, Carlo Poloni, Tomoyuki Hiroyasu, and Tadahiko Murata, editors, Evolutionary Multi-Criterion Optimization, 4th International Conference, EMO 2007, pages 302– 316, Matshushima, Japan, March 2007. Springer. Lecture Notes in Computer Science Vol. 4403.

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[1730] Chao-Lung Chiang. Efficient Trade-Off Algorithm for Hydrothermal Power Systems. In 2007 IEEE Congress on Evolutionary Computation (CEC’2007), pages 2325–2330, Singapore, September 2007. IEEE Press.

[1731] Tsung-Che Chiang, Hsueh-Chien Cheng, and Li-Chen Fu. Multiobjective Permutation Flow Shop Scheduling Using a Memetic Algorithm with an NEH-Based Local Search. In De-Shuang Huang, Kang-Hyun Jo, Hong-Hee Lee, Hee-Jun Kang, and Vitoantonio Bevilacqua, editors, Emerging Intelligent Computing Technology and Applications, 5th International Conference on Intelligent Computing, ICIC 2009, pages 813–825, Ulsan, South Korea, September 16-19 2009. Springer. Lecture Notes in Computer Science Vol. 5754.

[1732] Tsung-Che Chiang, Hsueh-Chien Cheng, and Li-Chen Fu. NNMA: An effective memetic algorithm for solving multiobjective permutation flow shop scheduling problems. Expert Systems with Applications, 38(5):5986–5999, May 2011.

[1733] Tsung-Che Chiang and Li-Chen Fu. Multiobjective Job Shop Scheduling using Genetic Algorithm with Cyclic Fitness Assignment. In 2006 IEEE Congress on Evolutionary Computation (CEC’2006), pages 11035–11042, Vancouver, BC, Canada, July 2006. IEEE.

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[1735] Tsung-Che Chiang and Wei-Huai Hsu. A knowledge-based evolutionary algorithm for the multiobjective vehicle routing problem with time windows. Computers & Operations Research, 45:25–37, May 2014.

[1736] Tsung-Che Chiang and Yung-Pin Lai. MOEA/D-AMS: Improving MOEA/D by an Adaptive Mating Selection Mechanism. In 2011 IEEE Congress on Evolutionary Computation (CEC’2011), pages 1473–1480, New Orleans, Lousiana, USA, 5-8 June 2011. IEEE Service Center.

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[1739] Kazuhisa Chiba, Masahiro Kanazaki, Atthaphon Ariyarit, Hideyuki Yoda, Shoma Ito, Koki Kitagawa, and Toru Shimada. Multidisciplinary Design Exploration for Sounding Launch Vehicle using Hybrid Rocket Engine in View of Ballistic Performance. International Journal of Turbo & Jet-Engines, 32(3):299–304, September 2015.

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[1849] B. Cobacho, R. Caballero, M. Gonzalez, and J. Molina. Planning federal public investment in Mexico using multiobjective decision making. Journal Of The Operational Research Society, 61(9):1328–1339, September 2010.

[1850] Corie L. Cobb, Ying Zhang, Alice M. Agogino, and Jennifer Mangold. Knowledge-Based Evolutionary Linkage in MEMS Design Synthesis. In Ying ping Chen and Meng-Hiot Lim, editors, Linkage in Evolutionary Computation, pages 461–483. Springer-Verlag, Berlin Heidelberg, 2008.

[1851] Carlos Cobos, Claudia Montealegre, María-Fernanda Mejía, Martha Mendoza, and Elizabeth León. Web document clustering based on a new niching Memetic Algorithm, Term-Document Matrix and Bayesian Information Criterion. In 2010 IEEE Congress on Evolutionary Computation (CEC’2010), pages 4629–4636, Barcelona, Spain, July 18–23 2010. IEEE Press.

[1852] Grant Cochenour, Jerad Simon, Sanjoy Das, Anil Pahwa, and Surasish Nag. A Pareto Archive Evolutionary Strategy Based Radial Basis Function Neural Network Training Algorithm for Failure Rate Prediction in Overhead Feeders. In Hans-Georg Beyer et al., editor, 2005 Genetic and Evolutionary Computation Conference (GECCO’2005), volume 2, pages 2127–2132, New York, USA, June 2005. ACM Press.

[1853] Jeffery K. Cochran, Shwu-Min Horng, and John W. Fowler. A Multi-Population Genetic Algorithm to Solve Multi-Objective Scheduling Problems for Parallel Machines. Computers and Operations Research, 30(7):1087– 1102, 2003.

[1854] M. Cococcioni, G. Corsini, B. Lazzerini, and F. Marcelloni. Solving the ocean color inverse problem by using evolutionary multi-objective optimization of neuro-fuzzy systems. International Journal of Knowledge-Based and Intelligent Engineering Systems, 12(5-6):39–55, 2008.

[1855] M. Cococcioni, P. Ducange, B. Lazzerini, and F. Marcelloni. A comparison of multi-objective evolutionary algorithms in fuzzy rule-based systems generation. In Proceedings of the North American Fuzzy Information Processing Society (NAFIPS’06), Montreal, Canada, June 2006.

[1856] M. Cococcioni, P. Ducange, B. Lazzerini, F. Marcelloni, and M. Vecchio. Identification of mamdani fuzzy systems based on a multi-objective genetic algorithm. In AI*IA 2005 Workshop on Evolutionary Computation, volume 1, pages 1–10, Milan, Italy, 2005.

[1857] M. Cococcioni, P. Guasqui, B. Lazzerini, and F. Marcelloni. Identification of Takagi-Sugeno Fuzzy Systems based on Multi-Objective Genetic Algorithms. In International Workshop on Fuzzy Logic and Applications, pages 172–177. Springer-Verlag. Lecture Notes on Artificial Intelligence Vol. 3849, 2005.

[1858] Marco Cococcioni, Pietro Ducange, Beatrice Lazzerini, and Francesco Marcelloni. A New Multi-Objective Evolutionary Algorithm Based on Convex Hull for Binary Classifier Optimization. In 2007 IEEE Congress on Evolutionary Computation (CEC’2007), pages 3150–3156, Singapore, September 2007. IEEE Press.

[1859] Marco Cococcioni, Pietro Ducange, Beatrice Lazzerini, and Francesco Marcelloni. A Pareto-based multi-objective evolutionary approach to the identification of Mamdani fuzzy systems. Soft Computing, 11(11):1013–1031, September 2007.

[1860] Marco Cococcioni, Beatrice Lazzerini, and Francesco Marcelloni. Towards Efficient Multi-objective Genetic Takagi-Sugeno Fuzzy Systems for High Dimensional Problems. In Yoel Tenne and Chi-Keong Goh, editors, Computational Intelligence in Expensive Optimization Problems, pages 397–422. Springer, Berlin, Germany, 2010. ISBN 978-3-642-10700-9.

[1861] Marco Cococcioni, Beatrice Lazzerini, and Francesco Marcelloni. On reducing computational overhead in multi-objective genetic Takagi-Sugeno fuzzy systems. Applied Soft Computing, 11(1):675–688, January 2011.

[1862] Andre L.V. Coelho, Everlandio Fernandes, and Katti Faceli. Multi-objective Design of Hierarchical Consensus Functions for Clustering Ensembles Via Genetic Programming. Decision Support Systems, 51(4):794–809, November 2011.

[1863] Dayanne G. Coelho, Elizabeth F. Wanner, Sergio R. Souza, Eduardo G. Carrano, and Robin C. Purshouse. A Multiobjective Evolutionary Algorithm for the 2D Guillotine Strip Packing Problem. In 2012 IEEE Congress on Evolutionary Computation (CEC’2012), pages 1012–1019, Brisbane, Australia, June 10-15 2012. IEEE Press.

[1864] Guilherme P. Coelho, Ana Estela A. da Silva, and Fernando J. Von Zuben. Evolving Phylogenetic Trees: A Multiobjective Approach. In Marie-France Sagot and Maria Emilia M.T. Walter, editors, Second Brazilian Symposium on Bioinformatics, BSB 2007, pages 113–125. Springer. Lecture Notes in Computer Science Vol. 4643, Angra dos Reis, Brazil, August 29-31 2007. ISBN 978-3-540-73730-8.

[1865] Guilherme P. Coelho, Ana Estela A. da Silva, and Fernando J. Von Zuben. An Immune-inspired Multi-objective Approach to the Reconstruction of Phylogenetic Trees. Neural Computing & Applications, 19(8):1103–1132, November 2010.

[1866] Guilherme P. Coelho, Fernando J. Von Zuben, and Ana Estela A. da Silva. A Multiobjective Approach to Phylogenetic Trees: Selecting the Most Promising Solutions from the Pareto Front. In Seventh International Conference on Intelligent Systems Design and Applications, ISDA 2007, pages 837–842, Rio de Janeiro, Brazil, 20-24 October 2007. IEEE Computer Society Press. ISBN 978-0-7695-2976-9.

[1867] Guilherme P. Coelho and Fernando Von Zuben. Omni-aiNet: An Immuneinspired Approach for Omni Optimization. In Hughes Bersini and Jorge Carneiro, editors, Artificial Immune Systems, 5th International Conference, ICARIS 2006, Proceedings, pages 294–308. Springer-Verlag, Lecture Notes in Computer Science Vol. 4163, Oeiras, Portugal, September 2006.

[1868] Guilherme Palermo Coelho, Fabrício Olivetti de França, and Fernando J. Von Zuben. Improving a Multi-Objective Multipopulation Artificial Immune Network for Biclustering. In 2009 IEEE Congress on Evolutionary Computation (CEC’2009), pages 2748–2755, Trondheim, Norway, May 2009. IEEE Press.

[1869] Guilherme Palermo Coelho, Fabrício Olivetti de França, and Fernando J. Von Zuben. A Multi-Objective Multipopulation Approach for Biclustering. In Peter J. Bentley, Doheon Lee, and Sungwon Jung, editors, Artificial Immune Systems, 7th International Conference, ICARIS 2008, pages 71–82, Phuket, Thailand, August 10-13 2008. Springer. Lecture Notes in Computer Science Volume 5132.

[1870] Guilherme Palermo Coelho and Fernando J. Von Zuben. A Concentration-Based Artificial Immune Network for Multi-objective Optimization. In Ricardo H.C. Takahashi, Kalyanmoy Deb, Elizabeth F. Wanner, and Salvatore Grecco, editors, Evolutionary Multi-Criterion Optimization, 6th International Conference, EMO 2011, pages 343–357, Ouro Preto, Brazil, April 2011. Springer. Lecture Notes in Computer Science Vol. 6576.

[1871] R. Filomeno Coelho, PH. Bouillard, and H. Bersini. PAMUC: A New Method to Handle Constraints and Multiobjectivity in Evolutionary Algorithms. In Tadeusz Burczyński and Andrzej Osyczka, editors, IUTAM Symposium on Evolutionary Methods in Mechanics, pages 91–100. Kluwer Academic Publishers, Cracow, Poland, 2004. ISBN 1-4020-2266-2.

[1872] Rajan Filomenho Coelho, Manuel Herrera, Manyu Xiao, and Weihong Zhang. On-line Metamodel-Assisted Optimization with Mixed Variables. In Jorge Magalhães-Mendes and David Greiner, editors, Evolutionary Algorithms and Metaheuristics in Civil Engineering and Construction Management, chapter 1, pages 1–15. Springer, Switzerland, 2015. ISBN 978-3-39-20405-5.

[1873] Rajan Filomeno Coelho. Multicriteria Optimization with Expert Rules for Mechanical Design. PhD thesis, Faculté des Sciences Appliquées, Université Libre de Bruxelles, Belgium, 2004.

[1874] Rajan Filomeno Coelho. Co-Evolutionary Optimization for Multi-Objective Design Under Uncertainty. Journal of Mechanical Design, 135(2), February 2013. Article Number: 021006.

[1875] Rajan Filomeno Coelho. Bi-Objective Hypervolume-Based Pareto Optimization a Gradient-Based Approach as an Alternative to Evolutionary Algorithms. Optimization Letters, 9(6):1091–1103, August 2015.

[1876] Rajan Filomeno Coelho. Probabilistic Dominance in Multiobjective Reliability-Based Optimization: Theory and Implementation. IEEE Transactions on Evolutionary Computation, 19(2):214–224, April 2015.

[1877] Rajan Filomeno Coelho, Hugues Bersini, and Philippe Bouillard. Parametrical Mechanical Design with Constraints and Preferences: Application to a Purge Valve. Computer Methods in Applied Mechanics and Engineering, 192(39-40):4355–4378, September 2003.

[1878] Rajan Filomeno Coelho and Philippe Bouillard. PAMUC II for Multicriteria Optimization of Mechanical Designs with Expert Rules. In 2004 Congress on Evolutionary Computation (CEC’2004), volume 1, pages 17–22, Portland, Oregon, USA, June 2004. IEEE Service Center.

[1879] Rajan Filomeno Coelho and Philippe Bouillard. Multi-Objective Reliability-Based Optimization with Stochastic Metamodels. Evolutionary Computation, 19(4):525–560, Winter 2011.

[1880] Rajan Filomeno Coelho, Jeremy Lebon, and Philippe Bouillard. Hierarchical Stochastic Metamodels Based on Moving Least Squares and Polynomial Chaos Expansion. Structural and Multidisciplinary Optimization, 43(5):707– 729, May 2011.

[1881] R.F. Coelho and P. Bouillard. A Multicriteria Evolutionary Algorithm for Mechanical Design Optimization with Expert Rules. International Journal for Numerical Methods in Engineering, 62(4):516–536, January 28 2005.

[1882] Vitor N. Coelho, Thays A. Oliveira, Igor M. Coelho, Bruno N. Coelho, Peter J. Fleming, Frederico G. Guimaraes, Helena Ramalhinho, Marcone J. F. Souza, El-Ghazali Talbi, and Thibaut Lust. Generic Pareto Local Search Metaheuristic for Optimization of Targeted Offers in a Bi-Objective Direct Marketing Campaign. Computers & Operations Research, 78:578–587, February 2017.

[1883] C. A. Coello Coello, G. Toscano Pulido, and A. Hernández Aguirre. Multi-Objective Evolutionary Algorithms for Structural Optimization. In K.J. Bathe, editor, Computational Fluid and Solid Mechanics 2003. Proceedings of the Second MIT Conference on Computational Fluid and Solid Mechanics, volume 2, pages 2244–2248, The Netherlands, June 2003. Elsevier.

[1884] Carlos A. Coello Coello. An Updated Survey of GA-Based Multiobjective Optimization Techniques. Technical Report Lania-RD-98-08, Laboratorio Nacional de Informática Avanzada (LANIA), Xalapa, Veracruz, México, December 1998.

[1885] Carlos A. Coello Coello. Using a Min-Max Method to solve Multiobjective Optimization Problems with Genetic Algorithms. In IBERAMIA’98. Lecture Notes in Computer Science, pages 303–314, Lisbon, Portugal, October 1998. Springer-Verlag.

[1886] Carlos A. Coello Coello. A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques. Knowledge and Information Systems. An International Journal, 1(3):269–308, August 1999.

[1887] Carlos A. Coello Coello. Constraint handling through a multiobjective optimization technique. In Annie S. Wu, editor, Proceedings of the 1999 Genetic and Evolutionary Computation Conference. Workshop Program, pages 117– 118, Orlando, Florida, July 1999.

[1888] Carlos A. Coello Coello. An Updated Survey of Evolutionary Multiobjective Optimization Techniques : State of the Art and Future Trends. In 1999 Congress on Evolutionary Computation, volume 1, pages 3–13, Washington, D.C., July 1999. IEEE Service Center.

[1889] Carlos A. Coello Coello. An Updated Survey of GA-Based Multiobjective Optimization Techniques. ACM Computing Surveys, 32(2):109–143, June 2000.

[1890] Carlos A. Coello Coello. Constraint-handling using an evolutionary multiobjective optimization technique. Civil Engineering and Environmental Systems, 17:319–346, 2000.

[1891] Carlos A. Coello Coello. Handling Preferences in Evolutionary Multiobjective Optimization: A Survey. In 2000 IEEE Congress on Evolutionary Computation, volume 1, pages 30–37, Piscataway, New Jersey, July 2000. IEEE Service Center.

[1892] Carlos A. Coello Coello. Treating Constraints as Objectives for Single-Objective Evolutionary Optimization. Engineering Optimization, 32(3):275– 308, 2000.

[1893] Carlos A. Coello Coello. A Short Tutorial on Evolutionary Multiobjective Optimization. In Eckart Zitzler, Kalyanmoy Deb, Lothar Thiele, Carlos A. Coello Coello, and David Corne, editors, First International Conference on Evolutionary Multi-Criterion Optimization, pages 21–40. Springer-Verlag. Lecture Notes in Computer Science No. 1993, 2001.

[1894] Carlos A. Coello Coello. Evolutionary Multi-Objective Optimization: A Critical Review. In Ruhul Sarker, Masoud Mohammadian, and Xin Yao, editors, Evolutionary Optimization, pages 117–146. Kluwer Academic Publishers, New York, February 2002. ISBN 0-7923-7654-4.

[1895] Carlos A. Coello Coello. Evolutionary Multiobjective Optimization: Current and Future Challenges. In Jose Benitez, Oscar Cordon, Frank Hoffmann, and Rajkumar Roy, editors, Advances in Soft Computing—Engineering, Design and Manufacturing, pages 243–256. Springer-Verlag, September 2003.

[1896] Carlos A. Coello Coello. Recent Trends in Evolutionary Multiobjective Optimization. In Ajith Abraham, Lakhmi Jain, and Robert Goldberg, editors, Evolutionary Multiobjective Optimization: Theoretical Advances And Applications, pages 7–32. Springer-Verlag, London, 2005. ISBN 1-85233-787-7.

[1897] Carlos A. Coello Coello. 20 Years of Evolutionary Multi-Objective Optimization: What Has Been Done and What Remains to be Done. In Gary Y. Yen and David B. Fogel, editors, Computational Intelligence: Principles and Practice, chapter 4, pages 73–88. IEEE Computational Intelligence Society, Vancouver, Canada, 2006. ISBN 0-9787135-0-8.

[1898] Carlos A. Coello Coello. The EMOO repository: a resource for doing research in evolutionary multiobjective optimization. IEEE Computational Intelligence Magazine, 1(1):37–45, February 2006.

[1899] Carlos A. Coello Coello. Evolutionary multi-objective optimization in finance. In Jean-Philippe Rennard, editor, Handbook of Research on Nature Inspired Computing for Economy and Management, volume 1, pages 74–88. Idea Group Reference, Hershey, UK, 2006. ISBN 1-59140-984-5.

[1900] Carlos A. Coello Coello. Evolutionary multiobjective optimization: A historical view of the field. IEEE Computational Intelligence Magazine, 1(1):28–36, February 2006.

[1901] Carlos A. Coello Coello. Evolutionary Multi-Objective Optimization: Some Current Research Trends and Topics that Remain to be Explored. Frontiers of Computer Science in China, 3(1):18–30, 2009.

[1902] Carlos A. Coello Coello. A Tutorial on Multi-Objective Optimization using Metaheuristics. In L.M. Esteban, B. Lacruz, F.J. López, P.M. Mateo, A. Pérez-Palomares, G. Sanz, and C. Paroissin, editors, The Pyrenees International Workshop and Summer School on Statistics, Probability and Operations Research SPO 2009, Monografías Matemáticas “García de Galdeano” No. 36, pages 19–38. Universidad de Zaragoza, Spain, December 2010. ISBN 978-84-15031-92-5.

[1903] Carlos A. Coello Coello. An Introduction to Multi-Objective Particle Swarm Optimizers. In António Gaspar-Cunha, Ricardo Takahashi, Gerald Schaefer, and Lino Costa, editors, Soft Computing in Industrial Applications, volume 96 of Advances in Intelligent and Soft Computing Series, pages 3–12, Berlin, 2011. Springer. ISBN 978-3-642-20504-0.

[1904] Carlos A. Coello Coello. Evolutionary Multi-Objective Optimization. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(5):444– 447, September/October 2011.

[1905] Carlos A. Coello Coello. Evolutionary Multi-Objective Optimization: Basic Concepts and Some Applications in Pattern Recognition. In José Francisco Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa, Cherif Ben-Youssef Brants, and Edwin Robert Hancock, editors, Pattern Recognition, Third Mexican Conference, MCPR 2011, pages 22–33. Springer, Lecture Notes in Computer Science Vol. 6718, Cancún, México, June/July 2011.

[1906] Carlos A. Coello Coello. Fundamentals of Evolutionary Multi-Objective Optimization. In Bogdan M. Wilamowski and J. David Irwin, editors, Industrial Electronics Handbook. Intelligent Systems, chapter 25, pages 25–1–25–11. CRC Press, Boca Raton, Florida, USA, second edition, 2011. ISBN 978-1-4398-0283-0.

[1907] Carlos A. Coello Coello and Alan D. Christiansen. An Approach to Multiobjective Optimization Using Genetic Algorithms. In Cihan H. Dagli, Metin Akay, C. L. Philip Chen, Benito R. Fernández, and Joydeep Ghosh, editors, Intelligent Engineering Systems Through Artificial Neural Networks. Volume 5. Fuzzy Logic and Evolutionary Programming, pages 411–416, St. Louis, Missouri, USA, November 1995. ASME Press.

[1908] Carlos A. Coello Coello and Alan D. Christiansen. Two New GA-based methods for multiobjective optimization. Civil Engineering Systems, 15(3):207– 243, 1998.

[1909] Carlos A. Coello Coello and Alan D. Christiansen. MOSES : A Multiobjective Optimization Tool for Engineering Design. Engineering Optimization, 31(3):337–368, 1999.

[1910] Carlos A. Coello Coello and Alan D. Christiansen. Multiobjective optimization of trusses using genetic algorithms. Computers and Structures, 75(6):647–660, May 2000.

[1911] Carlos A. Coello Coello, Alan D. Christiansen, and A. Hernández Aguirre. Multiobjective Design Optimization of Counterweight Balancing of a Robot Arm using Genetic Algorithms. In Proceedings of the Seventh International Conference on Tools with Artificial Intelligence, pages 20–23, Herndon, Virginia, U.S.A., November 1995. IEEE Computer Society Press.

[1912] Carlos A. Coello Coello, Alan D. Christiansen, and Arturo Hernández Aguirre. Use of Genetic Algorithms for Multiobjective Optimization of Counterweight Balancing of Robot Arms. In Jacob J. G. Chen, editor, EXPERSYS-95 Expert Systems Applications and Artificial Intelligence, pages 243–248, San Francisco, California, November 1995. I. I. T. T. International, Technology Transfer Series.

[1913] Carlos A. Coello Coello, Alan D. Christiansen, and Arturo Hernández Aguirre. Using a New GA-Based Multiobjective Optimization Technique for the Design of Robot Arms. Robotica, 16(4):401–414, July–August 1998.

[1914] Carlos A. Coello Coello and Nareli Cruz Cortés. An Approach to Solve Multiobjective Optimization Problems Based on an Artificial Immune System. In Jonathan Timmis and Peter J. Bentley, editors, First International Conference on Artificial Immune Systems (ICARIS’2002), pages 212–221. University of Kent at Canterbury, UK, September 2002. ISBN 1-902671-32-5.

[1915] Carlos A. Coello Coello and Nareli Cruz Cortés. Solving Multiobjective Optimization Problems using an Artificial Immune System. Genetic Programming and Evolvable Machines, 6(2):163–190, June 2005.

[1916] Carlos A. Coello Coello, Satchidananda Dehuri, and Susmita Ghosh, editors. Swarm Intelligence for Multi-objective Problems in Data Mining. Springer, Berlin/Heidelberg, 2009. ISBN: 978-3-642-03624-8.

[1917] Carlos A. Coello Coello, Clarisse Dhaenens, and Laetitia Jourdan, editors. Advances in Multi-Objective Nature Inspired Computing. Springer, Berlin/Heidelberg, 2010. ISBN 978-3-642-11217-1.

[1918] Carlos A. Coello Coello, Clarisse Dhaenens, and Laetitia Jourdan. Multi-Objective Combinatorial Optimization: Problematic and Context. In Carlos A. Coello Coello, Clarisse Dhaenens, and Laetitia Jourdan, editors, Advances in Multi-Objective Nature Inspired Computing, chapter 1, pages 1–21. Springer, Studies in Computational Intelligence, Vol. 272, Berlin, Germany, 2010. ISBN 978-3-642-11217-1.

[1919] Carlos A. Coello Coello and Arturo Hernández Aguirre. Design of Combinational Logic Circuits through an Evolutionary Multiobjective Optimization Approach. Artificial Intelligence for Engineering, Design, Analysis and Manufacture, 16(1):39–53, January 2002.

[1920] Carlos A. Coello Coello, Arturo Hernández Aguirre, and Bill P. Buckles. Evolutionary Multiobjective Design of Combinational Logic Circuits. In Jason Lohn, Adrian Stoica, Didier Keymeulen, and Silvano Colombano, editors, Proceedings of the Second NASA/DoD Workshop on Evolvable Hardware, pages 161–170, Los Alamitos, California, July 2000. IEEE Computer Society.

[1921] Carlos A. Coello Coello and Gary B. Lamont, editors. Applications of Multi-Objective Evolutionary Algorithms. World Scientific, Singapore, 2004. ISBN 981-256-106-4.

[1922] Carlos A. Coello Coello and Gary B. Lamont. An Introduction to Multi-Objective Evolutionary Algorithms and Their Applications. In Carlos A. Coello Coello and Gary B. Lamont, editors, Applications of Multi-Objective Evolutionary Algorithms, pages 1–28. World Scientific, Singapore, 2004.

[1923] Carlos A. Coello Coello, Gary B. Lamont, and David A. Van Veldhuizen. Evolutionary Algorithms for Solving Multi-Objective Problems. Springer, New York, second edition, September 2007. ISBN 978-0-387-33254-3.

[1924] Carlos A. Coello Coello and Ricardo Landa Becerra. Evolutionary Multiobjective Optimization using a Cultural Algorithm. In 2003 IEEE Swarm Intelligence Symposium Proceedings, pages 6–13, Indianapolis, Indiana, USA, April 2003. IEEE Service Center.

[1925] Carlos A. Coello Coello and Ricardo Landa Becerra. Evolutionary multiobjective optimization in materials science and engineering. Materials and Manufacturing Processes, 24(2):119–129, February 2009.

[1926] Carlos A. Coello Coello and Carlos E. Mariano Romero. Evolutionary Algorithms and Multiple Objective Optimization. In Matthias Ehrgott and Xavier Gandibleux, editors, Multiple Criteria Optimization: State of the Art Annotated Bibliographic Surveys, pages 277–331. Kluwer Academic Publishers, Boston, 2002.

[1927] Carlos A. Coello Coello and Margarita Reyes Sierra. A Coevolutionary Multi-Objective Evolutionary Algorithm. In Proceedings of the 2003 Congress on Evolutionary Computation (CEC’2003), volume 1, pages 482–489, Canberra, Australia, December 2003. IEEE Press.

[1928] Carlos A. Coello Coello and Margarita Reyes Sierra. A Study of the Parallelization of a Coevolutionary Multi-Objective Evolutionary Algorithm. In Raúl Monroy, Gustavo Arroyo-Figueroa, Luis Enrique Sucar, and Humberto Sossa, editors, Proceedings of the Third Mexican International Conference on Artificial Intelligence (MICAI’2004), pages 688–697. Springer Verlag. Lecture Notes in Artificial Intelligence Vol. 2972, April 2004.

[1929] Carlos A. Coello Coello and Maximino Salazar Lechuga. MOPSO: A Proposal for Multiple Objective Particle Swarm Optimization. In Congress on Evolutionary Computation (CEC’2002), volume 2, pages 1051–1056, Piscataway, New Jersey, May 2002. IEEE Service Center.

[1930] Carlos A. Coello Coello and Gregorio Toscano Pulido. A Micro-Genetic Algorithm for Multiobjective Optimization. In Eckart Zitzler, Kalyanmoy Deb, Lothar Thiele, Carlos A. Coello Coello, and David Corne, editors, First International Conference on Evolutionary Multi-Criterion Optimization, pages 126–140. Springer-Verlag. Lecture Notes in Computer Science No. 1993, 2001.

[1931] Carlos A. Coello Coello and Gregorio Toscano Pulido. Multiobjective Optimization using a Micro-Genetic Algorithm. In Lee Spector, Erik D. Goodman, Annie Wu, W.B. Langdon, Hans-Michael Voigt, Mitsuo Gen, Sandip Sen, Marco Dorigo, Shahram Pezeshk, Max H. Garzon, and Edmund Burke, editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2001), pages 274–282, San Francisco, California, 2001. Morgan Kaufmann Publishers.

[1932] Carlos A. Coello Coello and Gregorio Toscano Pulido. Multiobjective Structural Optimization using a Micro-Genetic Algorithm. Structural and Multidisciplinary Optimization, 30(5):388–403, November 2005.

[1933] Carlos A. Coello Coello, Gregorio Toscano Pulido, and Efrén Mezura Montes. Current and future research trends in evolutionary multiobjective optimization. In Manuel Gra na, Richard Duro, Alicia d’Anjou, and Paul P. Wang, editors, Information Processing with Evolutionary Algorithms: From Industrial Applications to Academic Speculations, pages 213–231. Springer-Verlag, 2005. ISBN 1-8523-3866-0.

[1934] Carlos A. Coello Coello, Gregorio Toscano Pulido, and Maximino Salazar Lechuga. Handling Multiple Objectives With Particle Swarm Optimization. IEEE Transactions on Evolutionary Computation, 8(3):256–279, June 2004.

[1935] Carlos A. Coello Coello, David A. Van Veldhuizen, and Gary B. Lamont. Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, New York, May 2002. ISBN 0-3064-6762-3.

[1936] Carlos Artemio Coello Coello. An Empirical Study of Evolutionary Techniques for Multiobjective Optimization in Engineering Design. PhD thesis, Department of Computer Science, Tulane University, New Orleans, Louisiana, USA, April 1996.

[1937] Corrado Coia and Brian J. Ross. Automatic Evolution of Conceptual Building Architectures. In 2011 IEEE Congress on Evolutionary Computation (CEC’2011), pages 1140–1147, New Orleans, Louisiana, USA, 5–8 June 2011. IEEE Service Center.

[1938] Mitchell Colby, Logan Yliniemi, Paolo Pezzini, David Tucker, Kenneth “Mark” Bryden, and Kagan Tumer. Multiobjective Neuroevolutionary Control for a Fuel Cell Turbine Hybrid Energy System. In 2016 Genetic and Evolutionary Computation Conference (GECCO’2016), pages 877–884, Denver, Colorado, USA, 20-24 July 2016. ACM Press. ISBN 978-1-4503-4206-3.

[1939] David A. Coley. Evolving Green Buildings. In Erick Cantú-Paz, editor, 2002 Genetic and Evolutionary Computation Conference. Late-Breaking Papers, pages 62–68, New York, July 2002.

[1940] Y. Collette and P. Siarry. Three new metrics to measure the convergence of metaheuristics towards the Pareto frontier and the aesthetic of a set of solutions in biobjective optimization. Computers & Operations Research, 32(4):773–792, April 2005.

[1941] Y. Collette, P. Siarry, and H.-I. Wong. Multidimensional Data Representation Aimed at the Interpretation of Results from Multiobjective Optimization. In PPSN/SAB Workshop on Multiobjective Problem Solving from Nature (MPSN), Paris, France, September 2000.

[1942] Y. Collette, P. Siarry, and H.-I. Wong. A Systematic Comparison of Performance of Various Multiple Objective Metaheuristics Using a Common Set of Analytical Test Functions. Foundations of Computing and Decision Sciences, 25(4):249–271, 2000.

[1943] Yann Collette and Patrick Siarry. Multiobjective Optimization. Principles and Case Studies. Springer, August 2003.

[1944] J. Manuel Colmenar, José L. Risco-Martín, David Atienza, and J. Ignacio Hidalgo. Multi-Objective Optimization of Dynamic Memory Managers Using Grammatical Evolution. In 2011 Genetic and Evolutionary Computation Conference (GECCO’2011), pages 1819–1826, Dublin, Ireland, July 12-16 2011. ACM Press.

[1945] Glauber R. Colnago and Paulo B. Correia. Multiobjective dispatch of hydrogenerating units using a two-step genetic algorithm method. In 2009 IEEE Congress on Evolutionary Computation (CEC’2009), pages 2554– 2560, Trondheim, Norway, May 2009. IEEE Press.

[1946] Gualtiero Colombo and Stuart M. Allen. A Decomposed Approach for the Minimum Interference Frequency Assignment. In Ying ping Chen and Meng-Hiot Lim, editors, Linkage in Evolutionary Computation, pages 389–417. Springer-Verlag, Berlin Heidelberg, 2008.

[1947] Gualtiero Colombo and Christine Mumford. Comparing Algorithms, Representations and Operators for the Multi-Objective Knapsack Problem. In 2005 IEEE Congress on Evolutionary Computation (CEC’2005), volume 2, pages 1268–1275, Edinburgh, Scotland, September 2005. IEEE Service Center.

[1948] Iulia Maria Comsa, Crina Grosan, and Shengxiang Yang. Dynamics in the Multi-objective Subset Sum: Analysing the Behavior of Population Based Algorithms. In Shengxiang Yang and Xin Yao, editors, Evolutionary Computation for Dynamic Optimization Problems, chapter 12, pages 299–313. Springer-Verlag, Berlin, Germany, 2013. ISBN 978-3-642-38415-8.

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[2245] F. de Toro, E. Ros, S. Mota, and J. Ortega. Evolutionary algorithms for multiobjective and multimodal optimization of diagnostic schemes. IEEE Transactions on Biomedical Engineering, 53(2):178–189, February 2006.

[2246] Francisco de Toro, Julio Ortega, Javier Fernández, and A. Díaz. PSFGA: A Parallel Genetic Algorithm for Multiobjective Optimization. In F. Vajda and N. Podhorszki, editors, 10th Euromicro Workshop on Parallel, Distributed and Network-Based Processing, pages 384–391. IEEE, 2002.

[2247] Francisco de Toro, Eduardo Ros, Sonia Mota, and Julio Ortega. Multi-Objective Optimization Evolutionary Algorithms Applied to Paroxysmal Atrial Fibrillation Diagnosis Based on the k-Nearest Neighbours Classifier. In Francisco J. Garijo, José C. Riquelme, and Miguel Toro, editors, Advances in Artificial Intelligence–IBERAMIA 2002 Proceedings. 8th Ibero-American Conference on AI, pages 313–318, Seville, Spain, 2002. Springer. Lecture Notes in Artificial Intelligence Vol. 2527.

[2248] Francisco de Toro, Eduardo Ros, Sonia Mota, and Julio Ortega. Non-invasive Atrial Disease Diagnosis Using Decision Rules: A Multi-objective Optimization Approach. In Carlos M. Fonseca, Peter J. Fleming, Eckart Zitzler, Kalyanmoy Deb, and Lothar Thiele, editors, Evolutionary Multi-Criterion Optimization. Second International Conference, EMO 2003, pages 638–647, Faro, Portugal, April 2003. Springer. Lecture Notes in Computer Science. Volume 2632.

[2249] R. de Toro, R. Jimenez, M. Sanchez, and J. Ortega. Synthesis of hybrid CBL/CMOS cell using multiobjective evolutionary algorithms. In Integrated Circuit and System Design, pages 629–637. Springer-Verlag. Lecture Notes in Computer Science Vol. 3728, 2005.

[2250] F. de Toro Negro, J. Ortega, E. Ros, S. Mota, B. Paechter, and J.M. Martín. PSFGA: Parallel Processing and Evolutionary Computation for Multiobjective Optimisation. Parallel Computing, 30(5–6):721–739, May–June 2004.

[2251] K. Deb. An introduction to genetic algorithms. Sadhana-Academy Proceedings In Engineering Sciences, 24:293–315, August-October 1999.

[2252] K. Deb and DE. Goldberg. An Investigation of Niche and Species Formation in Genetic Function Optimization. In 3rd Internation Conference on Genetic Algorithms, pages 42–50, Virginia, USA, June 4-7 1989. Morgan Kaufmann Publishers. ISBN 1-55860-066-3.

[2253] K. Deb, P. Jain, NK. Gupta, and HK. Maji. Multiobjective placement of electronic components using evolutionary algorithms. IEEE Transactions on Components and Packaging Technologies, 27(3):480–492, September 2004.

[2254] K. Deb, K. Mitra, R. Dewri, and S. Majumdar. Towards a Better Understanding of the Epoxy Polymerization Process Using Multi-objective Evolutionary Computation. Chemical Engineering Science, 59(20):4261–4277, 2004.

[2255] Kalyamoy Deb. Two Approaches for Single and Multi-Objective Dynamic Optimization. In Enrique Alba, Amir Nakib, and Patrick Siarry, editors, Metaheuristics for Dynamic Optimization, chapter 6, pages 99–116. Springer, Berlin, Germany, 2013. ISBN 978-3-642-30664-8.

[2256] Kalyanmoy Deb. Multi-Objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems. Technical Report CI-49/98, Dortmund: Department of Computer Science/LS11, University of Dortmund, Germany, 1998.

[2257] Kalyanmoy Deb. Evolutionary Algorithms for Multi-Criterion Optimization in Engineering Design. In Kaisa Miettinen, Marko M. Mäkelä, Pekka Neittaanmäki, and Jacques Periaux, editors, Evolutionary Algorithms in Engineering and Computer Science, chapter 8, pages 135–161. John Wiley & Sons, Ltd, Chichester, UK, 1999.

[2258] Kalyanmoy Deb. Multi–Objective Evolutionary Algorithms: Introducing Bias Among Pareto–Optimal Solutions. KanGAL report 99002, Indian Institute of Technology, Kanpur, India, 1999.

[2259] Kalyanmoy Deb. Multi-Objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems. Evolutionary Computation, 7(3):205– 230, Fall 1999.

[2260] Kalyanmoy Deb. Non-Linear Goal Programming using Multi-Objective Genetic Algorithms. Technical Report CI-60/98, Dortmund: Department of Computer Science/LS11, University of Dortmund, Germany, 1999.

[2261] Kalyanmoy Deb. Solving Goal Programming Problems Using Multi-Objective Genetic Algorithms. In 1999 Congress on Evolutionary Computation, pages 77–84, Washington, D.C., July 1999. IEEE Service Center.

[2262] Kalyanmoy Deb. Multi-objective Evolutionary Optimization: Past, Present and Future. In Ian C. Parmee, editor, Proceedings of the Fourth International Conference on Adaptive Computing in Design and Manufacture (ACDM’2000), pages 225–236. PEDC, University of Plymouth, UK, Springer London, 2000.

[2263] Kalyanmoy Deb. Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Chichester, UK, 2001. ISBN 0-471-87339-X.

[2264] Kalyanmoy Deb. Nonlinear goal programming using multi-objective genetic algorithms. Journal of the Operational Research Society, 52(3):291–302, 2001.

[2265] Kalyanmoy Deb. Multi-objective Evolutionary Algorithms: Introducing Bias Among Pareto-optimal Solutions. In Ashish Ghosh and Shigeyoshi Tsutsui, editors, Advances in Evolutionary Computing. Theory and Applications, pages 263–292. Springer, Berlin, 2003.

[2266] Kalyanmoy Deb. Unveiling innovative design principles by means of multiple conflicting objectives. Engineering Optimization, 35(5):445–470, October 2003.

[2267] Kalyanmoy Deb. An Ideal Evolutionary Multi-Objective Optimization Procedure. IPSJ Transactions on Mathematical Modeling and Its Applications, 45(SIG 2 (TOM 10)):1–11, February 2004.

[2268] Kalyanmoy Deb. Multi-Objective Optimization. In Edmund K. Burke and Graham Kendall, editors, Search Methodologies. Introductory Tutorials in Optimization and Decision Support Techniques, pages 273–316. Springer, 2005.

[2269] Kalyanmoy Deb. Current trends in evolutionary multi-objective optimization. International Journal for Simulation and Multidisciplinary Design Optimization, 1(1):1–8, October 2007.

[2270] Kalyanmoy Deb. Evolutionary Multi-Objective Optimization Without Additional Parameters. In Fernando G. Lobo, Cláudio F. Lima, and Zbigniew Michalewicz, editors, Parameter Setting in Evolutionary Algorithms, pages 241–257. Springer-Verlag, Berlin, 2007.

[2271] Kalyanmoy Deb. A Robust Evolutionary Framework for Multi-Objective Optimization. In 2008 Genetic and Evolutionary Computation Conference (GECCO’2008), pages 633–647, Atlanta, USA, July 2008. ACM Press. ISBN 978-1-60558-131-6.

[2272] Kalyanmoy Deb. Evolutionary Multi-Objective Optimization and Decision Making. In Bogan Filipic and Jurij Silc, editors, Third International Conference on Bioinspired Optimization Methods and their Applications (BIOMA 2008), pages 3–15, Ljubljana, Slovenia, October 2008. Jozef Stefan Institute. ISBN 978-961-264-002-6.

[2273] Kalyanmoy Deb. Introduction to Evolutionary Multiobjective Optimization. In Jürgen Branke, Kalyanmoy Deb, Kaisa Miettinen, and Roman Slowinski, editors, Multiobjective Optimization. Interactive and Evolutionary Approaches, pages 59–96. Springer. Lecture Notes in Computer Science Vol. 5252, Berlin, Germany, 2008.

[2274] Kalyanmoy Deb. Scope of stationary multi-objective evolutionary optimization: a case study on a hydro-thermal power dispatch problem. Journal of Global Optimization, 41(4):479–515, August 2008.

[2275] Kalyanmoy Deb. Evolution’s Niche in Multi-Criterion Problem Solving. In Andrew Lewis, Sanaz Mostaghim, and Marcus Randall, editors, Biologically-Inspired Optimisation Methods, pages 1–21. Springer, 2009. ISBN 978-3-642-01261-7.

[2276] Kalyanmoy Deb. Recent Developments in Evolutionary Multi-Objective Optimization. In Matthias Ehrgott, José Rui Figueira, and Salvatore Greco, editors, Trends in Multiple Criteria Decision Analysis, chapter 12, pages 339– 368. Springer, International Series in Operations Research and Management Science, 2010. ISBN 978-1-4419-5903-4.

[2277] Kalyanmoy Deb. Multi-objective Optimisation Using Evolutionary Algorithms: An Introduction. In Lihui Wang, Amos H.C. Ng, and Kalyanmoy Deb, editors, Multi-objective Evolutionary Optimisation for Product Design and Manufacturing, chapter 1, pages 3–34. Springer, London, UK, 2011. ISBN 978-0-85729-617-7.

[2278] Kalyanmoy Deb. Advances in Evolutionary Multi-objective Optimization. In Gordon Fraser and Jerffeson Teixeira de Souza, editors, Search Based Software Engineering, 4th International Symposium, SSBSE 2012, pages 1–26. Springer. Lecture Notes in Computer Science Vol. 7515, Riva del Garda, Italy, September 28-30 2012.

[2279] Kalyanmoy Deb, Mohamed Abouhawwash, and Joydeep Dutta. An Optimality Theory Based Proximity Measure for Evolutionary Multi-Objective and Many-Objective Optimization. In António Gaspar-Cunha, Carlos Henggeler Antunes, and Carlos Coello Coello, editors, Evolutionary Multi-Criterion Optimization, 8th International Conference, EMO 2015, pages 18–33. Springer.

Lecture Notes in Computer Science Vol. 9019, Guimarães, Portugal, March 29 - April 1 2015.

[2280] Kalyanmoy Deb, Samir Agrawal, Amrit Pratap, and T. Meyarivan. A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II. KanGAL report 200001, Indian Institute of Technology, Kanpur, India, 2000.

[2281] Kalyanmoy Deb, Samir Agrawal, Amrit Pratap, and T. Meyarivan. A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II. In Marc Schoenauer, Kalyanmoy Deb, Günter Rudolph, Xin Yao, Evelyne Lutton, Juan Julian Merelo, and Hans-Paul Schwefel, editors, Proceedings of the Parallel Problem Solving from Nature VI Conference, pages 849–858, Paris, France, 2000. Springer. Lecture Notes in Computer Science No. 1917.

[2282] Kalyanmoy Deb, Sunith Bandaru, and Cem Celal Tutum. Temporal Evolution of Design Principles in Engineering Systems: Analogies with Human Evolution. In Carlos A. Coello Coello, Vincenzo Cutello, Kalyanmoy Deb, Stephanie Forrest, Giuseppe Nicosia, and Mario Pavone, editors, Parallel Problem Solving from Nature - PPSN XII, 12th International Conference, pages 1–10, Taormina, Italy, September 1-5 2012. Springer. Lecture Notes in Computer Science Vol. 7492.

[2283] Kalyanmoy Deb and Shamik Chaudhuri. I-MODE: An Interactive Multiobjective Optimization and Decision-Making Using Evolutionary Methods. In Shigeru Obayashi, Kalyanmoy Deb, Carlo Poloni, Tomoyuki Hiroyasu, and Tadahiko Murata, editors, Evolutionary Multi-Criterion Optimization, 4th International Conference, EMO 2007, pages 788–802, Matshushima, Japan, March 2007. Springer. Lecture Notes in Computer Science Vol. 4403.

[2284] Kalyanmoy Deb, Shamik Chaudhuri, and Kaisa Miettinen. Towards Estimating Nadir Objective Vector Using Evolutionary Approaches. In Maarten Keijzer et al., editor, 2006 Genetic and Evolutionary Computation Conference (GECCO’2006), volume 1, pages 643–650, Seattle, Washington, USA, July 2006. ACM Press. ISBN 1-59593-186-4.

[2285] Kalyanmoy Deb and Rituparna Datta. A Fast and Accurate Solution of Constrained Optimization Problems Using a Hybrid Bi-Objective and Penalty Function Approach. In 2010 IEEE Congress on Evolutionary Computation (CEC’2010), pages 165–172, Barcelona, Spain, July 18–23 2010. IEEE Press.

[2286] Kalyanmoy Deb and Rituparna Datta. A bi-objective constrained optimization algorithm using a hybrid evolutionary and penalty function approach. Engineering Optimization, 45(5):503–527, May 1 2013.

[2287] Kalyanmoy Deb and Tushar Goel. Controlled Elitist Non-dominated Sorting Genetic Algorithms for Better Convergence. In Eckart Zitzler, Kalyanmoy Deb, Lothar Thiele, Carlos A. Coello Coello, and David Corne, editors, First International Conference on Evolutionary Multi-Criterion Optimization, pages 67–81. Springer-Verlag. Lecture Notes in Computer Science No. 1993, 2001.

[2288] Kalyanmoy Deb and Tushar Goel. A Hybrid Multi-Objective Evolutionary Approach to Engineering Shape Design. In Eckart Zitzler, Kalyanmoy Deb, Lothar Thiele, Carlos A. Coello Coello, and David Corne, editors, First International Conference on Evolutionary Multi-Criterion Optimization, pages 385–399. Springer-Verlag. Lecture Notes in Computer Science No. 1993, 2001.

[2289] Kalyanmoy Deb and Tushar Goel. Multi-Objective Evolutionary Algorithms for Engineering Shape Design. In Ruhul Sarker, Masoud Mohammadian, and Xin Yao, editors, Evolutionary Optimization, pages 146–175. Kluwer Academic Publishers, New York, February 2002. ISBN 0-7923-7654-4.

[2290] Kalyanmoy Deb and Tushar Goyal. Controlled Elitist Non-dominated Sorting Genetic Algorithms for Better Convergence. KanGAL report 200004, Indian Institute of Technology, Kanpur, India, 2000.

[2291] Kalyanmoy Deb and Tushar Goyal. Multi-Objective Evolutionary Algorithms for Engineering Shape Design. KanGAL report 200003, Indian Institute of Technology, Kanpur, India, 2000.

[2292] Kalyanmoy Deb and Himanshu Gupta. Searching for Robust Pareto-Optimal Solutions in Multi-objective Optimization. In Carlos A. Coello Coello, Arturo Hernández Aguirre, and Eckart Zitzler, editors, Evolutionary Multi-Criterion Optimization. Third International Conference, EMO 2005, pages 150–164, Guanajuato, México, March 2005. Springer. Lecture Notes in Computer Science Vol. 3410.

[2293] Kalyanmoy Deb and Himanshu Gupta. Introducing robustness in multiobjective optimization. Evolutionary Computation, 14(4):463–494, Winter 2006.

[2294] Kalyanmoy Deb and Naveen Kumar Gupta. Optimal Operating Conditions for Overhead Crane Maneuvering Using Multi-objective Evolutionary Algorithms. In Kalyanmoy Deb et al., editor, Genetic and Evolutionary Computation–GECCO 2004. Proceedings of the Genetic and Evolutionary Computation Conference. Part I, pages 1042–1053, Seattle, Washington, USA, June 2004. Springer-Verlag, Lecture Notes in Computer Science Vol. 3102.

[2295] Kalyanmoy Deb and Shivam Gupta. Towards a Link Between Knee Solutions and Preferred Solution Methodologies. In Bijaya Ketan Panigrahi, Swagatam Das, Ponnuthurai Nagaratnam Suganthan, and Subhransu Sekhar Dash, editors, Swarm, Evolutionary, and Memetic Computing, First International Conference on Swarm, Evolutionary and Memetic Computing, SEMCCO 2010, pages 182–189. Springer-Verlag. Lecture Notes in Computer Science Vol. 6466, Chennai, India, December 16-18 2010.

[2296] Kalyanmoy Deb and Shivam Gupta. Understanding knee points in bicriteria problems and their implications as preferred solution principles. Engineering Optimization, 43(11):1175–1204, 2011.

[2297] Kalyanmoy Deb, Shubham Gupta, David Daum, Juergen Branke, Abhishek Kumar Mall, and Dhanesh Padmanabhan. Reliability-Based Optimization Using Evolutionary Algorithms. IEEE Transactions on Evolutionary Computation, 13(5):1054–1074, October 2009.

[2298] Kalyanmoy Deb and Himanshu Jain. Handling many-objective problems using an improved NSGA-II procedure. In 2012 IEEE Congress on Evolutionary Computation (CEC’2012), pages 1400–1407, Brisbane, Australia, June 10-15 2012. IEEE Press.

[2299] Kalyanmoy Deb and Himanshu Jain. An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints. IEEE Transactions on Evolutionary Computation, 18(4):577–601, August 2014.

[2300] Kalyanmoy Deb and Sachin Jain. Running Performance Metrics for Evolutionary Multi-Objective Optimization. In Lipo Wang, Kay Chen Tan, Takeshi Furuhashi, Jong-Hwan Kim, and Xin Yao, editors, Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning (SEAL’02), volume 1, pages 13–20, Orchid Country Club, Singapore, November 2002. Nanyang Technical University.

[2301] Kalyanmoy Deb and Sachin Jain. Evaluating Evolutionary Multi-Objective Optimization Algorithms using Running Performance Metrics. In Kay Chen Tan, Meng Hiot Lim, Xin Yao, and Lipo Wang, editors, Recent Advances in Simulated Evolution and Learning, pages 307–326. World Scientific, Singapore, 2004.

[2302] Kalyanmoy Deb and Sachin Kain. Multi-Speed Gearbox Design Using Multi-Objective Evolutionary Algorithms. Journal of Mechanical Design, 125(3):609–619, September 2003.

[2303] Kalyanmoy Deb, S. Karthik, and Tatsuya Okabe. Self-Adaptive Simulated Binary Crossover for Real-Parameter Optimization. In Dirk Thierens, editor, 2007 Genetic and Evolutionary Computation Conference (GECCO’2007), volume 2, pages 1187–1194, London, UK, July 2007. ACM Press.

[2304] Kalyanmoy Deb and Abhay Kumar. Light Beam Search Based Multiobjective Optimization using Evolutionary Algorithms. In 2007 IEEE Congress on Evolutionary Computation (CEC’2007), pages 2125–2132, Singapore, September 2007. IEEE Press.

[2305] Kalyanmoy Deb and Abhishek Kumar. Interactive Evolutionary Multi-Objective Optimization and Decision-Making using Reference Direction Method. In Dirk Thierens, editor, 2007 Genetic and Evolutionary Computation Conference (GECCO’2007), volume 1, pages 781–788, London, UK, July 2007. ACM Press.

[2306] Kalyanmoy Deb and Amarendra Kumar. Real-coded Genetic Algorithms with Simulated Binary Crossover: Studies on Multimodal and Multiobjective Problems. Complex Systems, 9:431–454, 1995.

[2307] Kalyanmoy Deb, Swanand Lele, and Rituparna Datta. A Hybrid Evolutionary Multi-objective and SQP Based Procedure for Constrained Optimization. In Lishan Kang, Yong Liu, and Sanyou Zeng, editors, Advances in Computation and Intelligence, Second International Symposium, ISICA 2007, pages 36–45, Wuhan, China, September 21-23 2007. Springer. Lecture Notes in Computer Science Vol. 4683.

[2308] Kalyanmoy Deb, Zhichao Lu, Chris B. McKesson, Cherie C. Trumbach, and Larry DeCan. Towards Optimal Ship Design and Valuable Knowledge Discovery Under Uncertain Conditions. In 2015 IEEE Congress on Evolutionary Computation (CEC’2015), pages 1815–1822, Sendai, Japan, 25-28 May 2015. IEEE Press. ISBN 978-1-4799-7492-4.

[2309] Kalyanmoy Deb and T. Meyarivan. Constrained Test Problems for Multi-Objective Evolutionary Optimization. KanGAL report 200005, Indian Institute of Technology, Kanpur, India, 2000.

[2310] Kalyanmoy Deb and Kaisa Miettinen. Nadir Point Estimation Using Evolutionary Approaches: Better Accuracy and Computational Speed Through Focused Search. In Matthias Ehrgott, Boris Naujoks, Theodor J. Stewart, and Jyrki Wallenius, editors, Multiple Criteria Decision Making for Sustainable Energy and Transportation Systems, pages 339–354. Springer, Lecture Notes in Economics and Mathematical Systems Vol. 634, Heidelberg, Germany, 2010.

[2311] Kalyanmoy Deb, Kaisa Miettinen, and Shamik Chaudhuri. Toward an Estimation of Nadir Objective Vector Using a Hybrid of Evolutionary and Local Search Approaches. IEEE Transactions On Evolutionary Computation, 14(6):821–841, December 2010.

[2312] Kalyanmoy Deb, Kaisa Miettinen, and Deepak Sharma. A Hybrid Integrated Multi-Objective Optimization Procedure for Estimating Nadir Point. In Matthias Ehrgott, Carlos M. Fonseca, Xavier Gandibleux, Jin-Kao Hao, and Marc Sevaux, editors, Evolutionary Multi-Criterion Optimization. 5th International Conference, EMO 2009, pages 569–583. Springer. Lecture Notes in Computer Science Vol. 5467, Nantes, France, April 2009.

[2313] Kalyanmoy Deb, Kishalay Mitra, Rinku Dewri, and Saptarshi Majumdar. Unveiling Optimal Operating Conditions for an Epoxy Polymerization Process Using Multi-objective Evolutionary Computation. In Kalyanmoy Deb et al., editor, Genetic and Evolutionary Computation–GECCO 2004. Proceedings of the Genetic and Evolutionary Computation Conference. Part II, pages 920– 931, Seattle, Washington, USA, June 2004. Springer-Verlag, Lecture Notes in Computer Science Vol. 3103.

[2314] Kalyanmoy Deb, Manikanth Mohan, and Shikhar Mishra. Towards a Quick Computation of Well-Spread Pareto-Optimal Solutions. In Carlos M. Fonseca, Peter J. Fleming, Eckart Zitzler, Kalyanmoy Deb, and Lothar Thiele, editors, Evolutionary Multi-Criterion Optimization. Second International Conference, EMO 2003, pages 222–236, Faro, Portugal, April 2003. Springer. Lecture Notes in Computer Science. Volume 2632.

[2315] Kalyanmoy Deb, Manikanth Mohan, and Shikhar Mishra. Evaluating the ϵ-Domination Based Multi-Objective Evolutionary Algorithm for a Quick Computation of Pareto-Optimal Solutions. Evolutionary Computation, 13(4):501–525, Winter 2005.

[2316] Kalyanmoy Deb and Pawan K.S. Nain. An Evolutionary Multi-objective Adaptive Meta-modeling Procedure Using Artificial Neural Networks. In Shengxiang Yang, Yew Soon Ong, and Yaochu Jin, editors, Evolutionary Computation in Dynamic and Uncertain Environments, pages 297–322. Springer, 2007. ISBN 978-3-540-49772-1.

[2317] Kalyanmoy Deb, Dhanesh Padmanabhan, Sulabh Gupta, and Abhishek Kumar Mall. Reliability-Based Multi-objective Optimization Using Evolutionary Algorithms. In Shigeru Obayashi, Kalyanmoy Deb, Carlo Poloni, Tomoyuki Hiroyasu, and Tadahiko Murata, editors, Evolutionary Multi-Criterion Optimization, 4th International Conference, EMO 2007, pages 66– 80, Matshushima, Japan, March 2007. Springer. Lecture Notes in Computer Science Vol. 4403.

[2318] Kalyanmoy Deb, A. Patrap, and S. Moitra. Mechanical Component Design for multi-objective using Elitist non-dominated sorting GA. KanGAL report 200002, Indian Institute of Technology, Kanpur, India, 2000.

[2319] Kalyanmoy Deb, Amrit Pratab, and Subrajyoti Moitra. Mechanical Component Design for Multiple Objectives Using Elitist Non-dominated Sorting GA. In Marc Schoenauer, Kalyanmoy Deb, Günter Rudolph, Xin Yao, Evelyne Lutton, Juan Julian Merelo, and Hans-Paul Schwefel, editors, Proceedings of the Parallel Problem Solving from Nature VI Conference, pages 859–868, Paris, France, 2000. Springer. Lecture Notes in Computer Science No. 1917.

[2320] Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and T. Meyarivan. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA–II. IEEE Transactions on Evolutionary Computation, 6(2):182–197, April 2002.

[2321] Kalyanmoy Deb, Amrit Pratap, and T. Meyarivan. Constrained Test Problems for Multi-objective Evolutionary Optimization. In Eckart Zitzler, Kalyanmoy Deb, Lothar Thiele, Carlos A. Coello Coello, and David Corne, editors, First International Conference on Evolutionary Multi-Criterion Optimization, pages 284–298. Springer-Verlag. Lecture Notes in Computer Science No. 1993, 2001.

[2322] Kalyanmoy Deb, Udaya Bhaskara N. Rao, and S. Karthik. Dynamic Multiobjective Optimization and Decision-Making Using Modified NSGA-II: A Case Study on Hydro-thermal Power Scheduling. In Shigeru Obayashi, Kalyanmoy Deb, Carlo Poloni, Tomoyuki Hiroyasu, and Tadahiko Murata, editors, Evolutionary Multi-Criterion Optimization, 4th International Conference, EMO 2007, pages 803–817, Matshushima, Japan, March 2007. Springer. Lecture Notes in Computer Science Vol. 4403.

[2323] Kalyanmoy Deb, Francisco Ruiz, Mariano Luque, Rahul Tewari, Jose M. Cabello, and Jose M. Cejudo. On the sizing of a solar thermal electricity plant for multiple objectives using evolutionary optimization. Applied Soft Computing, 12(10):3300–3311, October 2012.

[2324] Kalyanmoy Deb and Amit Saha. Finding Multiple Solutions for Multimodal Optimization Problems Using a Multi-Objective Evolutionary Approach. In Proceedings of the 12th annual conference on Genetic and Evolutionary Computation (GECCO’2010), pages 447–454, Portland, Oregon, USA, July 7–11 2010. ACM Press. ISBN 978-1-4503-0072-8.

[2325] Kalyanmoy Deb and Amit Saha. Multimodal Optimization Using a Bi-Objective Evolutionary Algorithm. Evolutionary Computation, 20(1):27–62, Spring 2012.

[2326] Kalyanmoy Deb and Dhish Kumar Saxena. Searching for Pareto-optimal solutions through dimensionality reduction for certain large-dimensional multiobjective optimization problems. In 2006 IEEE Congress on Evolutionary Computation (CEC’2006), pages 3353–3360, Vancouver, BC, Canada, July 2006. IEEE.

[2327] Kalyanmoy Deb and Karthik Sindhya. Deciphering Innovative Principles for Optimal Electric Brushless D.C. Permanent Magnet Motor Design. In 2008 Congress on Evolutionary Computation (CEC’2008), pages 2283–2290, Hong Kong, June 2008. IEEE Service Center.

[2328] Kalyanmoy Deb, Karthik Sindhya, and Jussi Hakanen. Multi-objective optimization. In Raghu Nandan Sengupta, Aparna Gupta, and Joydeep Dutta, editors, Decision Sciences. Theory and Practice, chapter 3, pages 145–183. CRC Press, Boca Raton, Florida, USA, 2017. ISBN 978-1-4665-6430-5.

[2329] Kalyanmoy Deb and Ankur Sinha. An Evolutionary Approach for Bilevel Multi-objective Problems. In Yong Shi, Shouyang Wang, Yi Peng, Jianping Li, and Yong Zeng, editors, Cutting-Edge Research Topics on Multiple Criteria Decision Making (MCDM’2009), pages 17–24. Springer, Communications in Computer and Information Science, Vol. 35, Heidelberg, Germany, 2009.

[2330] Kalyanmoy Deb and Ankur Sinha. Constructing Test Problems for Bilevel Evolutionary Multi-Objective Optimization. In 2009 IEEE Congress on Evolutionary Computation (CEC’2009), pages 1153–1160, Trondheim, Norway, May 2009. IEEE Press.

[2331] Kalyanmoy Deb and Ankur Sinha. Solving Bilevel Multi-Objective Optimization Problems Using Evolutionary Algorithms. In Matthias Ehrgott, Carlos M. Fonseca, Xavier Gandibleux, Jin-Kao Hao, and Marc Sevaux, editors, Evolutionary Multi-Criterion Optimization. 5th International Conference, EMO 2009, pages 110–124. Springer. Lecture Notes in Computer Science Vol. 5467, Nantes, France, April 2009.

[2332] Kalyanmoy Deb and Ankur Sinha. An Efficient and Accurate Solution Methodology for Bilevel Multi-Objective Programming Problems Using a Hybrid Evolutionary-Local-Search Algorithm. Evolutionary Computation, 18(3):403–449, Fall 2010.

[2333] Kalyanmoy Deb, Ankur Sinha, Pekka J. Korhonen, and Jyrki Wallenius. An Interactive Evolutionary Multiobjective Optimization Method Based on Progressively Approximated Value Functions. IEEE Transactions on Evolutionary Computation, 14(5):723–739, October 2010.

[2334] Kalyanmoy Deb, Ankur Sinha, and Saku Kukkonen. Multi-Objective Test Problems, Linkages, and Evolutionary Methodologies. In Maarten Keijzer et al., editor, 2006 Genetic and Evolutionary Computation Conference (GECCO’2006), volume 2, pages 1141–1148, Seattle, Washington, USA, July 2006. ACM Press. ISBN 1-59593-186-4.

[2335] Kalyanmoy Deb and Aravind Srinivasan. Innovization: Innovating Design Principles Through Optimization. In Maarten Keijzer et al., editor, 2006 Genetic and Evolutionary Computation Conference (GECCO’2006), volume 2, pages 1629–1636, Seattle, Washington, USA, July 2006. ACM Press. ISBN 1-59593-186-4.

[2336] Kalyanmoy Deb and Aravind Srinivasan. Innovization: Discovery of Innovative Design Principles Through Multiobjective Evolutionary Optimization. In Joshua Knowles, David Corne, and Kalyanmoy Deb, editors, Multi-Objective Problem Solving from Nature: From Concepts to Applications, pages 243– 262. Springer, Berlin, 2008. ISBN 978-3-540-72963-1.

[2337] Kalyanmoy Deb, Ralph E. Steuer, Rajat Tewari, and Rahul Tewari. Biobjective Portfolio Optimization Using a Customized Hybrid NSGA-II Procedure. In Ricardo H.C. Takahashi, Kalyanmoy Deb, Elizabeth F. Wanner, and Salvatore Grecco, editors, Evolutionary Multi-Criterion Optimization, 6th International Conference, EMO 2011, pages 358–373, Ouro Preto, Brazil, April 2011. Springer. Lecture Notes in Computer Science Vol. 6576.

[2338] Kalyanmoy Deb and J. Sundar. Reference Point Based Multi-Objective Optimization Using Evolutionary Algorithms. In Maarten Keijzer et al., editor, 2006 Genetic and Evolutionary Computation Conference (GECCO’2006), volume 1, pages 635–642, Seattle, Washington, USA, July 2006. ACM Press. ISBN 1-59593-186-4.

[2339] Kalyanmoy Deb, J. Sundar, Udaya Bhaskara Rao N., and Shamik Chaudhuri. Reference Point Based Multi-Objective Optimization Using Evolutionary Algorithms. International Journal of Computational Intelligence Research, 2(3):273–286, 2006.

[2340] Kalyanmoy Deb, Rahul Tewari, Mayur Dixit, and Joydeep Dutta. Finding Trade-off Solutions Close to KKT Points Using Evolutionary Multi-Objective Optimization. In 2007 IEEE Congress on Evolutionary Computation (CEC’2007), pages 2109–2116, Singapore, September 2007. IEEE Press.

[2341] Kalyanmoy Deb, Lothar Thiele, Marco Laumanns, and Eckart Zitzler. Scalable Test Problems for Evolutionary Multi-Objective Optimization. Technical Report 112, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Zurich, Switzerland, 2001.

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[3198] Carlos R. Garcia-Alonso, Luis Salvador-Carulla, Miguel A. Negrin-Hernandez, and Berta Moreno-Küstner. Development of a New Spatial Analysis Tool in Mental Health: Identification of Highly Autocorrelated Areas (Hot-Spots) of Schizophrenia Using a Multiobjective Evolutionary Algorithm Model (MOEA/HS). Epidemiologia e Psichiatria Sociale-an International Journal for Epidemiology and Psychiatric Sciences, 19(4):302–313, October-December 2010.

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[4238] Hisao Ishibuchi, Yasuhiro Hitotsuyanagi, and Yusuke Nojima. An Empirical Study on the Specification of the Local Search Application Probability in Multiobjective Memetic Algorithms. In 2007 IEEE Congress on Evolutionary Computation (CEC’2007), pages 2788–2795, Singapore, September 2007. IEEE Press.

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[4261] Hisao Ishibuchi and Tadahiko Murata. Multi-Objective Genetic Local Search for Minimizing the Number of Fuzzy Rules for Pattern Classification Problems. In David B. Fogel, editor, Proceedings of the 1998 IEEE International Conference on Evolutionary Computation, pages 1100–1105, Piscataway, New Jersey, May 1998. IEEE.

[4262] Hisao Ishibuchi, Tadahiko Murata, and Mitsuo Gen. Performance Evaluation of Fuzzy Rule-Based Classification Systems Obtained by Multi-Objective Genetic Algorithms. Computers & Industrial Engineering, 35(3–4):575–578, December 1998.

[4263] Hisao Ishibuchi, Tadahiko Murata, and I. B. Tursken. Selecting Linguistic Classification Rules by Two-Objective Genetic Algorithms. In Proceedings of the 1995 IEEE International Conference on Systems, Man and Cybernetics, pages 1410–1415, Vancouver, Canada, October 1995. IEEE.

[4264] Hisao Ishibuchi, Tadahiko Murata, and I. B. Tursken. Single-Objective and Two-Objective Genetic Algorithms for Selecting Linguistic Rules for Pattern Classification Problems. Fuzzy Sets and Systems, 89(2):135–150, July 1997.

[4265] Hisao Ishibuchi and Tomoharu Nakashima. Evolution of Reference Sets in Nearest Neighbor Classification. In B. McKay, X. Yao, C. S. Newton, J.-H. Kim, and T. Furuhashi, editors, Simulated Evolution and Learning. Second Asia-Pacific Conference on Simulated Evolution and Learning, SEAL’98, Canberra, Australia, 1998. Lecture Notes in Computer Science 1585, Springer-Verlag.

[4266] Hisao Ishibuchi and Tomoharu Nakashima. Linguistic Rule Extraction by Genetics-Based Machine Learning. In Darrell Whitley, David Goldberg, Erick Cantú-Paz, Lee Spector, Ian Parmee, and Hans-Georg Beyer, editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2000), pages 195–202, San Francisco, California, 2000. Morgan Kaufmann.

[4267] Hisao Ishibuchi and Tomoharu Nakashima. Multi-Objective Pattern and Feature Selection by a Genetic Algorithm. In Darrell Whitley, David Goldberg, Erick Cantú-Paz, Lee Spector, Ian Parmee, and Hans-Georg Beyer, editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2000), pages 1069–1076, San Francisco, California, 2000. Morgan Kaufmann.

[4268] Hisao Ishibuchi and Tomoharu Nakashima. Three-Objective Optimization in Linguistic Function Approximation. In Proceedings of the Congress on Evolutionary Computation 2001 (CEC’2001), volume 1, pages 340–347, Piscataway, New Jersey, May 2001. IEEE Service Center.

[4269] Hisao Ishibuchi, Tomoharu Nakashima, and Tetsuya Kuroda. A Hybrid Fuzzy GBML Algorithm for Designing Compact Fuzzy Rule-Based Classification Systems. In Proceedings of 9th IEEE International Conference on Fuzzy Systems, pages 706–711, San Antonio, Texas, May 7–10 2000.

[4270] Hisao Ishibuchi, Tomoharu Nakashima, and Tetsuya Kuroda. Minimizing the Number of Fuzzy Rules by Fuzzy Genetics-Based Machine Learning for Pattern Classification Problems. In Proceedings of the 8th Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pages 96–103, Madrid, Spain, July 3–7 2000.

[4271] Hisao Ishibuchi, Tomoharu Nakashima, and Tadahiko Murata. Threeobjective genetics-based machine learning for linguistic rule extraction. Information Sciences, 136(1–4):109–133, August 2001.

[4272] Hisao Ishibuchi, Tomoharu Nakashima, and Manabu Nii. Classification and Modeling with Linguistic Information Granules. Springer, Berlin/Heidelberg/New York, 2005. ISBN 3-540-20767-8.

[4273] Hisao Ishibuchi, Yusuke Nakashima, and Yusuke Nojima. Search Ability of Evolutionary Multiobjective Optimization Algorithms for Multiobjective Fuzzy Genetics-Based Machine Learning. In 2009 IEEE International Conference on Fuzzy Systems, pages 1724–1729, Jeju Isl, South Korea, August 20-24 2009. IEEE Press. ISBN 978-1-4244-3596-8.

[4274] Hisao Ishibuchi, Yusuke Nakashima, and Yusuke Nojima. Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning. Soft Computing, 15(12):2415– 2434, December 2011.

[4275] Hisao Ishibuchi and Satoshi Namba. Evolutionary Multiobjective Knowledge Extraction for High-Dimensional Pattern Classification Problems. In Xin Yao et al., editor, Parallel Problem Solving from Nature - PPSN VIII, pages 1123– 1132, Birmingham, UK, September 2004. Springer-Verlag. Lecture Notes in Computer Science Vol. 3242.

[4276] Hisao Ishibuchi and Kaname Narukawa. Performance evaluation of simple multiobjective genetic local search algorithms on multiobjective 0/1 knapsack problems. In 2004 Congress on Evolutionary Computation (CEC’2004), volume 1, pages 441–448, Portland, Oregon, USA, June 2004. IEEE Service Center.

[4277] Hisao Ishibuchi and Kaname Narukawa. Some Issues on the Implementation of Local Search in Evolutionary Multiobjective Optimization. In Kalyanmoy Deb et al., editor, Genetic and Evolutionary Computation–GECCO 2004. Proceedings of the Genetic and Evolutionary Computation Conference. Part I, pages 1246–1258, Seattle, Washington, USA, June 2004. Springer-Verlag, Lecture Notes in Computer Science Vol. 3102.

[4278] Hisao Ishibuchi and Kaname Narukawa. Comparison of Evolutionary Multiobjective Optimization with Reference Solution-Based Single-Objective Approach. In Hans-Georg Beyer et al., editor, 2005 Genetic and Evolutionary Computation Conference (GECCO’2005), volume 1, pages 787–794, New York, USA, June 2005. ACM Press.

[4279] Hisao Ishibuchi and Kaname Narukawa. Recombination of Similar Parents in EMO Algorithms. In Carlos A. Coello Coello, Arturo Hernández Aguirre, and Eckart Zitzler, editors, Evolutionary Multi-Criterion Optimization. Third International Conference, EMO 2005, pages 265–279, Guanajuato, México, March 2005. Springer. Lecture Notes in Computer Science Vol. 3410.

[4280] Hisao Ishibuchi and Kaname Narukawa. Spatial Implementation of Evolutionary Multiobjective Algorithms with Partial Lamarckian Repair for Multiobjective Knapsack Problems. In Nadia Nedjah, Luiza M. Mourelle, Marley M.B.R. Vellasco, Ajith Abraham, and Mario Köppen, editors, Fifth International Conference on Hybrid Intelligent Systems (HIS’05), pages 265–270, Los Alamitos, California, USA, November 2005. IEEE Computer Society.

[4281] Hisao Ishibuchi, Kaname Narukawa, and Yusuke Nojima. An Empirical Study on the Handling of Overlapping Solutions in Evolutionary Multiobjective Optimization. In Hans-Georg Beyer et al., editor, 2005 Genetic and Evolutionary Computation Conference (GECCO’2005), volume 1, pages 817– 824, New York, USA, June 2005. ACM Press.

[4282] Hisao Ishibuchi, Kaname Narukawa, Noritaka Tsukamoto, and Yusuke Nojima. An empirical study on similarity-based mating for evolutionary multiobjective combinatorial optimization. European Journal of Operational Research, 188(1):57–75, July 1 2008.

[4283] Hisao Ishibuchi and Yusuke Nojima. Performance Evaluation of Evolutionary Multiobjective Approaches to the Design of Fuzzy Rule-Based Ensemble Classifiers. In Nadia Nedjah, Luiza M. Mourelle, Marley M.B.R. Vellasco, Ajith Abraham, and Mario Köppen, editors, Fifth International Conference on Hybrid Intelligent Systems (HIS’05), pages 271–276, Los Alamitos, California, USA, November 2005. IEEE Computer Society.

[4284] Hisao Ishibuchi and Yusuke Nojima. Fuzzy Ensemble Design through Multi-Objective Fuzzy Rule Selection. In Yaochu Jin, editor, Multi-Objective Machine Learning, pages 507–530. Springer. Studies in Computational Intelligence, Volume 16, Berlin, 2006.

[4285] Hisao Ishibuchi and Yusuke Nojima. Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning. International Journal of Approximate Reasoning, 44(1):4–31, January 2007.

[4286] Hisao Ishibuchi and Yusuke Nojima. Optimization of Scalarizing Functions Through Evolutionary Multiobjective Optimization. In Shigeru Obayashi, Kalyanmoy Deb, Carlo Poloni, Tomoyuki Hiroyasu, and Tadahiko Murata, editors, Evolutionary Multi-Criterion Optimization, 4th International Conference, EMO 2007, pages 51–65, Matshushima, Japan, March 2007. Springer. Lecture Notes in Computer Science Vol. 4403.

[4287] Hisao Ishibuchi and Yusuke Nojima. Multiobjective Genetic Fuzzy Systems. In Christine L. Mumford and Lakhmi C. Jain, editors, Computational Intelligence, Collaboration, Fusion and Emergence, pages 131–173. Springer. Intelligent Systems Reference Library Vol. 1, Berlin, Germany, 2009. ISBN 978-3-642-01798-8.

[4288] Hisao Ishibuchi, Yusuke Nojima, and Tsutomu Doi. Comparison between Single-Objective and Multi-Objective Genetic Algorithms: Performance Comparison and Performance Measures. In 2006 IEEE Congress on Evolutionary Computation (CEC’2006), pages 3959–3966, Vancouver, BC, Canada, July 2006. IEEE.

[4289] Hisao Ishibuchi, Yusuke Nojima, and Isao Kuwajima. Finding simple fuzzy classification systems with high interpretability through multiobjective rule selection. In Knowledge-Based Intelligent Information and Engineering Systems, Pt 2, Proceedings, pages 86–93. Springer, Lecture Notes in Artificial Intelligence Vol. 4252, 2006.

[4290] Hisao Ishibuchi, Yusuke Nojima, and Isao Kuwajima. Multiobjective Genetic Rule Selection as a Data Mining Postprocessing Procedure. In Maarten Keijzer et al., editor, 2006 Genetic and Evolutionary Computation Conference (GECCO’2006), volume 2, pages 1591–1592, Seattle, Washington, USA, July 2006. ACM Press. ISBN 1-59593-186-4.

[4291] Hisao Ishibuchi, Yusuke Nojima, and Isao Kuwajima. Evolutionary Multiobjective Design of Fuzzy Rule-Based Classifiers. In John Fulcher and Lakhmi C. Jain, editors, Computational Intelligence: A Compendium, pages 641–685. Springer. Studies in Computational Intelligence Vol. 115, Berlin, Germany, 2008. ISBN 978-3-540-78293-3.

[4292] Hisao Ishibuchi, Yusuke Nojima, Kaname Narukawa, and Tsutomo Doi. Incorporation of Decision Maker’s Preferences into Evolutionary Multiobjective Optimization Algorithms. In Maarten Keijzer et al., editor, 2006 Genetic and Evolutionary Computation Conference (GECCO’2006), volume 1, pages 741–742, Seattle, Washington, USA, July 2006. ACM Press. ISBN 1-59593-186-4.

[4293] Hisao Ishibuchi, Yusuke Nojima, Noritaka Tsukamoto, and Ken Ohara. Effects of the Use of Non-Geometric Binary Crossover on Evolutionary Multiobjective Optimization. In Dirk Thierens, editor, 2007 Genetic and Evolutionary Computation Conference (GECCO’2007), volume 1, pages 829–836, London, UK, July 2007. ACM Press.

[4294] Hisao Ishibuchi, Yuji Sakane, Noritaka Tsukamoto, and Yusuke Nojima. Adaptation of Scalarizing Funtions in MOEA/D: An Adaptive Scalarizing Funtion-Based Multiobjective Evolutionary Algorithm. In Matthias Ehrgott, Carlos M. Fonseca, Xavier Gandibleux, Jin-Kao Hao, and Marc Sevaux, editors, Evolutionary Multi-Criterion Optimization. 5th International Conference, EMO 2009, pages 438–452. Springer. Lecture Notes in Computer Science Vol. 5467, Nantes, France, April 2009.

[4295] Hisao Ishibuchi, Yuji Sakane, Noritaka Tsukamoto, and Yusuke Nojima. Effects of Using Two Neighborhood Structures on the Performance of Cellular Evolutionary Algorithms for Many-Objective Optimization. In 2009 IEEE Congress on Evolutionary Computation (CEC’2009), pages 2508– 2515, Trondheim, Norway, May 2009. IEEE Press.

[4296] Hisao Ishibuchi, Yuji Sakane, Noritaka Tsukamoto, and Yusuke Nojima. Evolutionary Many-Objective Optimization by NSGA-II and MOEA/D with Large Populations. In IEEE International Conference on Systems, Man and Cybernetics (SMC’2009), pages 1758–1763, Texas, USA, October 2009. IEEE Press. ISBN 978-1-4244-2794-9.

[4297] Hisao Ishibuchi, Yuji Sakane, Noritaka Tsukamoto, and Yusuke Nojima. Selecting a Small Number of Representative Non-Dominated Solutions by a Hypervolume-Based Solution Selection Approach. In 2009 IEEE International Conference on Fuzzy Systems, pages 1609–1614, Jeju Isl, South Korea, August 20-24 2009. IEEE Press. ISBN 978-1-4244-3596-8.

[4298] Hisao Ishibuchi, Yuji Sakane, Noritaka Tsukamoto, and Yusuke Nojima. Single-objective and multi-objective formulations of solution selection for hypervolume maximization. In 2009 Genetic and Evolutionary Computation Conference (GECCO’2009), pages 1831–1832, Montreal, Canada, July 8–12 2009. ACM Press. ISBN 978-1-60558-325-9.

[4299] Hisao Ishibuchi, Yuji Sakane, Noritaka Tsukamoto, and Yusuke Nojima. Simultaneous Use of Different Scalarizing Functions in MOEA/D. In Proceedings of the 12th annual conference on Genetic and Evolutionary Computation (GECCO’2010), pages 519–526, Portland, Oregon, USA, July 7–11 2010. ACM Press. ISBN 978-1-4503-0072-8.

[4300] Hisao Ishibuchi, Yuji Sakane, Noritaka Tsukamoto, and Yusuke Nojima. Implementation of cellular genetic algorithms with two neighborhood structures for single-objective and multi-objective optimization. Soft Computing, 15(9):1749–1767, September 2011.

[4301] Hisao Ishibuchi and Youhei Shibata. An Empirical Study on the Effect of Mating Restriction on the Search Ability of EMO Algorithms. In Carlos M. Fonseca, Peter J. Fleming, Eckart Zitzler, Kalyanmoy Deb, and Lothar Thiele, editors, Evolutionary Multi-Criterion Optimization. Second International Conference, EMO 2003, pages 433–477, Faro, Portugal, April 2003. Springer. Lecture Notes in Computer Science. Volume 2632.

[4302] Hisao Ishibuchi and Youhei Shibata. A Similarity-Based Mating Scheme for Evolutionary Multiobjective Optimization. In Erick Cantú-Paz et al., editor, Genetic and Evolutionary Computation—GECCO 2003. Proceedings, Part I, pages 1065–1076. Springer. Lecture Notes in Computer Science Vol. 2723, July 2003.

[4303] Hisao Ishibuchi and Youhei Shibata. Mating Scheme for Controlling the Diversity-Convergence Balance for Multiobjective Optimization. In Kalyanmoy Deb et al., editor, Genetic and Evolutionary Computation–GECCO 2004. Proceedings of the Genetic and Evolutionary Computation Conference. Part I, pages 1259–1271, Seattle, Washington, USA, June 2004. Springer-Verlag, Lecture Notes in Computer Science Vol. 3102.

[4304] Hisao Ishibuchi and Youhei Shibata. Single-Objective and Multi-Objective Evolutionary Flowshop Scheduling. In Carlos A. Coello Coello and Gary B. Lamont, editors, Applications of Multi-Objective Evolutionary Algorithms, pages 529–554. World Scientific, Singapore, 2004.

[4305] Hisao Ishibuchi, Yuki Tanigaki, Naoya Akedo, and Yusuke Nojima. How to Strike a Balance Between Local Search and Global Search in Multiobjective Memetic Algorithms for Multiobjective 0/1 Kanapsack Problems. In 2013 IEEE Congress on Evolutionary Computation (CEC’2013), pages 1643–1650, Cancún, México, 20-23 June 2013. IEEE Press. ISBN 978-1-4799-0454-9.

[4306] Hisao Ishibuchi, Yuki Tanigaki, Hiroyuki Masuda, and Yusuke Nojima. Distance-Based Analysis of Crossover Operators for Many-Objective Knapsack Problems. In Thomas Bartz-Beielstein, Jürgen Branke, Bogdan Filipič, and Jim Smith, editors, Parallel Problem Solving from Nature - PPSN XIII, 13th International Conference, pages 600–610. Springer. Lecture Notes in Computer Science Vol. 8672, Ljubljana, Slovenia, September 13-17 2014.

[4307] Hisao Ishibuchi, Noritaka Tsukamoto, Yasuhiro Hitotsuyanagi, and Yusuke Nojima. Effectiveness of Scalability Improvement Attempts on the Performance of NSGA-II for Many-Objective Problems. In 2008 Genetic and Evolutionary Computation Conference (GECCO’2008), pages 649–656, Atlanta, USA, July 2008. ACM Press. ISBN 978-1-60558-131-6.

[4308] Hisao Ishibuchi, Noritaka Tsukamoto, and Yusuke Nojima. Iterative Approach to Indicator-Based Multiobjective Optimization. In 2007 IEEE Congress on Evolutionary Computation (CEC’2007), pages 3967–3974, Singapore, September 2007. IEEE Press.

[4309] Hisao Ishibuchi, Noritaka Tsukamoto, and Yusuke Nojima. Evolutionary many-objective optimization. In 2008 3rd International Workshop on Genetic and Evolving Fuzzy Systems, pages 45–50, Witten Bommerhalz, Germany, March 04-07 2008. IEEE. ISBN 978-1-4244-1612-7.

[4310] Hisao Ishibuchi, Noritaka Tsukamoto, and Yusuke Nojima. Evolutionary many-objective optimization: A short review. In 2008 Congress on Evolutionary Computation (CEC’2008), pages 2424–2431, Hong Kong, June 2008. IEEE Service Center.

[4311] Hisao Ishibuchi, Noritaka Tsukamoto, and Yusuke Nojima. Empirical Analysis of Using Weighted Sum Fitness Functions in NSGA-II for Many-Objective 0/1 Knapsack Problems. In UKSIM 2009: Eleventh International Conference on Computer Modelling and Simulation, pages 71–76, Cambridge, England, March 25-27 2009. IEEE Computer Society Press. ISBN 978-1-4503-1963-8.

[4312] Hisao Ishibuchi, Noritaka Tsukamoto, and Yusuke Nojima. Diversity Improvement by Non-Geometric Binary Crossover in Evolutionary Multiobjective Optimization. IEEE Transactions on Evolutionary Computation, 14(6):985–998, December 2010.

[4313] Hisao Ishibuchi, Noritaka Tsukamoto, Yuji Sakane, and Yusuke Nojima. Hypervolume Approximation Using Achievement Scalarizing Functions for Evolutionary Many-Objective Optimization. In 2009 IEEE Congress on Evolutionary Computation (CEC’2009), pages 530–537, Trondheim, Norway, May 2009. IEEE Press.

[4314] Hisao Ishibuchi, Noritaka Tsukamoto, Yuji Sakane, and Yusuke Nojima. Indicator-Based Evolutionary Algorithm with Hypervolume Approximation by Achievement Scalarizing Functions. In Proceedings of the 12th annual conference on Genetic and Evolutionary Computation (GECCO’2010), pages 527–534, Portland, Oregon, USA, July 7–11 2010. ACM Press. ISBN 978-1-4503-0072-8.

[4315] Hisao Ishibuchi and Takashi Yamamoto. Fuzzy Rule Selection by Data Mining Criteria and Genetic Algorithms. In W.B. Langdon, E. Cantú-Paz, K. Mathias, R. Roy, D. Davis, R. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M.A. Potter, A.C. Schultz, J.F. Miller, E. Burke, and N. Jonoska, editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2002), pages 399–406, San Francisco, California, July 2002. Morgan Kaufmann Publishers.

[4316] Hisao Ishibuchi and Takashi Yamamoto. Effects of Three-Objective Genetic Rule Selection on the Generalization Ability of Fuzzy Rule-Based Systems. In Carlos M. Fonseca, Peter J. Fleming, Eckart Zitzler, Kalyanmoy Deb, and Lothar Thiele, editors, Evolutionary Multi-Criterion Optimization. Second International Conference, EMO 2003, pages 608–622, Faro, Portugal, April 2003. Springer. Lecture Notes in Computer Science. Volume 2632.

[4317] Hisao Ishibuchi and Takashi Yamamoto. Evolutionary Multiobjective Optimization for Generating an Ensemble of Fuzzy Rule-Based Classifiers. In Erick Cantú-Paz et al., editor, Genetic and Evolutionary Computation—GECCO 2003. Proceedings, Part I, pages 1077–1088. Springer. Lecture Notes in Computer Science Vol. 2723, July 2003.

[4318] Hisao Ishibuchi and Takashi Yamamoto. Fuzzy Rule Selection by Multi-Objective Genetic Local Search Algorithms and Rule Evaluation Measures in Data Mining. Fuzzy Sets and Systems, 141(1):59–88, January 2004.

[4319] Hisao Ishibuchi, Masakazu Yamane, Naoya Akedo, and Yusuke Nojima. Many-Objective and Many-Variable Test Problems for Visual Examination of Multiobjective Search. In 2013 IEEE Congress on Evolutionary Computation (CEC’2013), pages 1491–1498, Cancún, México, 20-23 June 2013. IEEE Press. ISBN 978-1-4799-0454-9.

[4320] Hisao Ishibuchi, Masakazu Yamane, and Yusuke Nojima. Effects of Discrete Objective Functions with Different Granularities on the Search Behavior of EMO Algorithms. In 2012 Genetic and Evolutionary Computation Conference (GECCO’2012), pages 481–488, Philadelphia, USA, July 2012. ACM Press. ISBN: 978-1-4503-1177-9.

[4321] Hisao Ishibuchi, Masakazu Yamane, and Yusuke Nojima. Difficulty in Evolutionary Multiobjective Optimization of Discrete Objective Functions with Different Granularities. In Robin C. Purshouse, Peter J. Fleming, Carlos M. Fonseca, Salvatore Greco, and Jane Shaw, editors, Evolutionary Multi-Criterion Optimization, 7th International Conference, EMO 2013, pages 230– 245. Springer. Lecture Notes in Computer Science Vol. 7811, Sheffield, UK, March 19-22 2013.

[4322] Hisao Ishibuchi, Masakazu Yamane, and Yusuke Nojima. Effects of Duplicated Objectives in Many-Objective Optimization Problems on the Search Behavior of Hypervolume-Based Evolutionary Algorithms. In Proceedings of the 2013 IEEE Symposium on Computational Intelligence in Multicriteria Decision Making (MCDM’2013), pages 25–32, Singapore, April 16–19 2013. IEEE Press.

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[4324] Hisao Ishibuchi and Tadashi Yoshida. Implementation of Local Search in Hybrid Multi-Objective Genetic Algorithms: A Case Study on Flowshop Scheduling. In Lipo Wang, Kay Chen Tan, Takeshi Furuhashi, Jong-Hwan Kim, and Xin Yao, editors, Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning (SEAL’02), volume 1, pages 193–197, Orchid Country Club, Singapore, November 2002. Nanyang Technical University.

[4325] Hisao Ishibuchi, Tadashi Yoshida, and Tadahiko Murata. Balance between Genetic Search and Local Search in Hybrid Evolutionary Multi-Criterion Optimization Algorithms. In W.B. Langdon, E. Cantú-Paz, K. Mathias, R. Roy, D. Davis, R. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M.A. Potter, A.C. Schultz, J.F. Miller, E. Burke, and N. Jonoska, editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2002), pages 1301–1308, San Francisco, California, July 2002. Morgan Kaufmann Publishers.

[4326] Hisao Ishibuchi, Tadashi Yoshida, and Tadahiko Murata. Selection of Initial Solutions for Local Search in Multiobjective Genetic Local Search. In Congress on Evolutionary Computation (CEC’2002), volume 1, pages 950– 955, Piscataway, New Jersey, May 2002. IEEE Service Center.

[4327] Hisao Ishibuchi, Tadashi Yoshida, and Tadahiko Murata. Balance Between Genetic Search and Local Search in Memetic Algorithms for Multiobjective Permutation Flowshop Scheduling. IEEE Transactions on Evolutionary Computation, 7(2):204–223, April 2003.

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[4329] Celso Y. Ishida, Aurora Pozo, Elizabeth Goldbarg, and Marco Goldbarg. Multiobjective Optimization and Rule Learning: Subselection Algorithm or Meta-heuristic Algorithm? In Nadia Nedjah, Luiza de Macedo Mourelle, and Janusz Kacprzyk, editors, Innovative Applications in Data Mining, pages 47–70. Springer. Studies in Computational Intelligence Vol. 169, Berlin, Germany, 2009. ISBN 978-3-540-88044-8.

[4330] Toshimasa Ishida, Ikuya Nishimura, Hiromasa Tanino, Masaru Higa, Hiroshi Ito, and Yoshinori Mitamura. Use of a Genetic Algorithm for Multiobjective Design Optimization of the Femoral Stem of a Cemented Total Hip Arthroplasty. Artificial Organs, 35(4):404–410, April 2011.

[4331] Yukari Ishida, Hirotaka Nosato, Eiichi Takahashi, Masahiro Murakawa, Isamu Kajitani, Tatsumi Furuya, and Tetsuya Higuchi. Proposal for LDPC Code Design System Using Multi-Objective Optimization and FPGA-Based Emulation. In Gregory S. Hornby, Lukas Sekanina, and Pauline C. Haddow, editors, Evolvable Systems: From Biology to Hardware. 8th International Conference (ICES’2008), pages 237–248, Prague, Czech Republic, September 21-24 2008. Springer. Lecture Notes in Computer Science, Vol. 5216.

[4332] Hisao Isibuchi, Naoya Akedo, Hiroyuki Ohyanagi, Yasuhiro Hitotsuyanagi, and Yusuke Nojima. Many-Objective Test Problems with Multiple Pareto Optimal Regions in a Decision Space. In 2011 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM’2011), pages 113–120, Paris, France, April 11–15 2011. IEEE Press. ISBN 978-1-61284-067-3.

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[4925] Joshua T. Knight, Frank T. Zahradka, David J. Singer, and Matthew D. Collette. Multiobjective Particle Swarm Optimization of a Planing Craft with Uncertainty. Journal of Ship Production and Design, 30(4):194–200, November 2014.

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[4928] Joshua Knowles. ParEGO: A Hybrid Algorithm with On-line Landscape Approximation for Expensive Multiobjective Optimization Problems. Technical Report TR-COMPSYSBIO-2004-01, University of Manchester, September 2004.

[4929] Joshua Knowles. A summary-attainment-surface plotting method for visualizing the performance of stochastic multiobjective optimizers. In Fifth International Conference on Intelligent Systems Design and Applications (ISDA’2005), pages 552–557. IEEE, 2005.

[4930] Joshua Knowles. ParEGO: A Hybrid Algorithm With On-Line Landscape Approximation for Expensive Multiobjective Optimization Problems. IEEE Transactions on Evolutionary Computation, 10(1):50–66, February 2006.

[4931] Joshua Knowles. Closed-Loop Evolutionary Multiobjective Optimization. IEEE Computational Intelligence Magazine, 4(3):77–91, August 2009.

[4932] Joshua Knowles and David Corne. M-PAES: A Memetic Algorithm for Multiobjective Optimization. In 2000 IEEE Congress on Evolutionary Computation, volume 1, pages 325–332, Piscataway, New Jersey, July 2000. IEEE Service Center.

[4933] Joshua Knowles and David Corne. On Metrics for Comparing Nondominated Sets. In Congress on Evolutionary Computation (CEC’2002), volume 1, pages 711–716, Piscataway, New Jersey, May 2002. IEEE Service Center.

[4934] Joshua Knowles and David Corne. Towards Landscape Analyses to Inform the Design of Hybrid Local Search for the Multiobjective Quadratic Assignment Problem. In A. Abraham, J. Ruiz del Solar, and M. Köppen, editors, Soft Computing Systems: Design, Management and Applications, pages 271–279, Amsterdam, 2002. IOS Press. ISBN 1-58603-297-6.

[4935] Joshua Knowles and David Corne. Instance Generators and Test Suites for the Multiobjective Quadratic Assignment Problem. In Carlos M. Fonseca, Peter J. Fleming, Eckart Zitzler, Kalyanmoy Deb, and Lothar Thiele, editors, Evolutionary Multi-Criterion Optimization. Second International Conference, EMO 2003, pages 295–310, Faro, Portugal, April 2003. Springer. Lecture Notes in Computer Science. Volume 2632.

[4936] Joshua Knowles and David Corne. Properties of an Adaptive Archiving Algorithm for Storing Nondominated Vectors. IEEE Transactions on Evolutionary Computation, 7(2):100–116, April 2003.

[4937] Joshua Knowles and David Corne. Bounded Pareto Archiving: Theory and Practice. In Xavier Gandibleux, Marc Sevaux, Kenneth Sörensen, and Vincent T’kindt, editors, Metaheuristics for Multiobjective Optimisation, pages 39– 64, Berlin, 2004. Springer. Lecture Notes in Economics and Mathematical Systems Vol. 535.

[4938] Joshua Knowles and David Corne. Memetic Algorithms for Multiobjective Optimization: Issues, Methods and Prospects. In William E. Hart, N. Krasnogor, and J.E. Smith, editors, Recent Advances in Memetic Algorithms, pages 313–352. Springer. Studies in Fuzziness and Soft Computing, Vol. 166, 2005.

[4939] Joshua Knowles and David Corne. Quantifying the Effects of Objective Space Dimension in Evolutionary Multiobjective Optimization. In Shigeru Obayashi, Kalyanmoy Deb, Carlo Poloni, Tomoyuki Hiroyasu, and Tadahiko Murata, editors, Evolutionary Multi-Criterion Optimization, 4th International Conference, EMO 2007, pages 757–771, Matshushima, Japan, March 2007. Springer. Lecture Notes in Computer Science Vol. 4403.

[4940] Joshua Knowles, David Corne, and Kalyanmoy Deb. Introduction: Problem Solving, EC and EMO. In Joshua Knowles, David Corne, and Kalyanmoy Deb, editors, Multi-Objective Problem Solving from Nature: From Concepts to Applications, pages 1–28. Springer, Berlin, 2008. ISBN 978-3-540-72963-1.

[4941] Joshua Knowles, David Corne, and Kalyanmoy Deb, editors. Multiobjective Problem Solving from Nature. From Concepts to Applications. Springer, Berlin, 2008. ISBN 978-3-540-72963-1.

[4942] Joshua Knowles, David Corne, and Alan Reynolds. Noisy Multiobjective Optimization on a Budget of 250 Evaluations. In Matthias Ehrgott, Carlos M. Fonseca, Xavier Gandibleux, Jin-Kao Hao, and Marc Sevaux, editors, Evolutionary Multi-Criterion Optimization. 5th International Conference, EMO 2009, pages 36–50. Springer. Lecture Notes in Computer Science Vol. 5467, Nantes, France, April 2009.

[4943] Joshua Knowles and Evan J. Hughes. Multiobjective Optimization on a Budget of 250 Evaluations. In Carlos A. Coello Coello, Arturo Hernández Aguirre, and Eckart Zitzler, editors, Evolutionary Multi-Criterion Optimization. Third International Conference, EMO 2005, pages 176–190, Guanajuato, México, March 2005. Springer. Lecture Notes in Computer Science Vol. 3410.

[4944] Joshua Knowles and Hirotaka Nakayama. Meta-Modeling in Multiobjective Optimization. In Jürgen Branke, Kalyanmoy Deb, Kaisa Miettinen, and Roman Slowinski, editors, Multiobjective Optimization. Interactive and Evolutionary Approaches, pages 245–284. Springer. Lecture Notes in Computer Science Vol. 5252, Berlin, Germany, 2008.

[4945] Joshua Knowles, Lothar Thiele, and Eckart Zitzler. A Tutorial on the Performance Assessment of Stochastic Multiobjective Optimizers. 214, Computer Engineering and Networks Laboratory (TIK), ETH Zurich, Switzerland, feb 2006. revised version.

[4946] Joshua D. Knowles. Local-Search and Hybrid Evolutionary Algorithms for Pareto Optimization. PhD thesis, The University of Reading, Department of Computer Science, Reading, UK, January 2002.

[4947] Joshua D. Knowles and David W. Corne. Assessing the Performance of the Pareto Archived Evolution Strategy. In Annie S. Wu, editor, Proceedings of the 1999 Genetic and Evolutionary Computation Conference. Workshop Program, pages 123–124, Orlando, Florida, July 1999.

[4948] Joshua D. Knowles and David W. Corne. The Pareto Archived Evolution Strategy: A New Baseline Algorithm for Multiobjective Optimisation. In 1999 Congress on Evolutionary Computation, pages 98–105, Washington, D.C., July 1999. IEEE Service Center.

[4949] Joshua D. Knowles and David W. Corne. Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation, 8(2):149–172, 2000.

[4950] Joshua D. Knowles and David W. Corne. A Comparison of Diverse Approaches to Memetic Multiobjective Combinatorial Optimization. In Proceedings of the 2000 Genetic and Evolutionary Computation Conference Workshop Program, pages 103–108, Las Vegas, Nevada, July 2000.

[4951] Joshua D. Knowles and David W. Corne. Benchmark Problem Generators and Results for the Multiobjective Degree-Constrained Minimum Spanning Tree Problem. In Lee Spector, Erik D. Goodman, Annie Wu, W.B. Langdon, Hans-Michael Voigt, Mitsuo Gen, Sandip Sen, Marco Dorigo, Shahram Pezeshk, Max H. Garzon, and Edmund Burke, editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2001), pages 424–431, San Francisco, California, 2001. Morgan Kaufmann Publishers.

[4952] Joshua D. Knowles and David W. Corne. A Comparative Assessment of Memetic, Evolutionary, and Constructive Algorithms for the Multiobjective d-MST Problem. In 2001 Genetic and Evolutionary Computation Conference. Workshop Program, pages 162–167, San Francisco, California, July 2001.

[4953] Joshua D. Knowles and David W. Corne. A Comparison of Encodings and Algorithms for Multiobjective Minimum Spanning Tree Problems. In Proceedings of the Congress on Evolutionary Computation 2001 (CEC’2001), volume 1, pages 544–551, Piscataway, New Jersey, May 2001. IEEE Service Center.

[4954] Joshua D. Knowles and David W. Corne. Enumeration of Pareto optimal Multi-Criteria Spanning Trees—A Proof of the Incorrectness of Zhou and Gen’s Proposed Algorithm. European Journal of Operational Research, 143(3):543–547, December 2002.

[4955] Joshua D. Knowles, David W. Corne, and Mark Fleischer. Bounded Archiving using the Lebesgue Measure. In Proceedings of the 2003 Congress on Evolutionary Computation (CEC’2003), volume 4, pages 2490–2497, Canberra, Australia, December 2003. IEEE Press.

[4956] Joshua D. Knowles, David W. Corne, and Martin J. Oates. On the Assessment of Multiobjective Approaches to the Adaptive Distributed Database Management Problem. In Marc Schoenauer, Kalyanmoy Deb, Günter Rudolph, Xin Yao, Evelyne Lutton, Juan Julian Merelo, and Hans-Paul Schwefel, editors, Proceedings of the Sixth International Conference on Parallel Problem Solving from Nature (PPSN VI), pages 869–878, Berlin, September 2000. Springer.

[4957] Joshua D. Knowles, David W. Corne, and Martin J. Oates. The Pareto-Envelope based Selection Algorithm for Multiobjective Optimization. In Proceedings of the Sixth International Conference on Parallel Problem Solving from Nature (PPSN VI), pages 839–848, Berlin, September 2000. Springer.

[4958] Joshua D. Knowles, Martin J. Oates, and David W. Corne. Multiobjective Evolutionary Algorithms Applied to two Problems in Telecommunications. BT Technology Journal, 18(4):51–64, October 2000.

[4959] Joshua D. Knowles, Richard A. Watson, and David W. Corne. Reducing Local Optima in Single-Objective Problems by Multi-objectivization. In Eckart Zitzler, Kalyanmoy Deb, Lothar Thiele, Carlos A. Coello Coello, and David Corne, editors, First International Conference on Evolutionary Multi-Criterion Optimization, pages 268–282. Springer-Verlag. Lecture Notes in Computer Science No. 1993, 2001.

[4960] Chien-Ho Ko and Shu-Fan Wang. Precast production scheduling using multiobjective genetic algorithms. Expert Systems With Applications, 38(7):8293– 8302, July 2011.

[4961] Kenji Kobayashi, Tomoyuki Hiroyasu, and Mitsunori Miki. Mechanism of Multi-Objective Genetic Algorithm for Maintaining the Solution Diversity Using Neural Network. In Shigeru Obayashi, Kalyanmoy Deb, Carlo Poloni, Tomoyuki Hiroyasu, and Tadahiko Murata, editors, Evolutionary Multi-Criterion Optimization, 4th International Conference, EMO 2007, pages 216– 226, Matshushima, Japan, March 2007. Springer. Lecture Notes in Computer Science Vol. 4403.

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[5073] Johannes Krettek, Jan Braun, Frank Hoffmann, Torsten Bertram, Thomas Ewald, Hans-Georg Schubert, and Horst Lausch. Interactive Evolutionary Multiobjective Optimization for Hydraulic Valve Controller Parameters. In 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2009), pages 816–821, Singapore, July 14-17 2009. IEEE Press.

[5074] Allunu Gopala Krishna and K. Mallikarjuna Rao. Multi-objective optimisation of surface grinding operations using scatter search approach. International Journal of Advanced Manufacturing Technology, 29(5):475–480, June 2006.

[5075] K.N. Krishnanand and D. Ghose. Glowworm Swarm Optimization for Multimodal Search Spaces. In Bijaya Ketan Panigrahi, Yuhui Shi, and Meng-Hiot Lim, editors, Handbook of Swarm Intelligence. Concepts, Principles and Applications, pages 451–467. Springer-Verlag, Berlin, Germany, 2011. ISBN 978-3-642-17389-9.

[5076] K.R. Krishnanand, Bijaya Ketan Panigrahi, P.K. Rout, and Ankita Mohapatra. Application of Multi-Objective Teaching-Learning-Based Algorithm to an Economic Load Dispatch Problem with Incommensurable Objectives. In Bijaya Ketan Panigrahi, Ponnuthurai Nagaratnam Suganthan, Swagatam Das, and Suresh Chandra Satapathy, editors, Swarm, Evolutionary, and Memetic Computing, Second International Conference, SEMCCO 2011, pages 697– 705, Visakhapatnam, Andhra Pradesh, India, December 19-21 2011. Springer. Lecture Notes in Computer Science Vol. 7076.

[5077] Vojtech Krmicek and Michèle Sebag. Functional Brain Imaging with Multiobjective Multi-modal Evolutionary Optimization. In Thomas Philip Runarsson, Hans-Georg Beyer, Edmund Burke, Juan J. Merelo-Guervós, L. Darrell Whitley, and Xin Yao, editors, Parallel Problem Solving from Nature - PPSN IX, 9th International Conference, pages 382–391. Springer. Lecture Notes in Computer Science Vol. 4193, Reykjavik, Iceland, September 2006.

[5078] Marcel Kronfeld and Andreas Zell. Towards scalability in niching methods. In 2010 IEEE Congress on Evolutionary Computation (CEC’2010), pages 4409–4416, Barcelona, Spain, July 18–23 2010. IEEE Press.

[5079] Johannes W. Kruisselbrink, Alexander Aleman, Michael T. M. Emmerich, Ad P. IJzerman, Andreas Bender, Thomas Bäck, and Eelke van der Horst. Enhancing search space diversity in multi-objective evolutionary drug molecule design using niching. In 2009 Genetic and Evolutionary Computation Conference (GECCO’2009), pages 217–224, Montreal, Canada, July 8–12 2009. ACM Press. ISBN 978-1-60558-325-9.

[5080] Johannes W. Kruisselbrink, Thomas Bäck, Ad P. IJzerman, and Eelke van der Horst. Evolutionary Algorithms for Automated Drug Design Towards Target Molecule Properties. In 2008 Genetic and Evolutionary Computation Conference (GECCO’2008), pages 1555–1562, Atlanta, USA, July 2008. ACM Press. ISBN 978-1-60558-131-6.

[5081] Johannes W. Kruisselbrink, Michael T.M. Emmerich, Thomas Bäck, Andreas Bender, Ad P. IJzerman, and Eelke van der Horst. Combining Aggregation with Pareto Optimization: A Case Study in Evolutionary Molecular Design. In Matthias Ehrgott, Carlos M. Fonseca, Xavier Gandibleux, Jin-Kao Hao, and Marc Sevaux, editors, Evolutionary Multi-Criterion Optimization. 5th International Conference, EMO 2009, pages 453–467. Springer. Lecture Notes in Computer Science Vol. 5467, Nantes, France, April 2009.

[5082] K.K. Kshetrapalapuram and M. Kirley. Mining classification rules using evolutionary multi-objective algorithms. In Knowledge-Based Intelligent Information and Engineering Systems, Part 3, Proceedings, pages 959–965. Springer. Lecture Notes in Artificial Intelligence Vol. 3683, 2005.

[5083] Jiri Kubalik, Richard Mordinyi, and Stefan Biffl. Multiobjective Prototype Optimization with Evolved Improvement Steps. In Jano van Hemert and Carlos Cotta, editors, Evolutionary Computation in Combinatorial Optimization, 8th European Conference, EvoCOP 2008, pages 218–229, Naples, Italy, March 2008. Springer. Lecture Notes in Computer Science Vol. 4972.

[5084] Jiri Kubalik, Pavel Tichy, Radek Sindelar, and Raymond J. Staron. Clustering Methods for Agent Distribution Optimization. IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Reviewa, 40(1):78–86, January 2010.

[5085] Naoyuki Kubota. Multi-Objective Design of Neuro-Fuzzy Controllers for Robot Behavior Coordination. In Yaochu Jin, editor, Multi-Objective Machine Learning, pages 557–584. Springer. Studies in Computational Intelligence, Volume 16, Berlin, 2006.

[5086] Rajesh Kudikala, Kalyanmoy Deb, and Bishakh Bhattacharya. Multi-Objective Optimization of Piezoelectric Actuator Placement for Shape Control of Plates Using Genetic Algorithms. Journal of Mechanical Design, 131(9), September 2009. Article Number: 091007.

[5087] Rajesh Kudikala, Andrew R. Mills, Peter J. Fleming, Graham F. Tanner, and Jonathan E. Holt. Real World System Architecture Design Using Multicriteria Optimization: A Case Study. In Michael Emmerich, André Deutz, Oliver Schütze, Thomas Bäck, Emilia Tantar, Alexandru-Adrian Tantar, Pierre del Moral, Pierrick Legrand, Pascal Bouvry, and Carlos Coello Coello, editors, EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV, pages 245–260. Springer, Advances in Intelligent Systems and Computing Vol. 227, Heidelberg, Germany, July 10-13 2013. ISBN 978-3-319-01127-7.

[5088] Fumiya Kudo and Tomohiro Yoshikawa. Knowledge extraction in multiobjective optimization problem based on visualization of Pareto solutions. In 2012 IEEE Congress on Evolutionary Computation (CEC’2012), pages 860– 865, Brisbane, Australia, June 10-15 2012. IEEE Press.

[5089] Fumiya Kudo, Tomohiro Yoshikawa, and Takeshi Furuhashi. A Study on Analysis of Design Variables in Pareto Solutions for Conceptual Design Optimization Problem of Hybrid Rocket Engine. In 2011 IEEE Congress on Evolutionary Computation (CEC’2011), pages 2558–2562, New Orleans, Louisiana, USA, 5–8 June 2011. IEEE Service Center.

[5090] Saku Kukkonen. Generalized Differential Evolution for Global Multi-Objective Optimization with Constraints. PhD thesis, Lappeenranta University of Technology, Lappeeranta, Finland, May 2012.

[5091] Saku Kukkonen and Carlos A. Coello Coello. Applying Exponential Weighting Moving Average Control Parameter Adaptation Technique with Generalized Differential Evolution. In 2016 IEEE Congress on Evolutionary Computation (CEC’2016), pages 4755–4762, Vancouver, Canada, 24-29 July 2016. IEEE Press. ISBN 978-1-5090-0623-9.

[5092] Saku Kukkonen and Kalyanmoy Deb. A Fast and Effective Method for Pruning of Non-dominated Solutions in Many-Objective Problems. In Thomas Philip Runarsson, Hans-Georg Beyer, Edmund Burke, Juan J. Merelo-Guervós, L. Darrell Whitley, and Xin Yao, editors, Parallel Problem Solving from Nature - PPSN IX, 9th International Conference, pages 553–562. Springer. Lecture Notes in Computer Science Vol. 4193, Reykjavik, Iceland, September 2006.

[5093] Saku Kukkonen and Kalyanmoy Deb. Improved Pruning of Non-Dominated Solutions Based on Crowding Distance for Bi-Objective Optimization Problems. In 2006 IEEE Congress on Evolutionary Computation (CEC’2006), pages 1164–1171, Vancouver, BC, Canada, July 2006. IEEE.

[5094] Saku Kukkonen, Sujit R. Jangam, and Nirupam Chakraborti. Solving The Molecular Sequence Alignment Problem with Generalized Differential Evolution 3 (GDE3). In Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Multicriteria Decision Making (MCDM’2007), pages 302–309, Honolulu, Hawaii, USA, April 2007. IEEE Press.

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[5096] Saku Kukkonen and Jouni Lampinen. Comparison of Generalized Differential Evolution to other Multi-Objective Evolutionary Algorithms. In P. Neittaanmäki, T. Rossi, S. Korotov, E. O nate, J. Périaux, and D. Knörzer, editors, Proceedings of the 4th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2004), Jyväskylä, Finland, 24-28 July 2004. University of Jyväskylä, Department of Mathematical Information Technology.

[5097] Saku Kukkonen and Jouni Lampinen. Mechanical Component Design for Multiple Objectives Using Generalized Differential Evolution. In I.C. Parmee, editor, Adaptive Computing in Design and Manufacture VI, pages 261–272, London, 2004. Springer.

[5098] Saku Kukkonen and Jouni Lampinen. An Empirical Study of Control Parameters for Generalized Differential Evolution. In R. Schilling, W. Haase, J. Periaux, H. Baier, and G. Bugeda, editors, The Sixth Conference on Evolutionary and Deterministic Methods for Design, Optimization and Control with Applications to Industrial and Societal Problems (EUROGEN 2005), Munich, Germany, 12-14 September 2005.

[5099] Saku Kukkonen and Jouni Lampinen. GDE3: The third Evolution Step of Generalized Differential Evolution. In 2005 IEEE Congress on Evolutionary Computation (CEC’2005), volume 1, pages 443–450, Edinburgh, Scotland, September 2005. IEEE Service Center.

[5100] Saku Kukkonen and Jouni Lampinen. An Empirical Study of Control Parameters for the Third Version of Generalized Differential Evolution (GDE3). In 2006 IEEE Congress on Evolutionary Computation (CEC’2006), pages 1987–1994, Vancouver, BC, Canada, July 2006. IEEE.

[5101] Saku Kukkonen and Jouni Lampinen. Performance Assessment of Generalized Differential Evolution 3 (GDE3) with a Given Set of Problems. In 2007 IEEE Congress on Evolutionary Computation (CEC’2007), pages 3593–3600, Singapore, September 2007. IEEE Press.

[5102] Saku Kukkonen and Jouni Lampinen. Ranking-Dominance and Many-Objective Optimization. In 2007 IEEE Congress on Evolutionary Computation (CEC’2007), pages 3983–3990, Singapore, September 2007. IEEE Press.

[5103] Saku Kukkonen and Jouni Lampinen. Generalized Differential Evolution for Constrained Multi-Objective Optimization. In Lam Thu Bui and Sameer Alam, editors, Multi-Objective Optimization in Computational Intelligence: Theory and Practice, pages 43–75. Information Science Reference, Hershey, PA, USA, 2008. ISBN 978-1-59904-498-9.

[5104] Saku Kukkonen and Jouni Lampinen. Performance Assessment of Generalized Differential Evolution 3 with a Given Set of Constrained Multi-Objective Test Problems. In 2009 IEEE Congress on Evolutionary Computation (CEC’2009), pages 1943–1950, Trondheim, Norway, May 2009. IEEE Press.

[5105] Sadan Kulturel-Konak. Facility Layout and Relayout under Uncertainty. PhD thesis, Industrial and Systems Engineering Department, Auburn University, Auburn, Alabama, USA, May 2002.

[5106] Sadan Kulturel-Konak and David W. Coit. Determination of Pruned Pareto Sets for the Multi-Objective System Redundancy Allocation Problem. In Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Multicriteria Decision Making (MCDM’2007), pages 390–394, Honolulu, Hawaii, USA, April 2007. IEEE Press.

[5107] Sadan Kulturel-Konak, David W. Coit, and Fatema Baheranwala. Pruned Pareto-optimal sets for the system redundancy allocation problem based on multiple prioritized objectives. Journal of Heuristics, 14(4):335–357, August 2008.

[5108] Sadan Kulturel-Konak, Abdullah Konak, and David W. Coit. Multiobjective Metaheuristic Approaches to Reliability Optimization. In Gregory Levitin, editor, Computational Intelligence in Reliability Engineering. Evolutionary Techniques in Reliability Analysis and Optimization, pages 37–62. Springer, Heidelberg, 2007.

[5109] Sadan Kulturel-Konak, Alice E. Smith, and Bryan A. Norman. Multiobjective tabu search using a multinomial probability mass function. European Journal of Operational Research, 169:918–931, 2006.

[5110] P. Kulvanit, N. Chaiyaratana, and D. Laowattana. Biped Fast Walking Gait Shaping via Evolutionary Multi-Objective Optimization. In 2007 IEEE Congress on Evolutionary Computation (CEC’2007), pages 4019–4026, Singapore, September 2007. IEEE Press.

[5111] Pasan Kulvanit, Theera Piroonratana, Nachol Chaiyaratana, and Djitt Laowattana. Evolutionary Multi-objective Optimisation by Diversity Control. In Dima Grigoriev, John Harrison, and Edward A. Hirsch, editors, Computer Science – Theory and Applications. First International Computer Science Symposium in Russia (CSR 2006), pages 447–456. Springer, Lecture Notes in Computer Science, Vol. 3967, St. Petersburg, Russia, 2006.

[5112] A. Kumar, D. Sahoo, S. Chakraborty, and N. Chakraborti. Gas injection in steelmaking vessels: Coupling a fluid dynamic analysis with a genetic algorithms-based pareto-optimality. Materials and Manufacturing Processes, 20(3):363–379, 2005.

[5113] Abhay Kumar, Deepak Sharma, and Kalyanmoy Deb. A Hybrid Multi-Objective Optimization Procedure Using PCX Based NSGA-II and Sequential Quadratic Programming. In 2007 IEEE Congress on Evolutionary Computation (CEC’2007), pages 3011–3018, Singapore, September 2007. IEEE Press.

[5114] Aman Kumar, Debalay Chakrabarti, and Nirupam Chakraborti. Data-Driven Pareto Optimization for Microalloyed Steels Using Genetic Algorithms. Steel Research International, 83(2):169–174, February 2012.

[5115] Asish Kumar, Kyung Hyun Son, Bo Yong Han, and Kee-Sun Sohn. Simultaneous Optimization of Luminance and Color Chromaticity of Phosphors Using a Nondominated Sorting Genetic Algorithm. Advanced Functional Materials, 20(11):1750–1755, June 9 2010.

[5116] D. Nagesh Kumar and M. Janga Reddy. Ant Colony Optimization for Multipurpose Reservoir Operation. Water Resources Management, 20(6):879–898, December 2006.

[5117] D. Nagesh Kumar and M. Janga Reddy. Multipurpose reservoir operation using particle swarm optimization. Journal of Water Resources Planning and Management-ASCE, 133(3):192–201, May-June 2007.

[5118] Deepak Kumar, Sudheer Ch, Shashi Mathur, and B. K. Panigrahi. Hybrid Algorithm Performance With Varying Population Size for Multi-objective Optimisation of in-situ Bioremediation of Groundwater. International Journal Of Bio-Inspired Computation, 5(3):164–174, 2013.

[5119] G. N. Sashi Kumar, A. K. Mahendra, and G. Gouthaman. Multi-objective Shape Optimization Using Ant Colony Coupled Computational Fluid Dynamics Solver. Computers & Fluids, 46(1):298–305, July 2011.

[5120] G.N. Sashi Kumar, A.K. Mahendra, A. Sanyal, and G. Gouthaman. A Hybrid Method for Multi-Objective Shape Optimization. In Kalyanmoy Deb, Arnab Bhattacharya, Nirupam Chakraborti, Partha Chakroborty, Swagatam Das, Joydeep Dutta, Santosh K. Gupta, Ashu Jain, Varun Aggarwal, Jürgen Branke, Sushil J. Louis, and Kay Chen Tan, editors, Simulated Evolution and Learning, 8th International Conference, SEAL 2010, pages 563–567, Kanpur, India, December 1-4 2010. Springer. Lecture Notes in Computer Science Vol. 6457.

[5121] P. Kumar and P. Bauer. Progressive design methodology for complex engineering systems based on multiobjective genetic algoritms and linguistic decision making. Soft Computing, 13(7):649–679, May 2009.

[5122] P. Kumar, D. Gospodaric, and P. Bauer. Improved genetic algorithm inspired by biological evolution. Soft Computing, 11(10):923–941, August 2007.

[5123] Praveen Kumar and Pavol Bauer. Progressive Design Methodology for Design of Engineering Systems. In Yoel Tenne and Chi-Keong Goh, editors, Computational Intelligence in Expensive Optimization Problems, pages 571–607. Springer, Berlin, Germany, 2010. ISBN 978-3-642-10700-9.

[5124] Praveen K. Kumar, S. Sharath, Rio G. D’Souza, and K. Chandra. Memetic NSGA - A Multi-objective genetic algorithm for classification of microarray data. In ADCOM 2007: Proceedings of the 15th International Conference on Advanced Computing and Communications, pages 75–80, Guwahati, India, December 18-21 2007. IEEE Computer Society. ISBN 978-0-7695-3059-8.

[5125] R. Kumar and N. Banerjee. Analysis of a multiobjective evolutionary algorithm on the 0-1 knapsack problem. Theoretical Computer Science, 358(1):104–120, July 2006.

[5126] R. Kumar and P.K. Singh. Pareto Evolutionary Algorithm Hybridized with Local Search for Biobjective TSP. In Crina Grosan, Ajith Abraham, and Hisao Ishibuchi, editors, Hybrid Evolutionary Algorithms, pages 361–398. Springer, Heidelberg, 2007.

[5127] Rajeev Kumar. Feature Selection, Representation and Classification. PhD thesis, University of Sheffield, Sheffield, UK, 1997.

[5128] Rajeev Kumar. On Generalisation of Machine Learning with Neural-Evolutionary Computations. In Proceedings of the Third International Conference on Computational Intelligence and Multimedia Applications (IC-CIMA’99), pages 112–116, Los Alamitos, California, 1999. IEEE Computer Society Press.

[5129] Rajeev Kumar. Codebook Design for Vector Quantisation using Multiobjective Genetic Algorithms. In PPSN/SAB Workshop on Multiobjective Problem Solving from Nature (MPSN), Paris, France, September 2000.

[5130] Rajeev Kumar. On Machine Learning with Multiobjective Genetic Optimization. In Carlos A. Coello Coello and Gary B. Lamont, editors, Applications of Multi-Objective Evolutionary Algorithms, pages 393–425. World Scientific, Singapore, 2004.

[5131] Rajeev Kumar, Bipul Kumar Bal, and Peter Rockett. Multiobjective genetic programming approach to evolving heuristics for the bounded diameter minimum spanning tree problem. In 2009 Genetic and Evolutionary Computation Conference (GECCO’2009), pages 309–316, Montreal, Canada, July 8–12 2009. ACM Press. ISBN 978-1-60558-325-9.

[5132] Rajeev Kumar and Nilanjan Banerjee. Multicriteria Network Design Using Evolutionary Algorithm. In Erick Cantú-Paz et al., editor, Genetic and Evolutionary Computation—GECCO 2003. Proceedings, Part II, pages 2179–2190. Springer. Lecture Notes in Computer Science Vol. 2724, July 2003.

[5133] Rajeev Kumar and Nilanjan Banerjee. Running time analysis of a multiobjective evolutionary algorithm on simple and hard problems. In Alden H. Wright, Michael D. Vose, Kenneth A. De Jong, and Lothar M. Schmitt, editors, Foundations of Genetic Algorithms. 8th International Workshop, FOGA 2005, pages 112–131, Aizu-Wakamatsu City, Japan, January 2005. Springer. Lecture Notes in Computer Science Vol. 3469.

[5134] Rajeev Kumar and Nilanjan Banerjee. Multiobjective network topology design. Applied Soft Computing, 11(8):5120–5128, December 2011.

[5135] Rajeev Kumar, Ashwin H. Joshi, Krishna K. Banka, and Peter I. Rockett. Evolution of Hyperheuristics for the Biobjective 0/1 Knapsack Problem by Multiobjective Genetic Programming. In 2008 Genetic and Evolutionary Computation Conference (GECCO’2008), pages 1227–1234, Atlanta, USA, July 2008. ACM Press. ISBN 978-1-60558-131-6.

[5136] Rajeev Kumar, V. Prasanna Krishnan, and Kartik S. Santhanakrishnan. Design of an Optimal Communication Network Using Multiobjective Genetic Optimization. In Proceedings of the IEEE International Conference on Industrial Technology, volume 1, pages 515–520, 2000.

[5137] Rajeev Kumar, N. Vinay Kumar, and I. J. Nagrath. Object oriented toolkit for multiobjective genetic optimisation. In 3rd International Conference on Computational Intelligence and Multimedia Applications, New Delhi, India, September 1999.

[5138] Rajeev Kumar, Prajna P. Parida, and Mohit Gupta. Topological Design of Communication Networks using Multiobjective Genetic Optimization. In Congress on Evolutionary Computation (CEC’2002), volume 1, pages 425– 430, Piscataway, New Jersey, May 2002. IEEE Service Center.

[5139] Rajeev Kumar, S. Prasanth, and M.S. Sudarshan. Topological Design of Mesh Communication Networks using Multiobjecitve Genetic Optimisation. In PPSN/SAB Workshop on Multiobjective Problem Solving from Nature (MPSN), Paris, France, September 2000.

[5140] Rajeev Kumar and Peter Rockett. Decomposition of High Dimensional Pattern Spaces for Hierarchical Classification. In Proceedings of the Workshop on Statistical Techniques in Pattern Recognition, Prague, Czech Republic, June 1997.

[5141] Rajeev Kumar and Peter Rockett. Decomposition of High Dimensional Pattern Spaces for Hierarchical Classification. Kybernetika, 34(4):435–442, 1998.

[5142] Rajeev Kumar and Peter Rockett. Multiobjective Genetic Algorithm Partitioning for Hierarchical Learning of High-Dimensional Pattern Spaces: A Learning-Follows-Decomposition Strategy. IEEE Transactions on Neural Networks, 9(5):822–830, 1998.

[5143] Rajeev Kumar and Peter Rockett. Improved Sampling of the Pareto-Front in Multiobjective Genetic Optimizations by Steady-State Evolution: A Pareto Converging Genetic Algorithm. Evolutionary Computation, 10(3):283–314, Fall 2002.

[5144] Rajeev Kumar and Peter Rockett. Evolutionary Multimodal Optimization Revisited. In Erick Cantú-Paz et al., editor, Genetic and Evolutionary Computation—GECCO 2003. Proceedings, Part II, pages 1592–1593. Springer. Lecture Notes in Computer Science Vol. 2724, July 2003.

[5145] Rajeev Kumar and Peter Rockett. Effective Evolutionary Multimodal Optimization by Multiobjective Reformulation Without Explicit Niching/Sharing. In Suresh Manandhar, Jim Austin, Uday B. Desai, Yoshio Oyanagi, and Asoke K. Talukder, editors, Applied Computing, Second Asian Applied Computing Conference, AACC 2004, pages 1–8, Kathmandu, Nepal, October 29-31 2004. Springer. Lecture Notes in Computer Science Volume 3285.

[5146] Rajeev Kumar and Peter I. Rockett. Assessing the Convergence of Rank-Based Multiobjective Genetic Algorithms. In Proceedings of the 2nd IEE/IEEE International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications (GALESIA’97), pages 19–23, Glasgow, Scotland, September 1997. IEE.

[5147] Rajeev Kumar and P. K. Singh. On Quality Performance of Heuristic and Evolutionary Algorithms for Biobjective Minimum Spanning Trees. In Dirk Thierens, editor, 2007 Genetic and Evolutionary Computation Conference (GECCO’2007), volume 2, page 2259, London, UK, July 2007. ACM Press.

[5148] Rajeev Kumar and P. K. Singh. Assessing solution quality of biobjective 0-1 knapsack problem using evolutionary and heuristic algorithms. Applied Soft Computing, 10(3):711–718, June 2010.

[5149] Rajeev Kumar, P. K. Singh, and P. P. Chakrabarti. Improved Quality of Solutions for Multiobjective Spanning Tree Problem Using Distributed Evolutionary Algorithm. In Luc Bougé and Viktor K. Prasanna, editors, High Performance Computing (HiPC’2004), pages 494–503. Springer, Lecture Notes in Computer Science, Vol. 3296, Bangalore, India, 2004.

[5150] Rajeev Kumar, P.K. Singh, and Bhargab B. Bhattacharya. Biobjective Evolutionary and Heuristic Algorithms for Intersection of Geometric Graphs. In Maarten Keijzer et al., editor, 2006 Genetic and Evolutionary Computation Conference (GECCO’2006), volume 2, pages 1689–1696, Seattle, Washington, USA, July 2006. ACM Press. ISBN 1-59593-186-4.

[5151] Rajeev Kumar, P.K. Singh, and P.P. Chakrabarti. Multiobjective EA Approach for Improved Quality of Solutions for Spanning Tree Problem. In Carlos A. Coello Coello, Arturo Hernández Aguirre, and Eckart Zitzler, editors, Evolutionary Multi-Criterion Optimization. Third International Conference, EMO 2005, pages 811–825, Guanajuato, México, March 2005. Springer. Lecture Notes in Computer Science Vol. 3410.

[5152] Rajeev Kumar, R. K. Singh, A. P. Singhal, and Atul Bhartia. Evolutionary and heuristic algorithms for multiobjective 0-1 knapsack problem. In A. Tiwari, J. Knowles, E. Avineri, K. Dahal, and R. Roy, editors, Applications of Soft Computing: Recent Trends, pages 331–340. Springer, September 19 - October 07 2006. ISBN 3-540-29123-7.

[5153] Rajeev Kumar, Paresh Tolay, and Siddharth Tiwary. Enhancing Solution Quality of the Biobjective Graph Coloring Problem Using Hybridization of EA. In 2008 Genetic and Evolutionary Computation Conference (GECCO’2008), pages 547–554, Atlanta, USA, July 2008. ACM Press. ISBN 978-1-60558-131-6.

[5154] Rajesh Kumar, Abhinav Sadu, Rudesh Kumar, and S. K. Panda. A Novel Multi-Objective Directed Bee Colony Optimization Algorithm for Multi-Objective Emission Constrained Economic Power Dispatch. International Journal of Electrical Power & Energy Systems, 43(1):1241–1250, December 2012.

[5155] Ranjan Kumar, Kazuhiro Izui, Masataka Yoshimura, and Shinji Nishiwaki. Multi-objective hierarchical genetic algorithms for multilevel redundancy allocation optimization. Reliability Engineering & System Safety, 94(4):891– 904, April 2009.

[5156] Sri Krishna Kumar, S.G. Ponnambalam, and M.K. Tiwari. A Multi-Objective Resource Assignment Problem in Product Driven Supply Chain Using Quantum Inspired Particle Swarm Algorithm. In Bijaya Ketan Panigrahi, Yuhui Shi, and Meng-Hiot Lim, editors, Handbook of Swarm Intelligence. Concepts, Principles and Applications, pages 269–292. Springer-Verlag, Berlin, Germany, 2011. ISBN 978-3-642-17389-9.

[5157] Sujay V. Kumar. Vitri - A Generic Framework for Engineering Decision Support Systems on Heterogeneous Computer Networks. PhD thesis, Department of Civil Engineering, North Carolina State University, Raleigh, North Carolina, USA, 2002.

[5158] Sujay V. Kumar and S. Ranji Ranjithan. Evaluation of the Constraint Method-Based Evolutionary Algorithm (CMEA) for a Three-Objective Optimization Problem. In W.B. Langdon, E. Cantú-Paz, K. Mathias, R. Roy, D. Davis, R. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M.A. Potter, A.C. Schultz, J.F. Miller, E. Burke, and N. Jonoska, editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2002), pages 431–438, San Francisco, California, July 2002. Morgan Kaufmann Publishers.

[5159] V. V. Kumar, M. Tripathi, M. K. Pandey, and M. K. Tiwari. Physical programming and conjoint analysis-based redundancy allocation in multistate systems: a Taguchi embedded algorithm selection and control (TAS&C) approach. Proceedings Of The Institution of Mechanical Engineers Part O-Journal of Risk And Reliability, 223(O3):215–232, September 2009.

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[5474] Hui Li, Xiaolei Su, Zongben Xu, and Qingfu Zhang. MOEA/D with Iterative Thresholding Algorithm for Sparse Optimization Problems. In Carlos A. Coello Coello, Vincenzo Cutello, Kalyanmoy Deb, Stephanie Forrest, Giuseppe Nicosia, and Mario Pavone, editors, Parallel Problem Solving from Nature - PPSN XII, 12th International Conference, pages 93–101, Taormina, Italy, September 1-5 2012. Springer. Lecture Notes in Computer Science Vol. 7492.

[5475] Hui Li and Qingfu Zhang. A Multiobjective Differential Evolution Based on Decomposition for Multiobjective Optimization with Variable Linkages. In Thomas Philip Runarsson, Hans-Georg Beyer, Edmund Burke, Juan J. Merelo-Guervós, L. Darrell Whitley, and Xin Yao, editors, Parallel Problem Solving from Nature - PPSN IX, 9th International Conference, pages 583–592. Springer. Lecture Notes in Computer Science Vol. 4193, Reykjavik, Iceland, September 2006.

[5476] Hui Li and Qingfu Zhang. Multiobjective Optimization Problems with Complicated Pareto Sets, MOEA/D and NSGA-II. IEEE Transactions on Evolutionary Computation, 13(2):284–302, April 2009.

[5477] Hui Li, Qingfu Zhang, and Jingda Deng. Multiobjective Test Problems with Complicated Pareto Fronts: Difficulties in Degeneracy. In 2014 IEEE Congress on Evolutionary Computation (CEC’2014), pages 2156–2163, Beijing, China, 6-11 July 2014. IEEE Press. ISBN 978-1-4799-1488-3.

[5478] Hui Li, Qingfu Zhang, Edward Tsang, and John A. Ford. Hybrid Estimation of Distribution Algorithm for Multiobjective Knapsack Problem. In Jens Gottlieb and Günter R. Raidl, editors, Evolutionary Computation in Combinatorial Optimization, Proceedings of the 4th European Conference, Evo-COP 2004, pages 145–154. Springer. Lecture Notes in Computer Science, Vol. 3004, April 2004.

[5479] J. Li and N. Satofuka. Optimization Design of a Compressor Cascade Airfoil Using a Navier-stokes Solver and Genetic Algorithms. Proceedings of the Institution of Mechanical Engineering Part A—Journal of Power and Energy, 216(A2):195–202, 2002.

[5480] Jian-Ping Li, Marton E. Balazs, Geoffrey T. Parks, and P. John Clarkson. A Species Conserving Genetic Algorithm for Multimodal Function Optimization. Evolutionary Computation, 10(3):207–234, Fall 2002.

[5481] Jian-Ping Li, Xiao-Dong Li, and Alastair Wood. Species Based Evolutionary Algorithms for Multimodal Optimization: A Brief Review. In 2010 IEEE Congress on Evolutionary Computation (CEC’2010), pages 4156– 4163, Barcelona, Spain, July 18-23 2010. IEEE Press.

[5482] Jiaqi Li, X.Z. Gao, X. Wang, and K. Zenger. A Multiple Local Search Strategy in Memetic Evolutionary Computation for Multi-objective Robust Control Design. In 2012 IEEE International Conference on Systems, Man, and Cybernetics, pages 645–650, Seoul, Korea, October 14-17 2012. IEEE Press. ISBN 978-1-4673-1714-6.

[5483] Jie Li, Changwen Zheng, and Xiaohui Hu. A Hypervolume Based Approach for Minimal Visual Coverage Shortest Path. In 2011 IEEE Congress on Evolutionary Computation (CEC’2011), pages 1777–1784, New Orleans, Louisiana, USA, 5-8 June 2011. IEEE Service Center.

[5484] Jin Li and Sope Taiwo. Enhancing Financial Decision Making Using Multi-Objective Financial Genetic Programming. In 2006 IEEE Congress on Evolutionary Computation (CEC’2006), pages 2156–2163, Vancouver, BC, Canada, July 2006. IEEE.

[5485] Jingpeng Li, Edmund K. Burke, Tim Curtois, Sanja Petrovic, and Rong Qu. The falling Tide Algorithm: A New Multi-objective Approach for Complex Workforce Scheduling. Omega-International Journal of Management Science, 40(3):283–293, June 2012.

[5486] Jingyu Li, Jed Storie, and Jeff Clune. Encouraging Creative Thinking in Robots Improves Their Ability to Solve Challenging Problems. In 2014 Genetic and Evolutionary Computation Conference (GECCO 2014), pages 193– 200, Vancouver, Canada, July 12-16 2014. ACM Press. ISBN 978-1-4503-2662-9.

[5487] Jinlong Li and Mingying Yan. Pareto Partial Dominance on Two Selected Objectives MOEA on Many-Objective 0/1 Knapsack Problems. In Ying Tan, Yuhui Shi, and Carlos A. Coello Coello, editors, Advances in Swarm Intelligence, 5th International Conference, ICSI 2014, pages 365–373. Springer. Lecture Notes in Computer Science Vol. 8794, Hefei, China, October 17-20 2014.

[5488] Jinzhong Li, Jintao Zeng, Jiewu Xia, Manhua Li, and Changxin Liu. Research on Grid Workflow Scheduling Based on MOPSO Algorithm. In Global Congress on Intelligent Systems, 2009, pages 199–203, Xiamen, May 19-21 2009. IEEE.

[5489] J.P. Li and A. Wood. Random Search with Species Conservation for Multimodal Functions. In 2009 IEEE Congress on Evolutionary Computation (CEC’2009), pages 3164–3171, Trondheim, Norway, May 2009. IEEE Press.

[5490] Juan Li, Jie Chen, Bin Xin, and LiHua Dou. Solving Multi-Objective Multi-Stage Weapon Target Assignment Problem via Adaptive NSGA-II and Adaptive MOEA/D: A Comparison Study. In 2015 IEEE Congress on Evolutionary Computation (CEC’2015), pages 3132–3139, Sendai, Japan, 25-28 May 2015. IEEE Press. ISBN 978-1-4799-7492-4.

[5491] Jun Li. Compromise Approach-Based Genetic Algorithm for Constrained Multiobjective Portfolio Selection Model. In Yong Shi, Shouyang Wang, Yi Peng, Jianping Li, and Yong Zeng, editors, Cutting-Edge Research Topics on Multiple Criteria Decision Making (MCDM’2009), pages 697–704. Springer, Communications in Computer and Information Science, Vol. 35, Heidelberg, Germany, 2009.

[5492] Jun Li and Bo Gao. On Chance Maximization Model in Fuzzy Random Decision Systems. Mathematical and Computer Modelling, 50(3-4):453–464, August 2009.

[5493] Jun Li, Huping Xu, and Mitsuo Gen. A Class of Multiobjective Linear Programming Model with Fuzzy Random Coefficients. Mathematical and Computer Modelling, 44(11-12):1097–1113, December 2006.

[5494] Jun Li, Xiaoyong Yang, Chengzu Ren, Guang Chen, and Yan Wang. Multiobjective Optimization of Cutting Parameters in Ti-6Al-4V milling Process Using Nondominated Sorting Genetic Algorithm-II. International Journal of Advanced Manufacturing Technology, 76(5-8):941–953, February 2015.

[5495] Jun-Fang Li, Bu-Han Zhang, Yi-Fang Lui, Kui Wang, and Xiao-Shan Wu. Spatial Evolution Character of Multi-objective Evolutionary Algorithm Based on Self-organized Criticality Theory. Physica A-Statistical Mechanics and its Applications, 391(22):5490–5499, November 15 2012.

[5496] Jun-Qing Li, Quan-Ke Pan, and Kai-Zhou Gao. Pareto-based Discrete Artificial Bee Colony Algorithm for Multi-objective Flexible Job Shop Scheduling Problems. International Journal of Advanced Manufacturing Technology, 55(9-12):1159–1169, August 2011.

[5497] Jun-Qing Li, Quan-Ke Pan, and M. Fatih Tasgetiren. A Discrete Artificial Bee Colony Algorithm for the Multi-objective Flexible Job-shop Scheduling Problem with Maintenance Activities. Applied Mathematical Modelling, 38(3):1111–1132, February 1 2014.

[5498] Junfeng Li and Wenzhan Dai. Multi-objective Decision Algorithm Based on Adaptive Genetic Algorithm and Grey Relation Degree. In 2006 9th International Conference on Control, Automation, Robotics and Vision, Vols 1-5, pages 70–74, Singapore, Singapore, December 05-08 2006. IEEE. ISBN 978-1-4244-0341-7.

[5499] Junfeng Li and Wenzhan Dai. Research of Solving Method Against Multiobjective Problem Based on Adaptive Genetic Algorithm and Grey Relation Degree. In D. Z. Cheng and G. R. Duan, editors, 2006 Chinese Control Conference, Vols 1-5, pages 1771–1774, Harbin, China, August 07-10 2006. IEEE. ISBN 978-7-81077-802-2.

[5500] Junfeng Li and Wenzhan Dai. Multi-objective Evolutionary Algorithm Based on Correlativity and its Application. In 2008 7th World Congress on Intelligent Control and Automation, Vols 1-3, pages 7481–7486, Chongqing, China, June 25-27 2008. IEEE. ISBN 978-1-4244-2113-8.

[5501] Junfeng Li and Wenzhan Dai. Multi-objective Evolutionary Algorithm Based on Included Angle Cosine and its Application. In 2008 International Conference on Information and Automation, Vols 1-4, pages 1045–1049, Changsha, China, June 20-23 2008. IEEE. ISBN 978-1-4244-2183-1.

[5502] Junfeng Li, Wenzhan Dai, and Ye Yang. Multi-Objective Genetic Algorithm Based on the Correlation Coefficient and its Application. In 20th Chinese Control and Decision Conference, pages 3898–3902, Yantai, China, July 02-04 2008. IEEE. ISBN 978-1-4244-1733-9.

[5503] Junqing Li and Quanke Pan. A Hybrid Pareto-based Local Search for Multiobjective Flexible Job Shop Scheduling Problem. In 2010 IEEE Congress on Evolutionary Computation (CEC’2010), pages 316–320, Barcelona, Spain, July 18-23 2010. IEEE Press.

[5504] Junqing Li, Quanke Pan, and Shengxian Xie. An Effective Shuffled Frogleaping Algorithm for Multi-objective Flexible Job Shop Scheduling Problems. Applied Mathematics and Computation, 218(18):9353–9371, May 15 2012.

[5505] Junqing Li, Quanke Pan, Shengxian Xie, and Jing Liang. A Hybrid Pareto-Based Tabu Search for Multi-objective Flexible Job Shop Scheduling Problem with E/T Penalty. In Ying Tan, Yuhui Shi, and Kay Chen Tan, editors, Advances in Swarm Intelligence, First International Conference, ICSI 2010, pages 620–627. Springer. Lecture Notes in Computer Science Vol. 6145, Beijing, China, June 12-15 2010.

[5506] Kangshun Li, Weifeng Pan, Wensheng Zhang, and Zhangxin Chen. A Sequence Cipher Producing Method Based on Two-Layer Ranking Multi-Objective Evolutionary Algorithm. In 2008 Congress on Evolutionary Computation (CEC’2008), pages 169–173, Hong Kong, June 2008. IEEE Service Center.

[5507] Ke Li, Álvaro Fialho, and Sam Kwong. Multi-Objective Differential Evolution with Adaptive Control of Parameters and Operators. In Carlos A. Coello Coello, editor, Learning and Intelligent Optimization, 5th International Conference, LION 5, pages 473–487, Rome, Italy, January 17-21 2011. Springer. Lecture Notes in Computer Science Vol. 6683.

[5508] Ke Li, Kalyanmoy Deb, and Qingfu Zhang. Evolutionary Multiobjective Optimization with Hybrid Selection Principles. In 2015 IEEE Congress on Evolutionary Computation (CEC’2015), pages 900–907, Sendai, Japan, 25-28 May 2015. IEEE Press. ISBN 978-1-4799-7492-4.

[5509] Ke Li, Kalyanmoy Deb, Qingfu Zhang, and Sam Kwong. An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition. IEEE Transactions on Evolutionary Computation, 19(5):694–716, October 2015.

[5510] Ke Li, Alvaro Fialho, Sam Kwong, and Qingfu Zhang. Adaptive Operator Selection with Bandits for a Multiobjective Evolutionary Algorithm Based on Decomposition. IEEE Transactions on Evolutionary Computation, 18(1):114–130, February 2014.

[5511] Ke Li and Sam Kwong. A General Framework for Evolutionary Multiobjective Optimization Via Manifold Learning. Neurocomputing, 146:65–74, December 25 2014.

[5512] Ke Li, Sam Kwong, Jingjing Cao, Miqing Li, Jinhua Zheng, and Ruimin Shen. Achieving Balance Between Proximity and Diversity in Multi-objective Evolutionary Algorithm. Information Sciences, 182(1):220–242, January 1 2012.

[5513] Ke Li, Sam Kwong, and Kalyanmoy Deb. A Dual-population Paradigm for Evolutionary Multiobjective Optimization. Information Sciences, 309:50–72, July 10 2015.

[5514] Ke Li, Sam Kwong, Ran Wang, Jingjing Cao, and Imre J. Rudas. Multi-Objective Differential Evolution with Self-Navigation. In Proceedings 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 508–513, Seoul, South Korea, October 14-17 2012. IEEE. ISBN 978-1-4673-1714-6.

[5515] Ke Li, Sam Kwong, Ran Wang, Kit-Sang Tang, and Kim-Fung Man. Learning Paradigm Based on Jumping Genes: A General Framework for Enhancing Exploration in Evolutionary Multiobjective Optimization. Information Sciences, 226:1–22, March 20 2013.

[5516] Ke Li, Sam Kwong, Qingfu Zhang, and Kalyanmoy Deb. Interrelationship-Based Selection for Decomposition Multiobjective Optimization. IEEE Transactions on Cybernetics, 45(10):2076–2088, October 2015.

[5517] Ke Li, Mohammad Nabi Omidvar, Kalyanmoy Deb, and Xin Yao. Variable Interaction in Multi-Objective Optimization Problems. In Julia Handl, Emma Hart, Peter R. Lewis, Manuel L’opez-Ib’añez, Gabriela Ochoa, and Ben Paechter, editors, Parallel Problem Solving from Nature – PPSN XIV, 14th International Conference, pages 399–409. Springer. Lecture Notes in Computer Science Vol. 9921, Edinburgh, UK, September 17-21 2016. ISBN 978-3-319-45822-9.

[5518] Ke Li, Qingfu Zhang, Sam Kwong, Miqing Li, and Ran Wang. Stable Matching-Based Selection in Evolutionary Multiobjective Optimization. IEEE Transactions on Evolutionary Computation, 18(6):909–923, December 2014.

[5519] Ke Li, Jinhua Zheng, Miqing Li, Cong Zhou, and Hui Lv. A Novel Algorithm for Non-dominated Hypervolume-based Multiobjective Optimization. In 2009 IEEE International Conference on Systems, Man and Cybernetics (SMC 2009), pages 5220–5226, San Antonio, Texas, USA, 11-14 October 2009. IEEE Press. ISBN 978-1-4244-2793-2.

[5520] Kejing Li and Xiaobing Zhang. Multi-Objective Optimization of Interior Ballistic Performance Using NSGA-II. Propellants Explosives Pyrotechnics, 36(3):282–290, June 2011.

[5521] Kejing Li and Xiaobing Zhang. Using NSGA-II and TOPSIS Methods for Interior Ballistic Optimization Based on One-Dimensional Two-Phase Flow Model. Propellants Explosives Pyrotechnics, 37(4):468–475, August 2012.

[5522] L. Li and G. Q. Huang. Multiobjective Evolutionary Optimisation for Adaptive Product Family Design. International Journal of Computer Integrated Manufacturing, 22(4):299–314, 2009.

[5523] Li Li, Li Hong-Qi, and Xie Shao-Long. Particle Swarm Multi optimizer for Locating all Local Solutions. In 2008 Congress on Evolutionary Computation (CEC’2008), pages 1040–1046, Hong Kong, China, June 2008. IEEE Service Center.

[5524] Li Li, F. Qiao, and Q.D. Wu. ACO-based Multi-objective Scheduling of Parallel Batch Processing Machines with Advanced Process Control Constraints. International Journal of Advanced Manufacturing Technology, 44(9-10):985– 994, October 2009.

[5525] Li Li, Pengyi Yang, Ling Ou, Zili Zhang, and Peng Cheng. Genetic Algorithm-Based Multi-objective Optimisation for QoS-Aware Web Services Composition. In Yaxin Bi and Mary-Anne Williams, editors, Knowledge Science, Engineering and Management, 4th International Conference, KSEM 2010, pages 549–554. Springer. Lecture Notes in Artificial Intelligence Vol. 6291, Belfast, Northern Ireland, UK, September 1-3 2010.

[5526] Lijuan Li and Feng Liu. Group Search Optimization for Applications in Structural Design. Springer-Verlag, Berlin, Germany, 2011. ISBN 978-3-642-20535-4.

[5527] Lily D. Li, Xiaodong Li, and Xinghuo Yu. A Multi-Objective Constraint-Handling Method with PSO Algorithm for Constrained Engineering Optimization Problems. In 2008 Congress on Evolutionary Computation (CEC’2008), pages 1528–1535, Hong Kong, China, June 2008. IEEE Service Center.

[5528] Lily D. Li, Xiaodong Li, and Xinghuo Yu. Power Generation Loading Optimization using a Multi-Objective Constraint-Handling Method via PSO Algorithm. In 2008 6th IEEE International Conference on Industrial Informatics, Vols 1-3, pages 1530–1535, Daejeon, South Korea, July 13-16 2008. IEEE. ISBN 978-1-4244-2170-1.

[5529] Lin Li, Xin Yao, Rustam Stolkin, Maoguo Gong, and Shan He. An Evolutionary Multiobjective Approach to Sparse Reconstruction. IEEE Transactions on Evolutionary Computation, 18(6):827–845, December 2014.

[5530] Lingbo Li, Mark Harman, Emmanuel Letier, and Yuanyuan Zhang. Robust Next Release Problem: Handling Uncertainty During Optimization. In 2014 Genetic and Evolutionary Computation Conference (GECCO 2014), pages 1247–1254, Vancouver, Canada, July 12-16 2014. ACM Press. ISBN 978-1-4503-2662-9.

[5531] Lingxi Li and Ke Tang. History-Based Topological Speciation for Multimodal Optimization. IEEE Transactions on Evolutionary Computation, 19(1):136– 150, February 2015.

[5532] M. Li, S. Azarm, N. Williams, S. Al Hashimi, A. Almansoori, and N. Al Qasas. Integrated Multi-objective Robust Optimization and Sensitivity Analysis with Irreducible and Reducible Interval Uncertainty. Engineering Optimization, 41(10):889–908, October 2009.

[5533] M. Li, G. Li, and S. Azarm. A Kriging Metamodel Assisted Multi-objective Genetic Algorithm for Design Optimization. Journal of Mechanical Design, 130(3), March 2008. Article Number: 031401.

[5534] Mian Li. Robust Optimization and Sensitivity Analysis with Multi-Objective Genetic Algorithms: Single- and Multidisciplinary Applications. PhD thesis, Department of Mechanical Engineering, University of Maryland, College Park, USA, 2007.

[5535] Mian Li, Shapour Azarm, and Vikrant Aute. A Multi-Objective Genetic Algorithm for Robust Design Optimization. In Hans-Georg Beyer et al., editor, 2005 Genetic and Evolutionary Computation Conference (GECCO’2005), volume 1, pages 771–778, New York, USA, June 2005. ACM Press.

[5536] Ming Li, Liya Wang, and Mingxing Wu. A Multi-objective Genetic Algorithm Approach for Solving Feature Addition Problem in Feature Fatigue Analysis. Journal of Intelligent Manufacturing, 24(6):1197–1211, December 2013.

[5537] Minqiang Li, Dan Lin, and Shouyang Wang. Solving a Type of Biobjective Bilevel Programming Problem Using NSGA-II. Computers & Mathematics with Applications, 59(2):706–715, January 2010.

[5538] Minqiang Li, Liu Liu, and Dan Lin. A Fast Steady-state Epsilon-dominance Multi-objective Evolutionary Algorithm. Computational Optimization and Applications, 48(1):109–138, January 2011.

[5539] Miqing Li. Evolutionary Many-Objective Optimisation: Pushing the Boundaries. PhD thesis, Department of Computer Science, Brunel University London, London, UK, December 2015.

[5540] Miqing Li, Shengxiang Yang, Ke Li, and Xiaohui Liu. Evolutionary Algorithms with Segment-Based Search for Multiobjective Optimization Problems. IEEE Transactions on Cybernetics, 44(8):1295–1313, August 2014.

[5541] Miqing Li, Shengxiang Yang, and Xiaohui Liu. A Test Problem for Visual Investigation of High-Dimensional Multi-Objective Search. In 2014 IEEE Congress on Evolutionary Computation (CEC’2014), pages 2140–2147, Beijing, China, 6-11 July 2014. IEEE Press. ISBN 978-1-4799-1488-3.

[5542] Miqing Li, Shengxiang Yang, and Xiaohui Liu. Diversity Comparison of Pareto Front Approximations in Many-Objective Optimization. IEEE Transactions on Cybernetics, 44(12):2568–2584, December 2014.

[5543] Miqing Li, Shengxiang Yang, and Xiaohui Liu. Shift-Based Density Estimation for Pareto-Based Algorithms in Many-Objective Optimization. IEEE Transactions on Evolutionary Computation, 18(3):348–365, June 2014.

[5544] Miqing Li, Shengxiang Yang, and Xiaohui Liu. A Performance Comparison Indicator for Pareto Front Approximations in Many-Objective Optimization. In 2015 Genetic and Evolutionary Computation Conference (GECCO 2015), pages 703–710, Madrid, Spain, July 11-15 2015. ACM Press. ISBN 978-1-4503-3472-3.

[5545] Miqing Li, Shengxiang Yang, and Xiaohui Liu. Bi-goal Evolution for Manyobjective Optimization Problems. Artificial Intelligence, 228:45–65, November 2015.

[5546] Miqing Li, Shengxiang Yang, and Xiaohui Liu. Pareto or Non-Pareto: Bi-Criterion Evolution in Multiobjective Optimization. IEEE Transactions on Evolutionary Computation, 20(5):645–665, October 2016.

[5547] Miqing Li, Shengxiang Yang, Xiaohui Liu, and Ruimin Shen. A Comparative Study on Evolutionary Algorithms for Many-Objective Optimization. In Robin C. Purshouse, Peter J. Fleming, Carlos M. Fonseca, Salvatore Greco, and Jane Shaw, editors, Evolutionary Multi-Criterion Optimization, 7th International Conference, EMO 2013, pages 261–275. Springer. Lecture Notes in Computer Science Vol. 7811, Sheffield, UK, March 19-22 2013.

[5548] Miqing Li, Shengxiang Yang, Xiaohui Liu, and Kang Wang. IPESA-II: Improved Pareto Envelope-Based Selection Algorithm II. In Robin C. Purshouse, Peter J. Fleming, Carlos M. Fonseca, Salvatore Greco, and Jane Shaw, editors, Evolutionary Multi-Criterion Optimization, 7th International Conference, EMO 2013, pages 143–155. Springer. Lecture Notes in Computer Science Vol. 7811, Sheffield, UK, March 19-22 2013.

[5549] Miqing Li, Shengxiang Yang, Jinhua Zheng, and Xiaohui Liu. ETEA: A Euclidean Minimum Spanning Tree-Based Evolutionary Algorithm for Multi-Objective Optimization. Evolutionary Computation, 22(2):189–230, 2014.

[5550] Miqing Li and Jinhua Zheng. Spread Assessment for Evolutionary Multi-Objective Optimization. In Matthias Ehrgott, Carlos M. Fonseca, Xavier Gandibleux, Jin-Kao Hao, and Marc Sevaux, editors, Evolutionary Multi-Criterion Optimization. 5th International Conference, EMO 2009, pages 216– 230. Springer. Lecture Notes in Computer Science Vol. 5467, Nantes, France, April 2009.

[5551] Miqing Li, Jinhua Zheng, Ke Li, Qizhao Yuan, and Ruimin Shen. Enhancing Diversity for Average Ranking Method in Evolutionary Many-Objective Optimization. In Robert Schaefer, Carlos Cotta, Joanna Ko�odziej, and Günter Rudolph, editors, Parallel Problem Solving from Nature–PPSN XI, 11th International Conference, Proceedings, Part I, pages 647–656. Springer, Lecture Notes in Computer Science Vol. 6238, Kraków, Poland, September 2010.

[5552] Miqing Li, Jinhua Zheng, Ruimin Shen, Ke Li, and Qizhao Yuan. A Grid-Based Fitness Strategy for Evolutionary Many-Objective Optimization. In Proceedings of the 12th annual conference on Genetic and Evolutionary Computation (GECCO’2010), pages 463–470, Portland, Oregon, USA, July 7–11 2010. ACM Press. ISBN 978-1-4503-0072-8.

[5553] Miqing Li, Jinhua Zheng, and Jun Wu. Improving NSGA-II Algorithm Based on Minimum Spanning Tree. In Xiaodong Li, Michael Kirley, Mengjie Zhang, David Green, Vic Ciesielski, Hussein Abbass, Zbigniew Michalewicz, Tim Hendtlass, Kalyanmoy Deb, Kay Chen Tan, Jürgen Branke, and Yuhui Shi, editors, Simulated Evolution and Learning, 7th International Conference, SEAL 2008, pages 170–179, Melbourne, Australia, December 7-10 2008. Springer. Lecture Notes in Computer Science, Vol. 5361.

[5554] Miqing Li, Jinhua Zheng, and Guixia Xiao. An Efficient Multi-Objective Evolutionary Algorithm Based on Minimum Spanning Tree. In IEEE Congress on Evolutionary Computation, pages 617–624, Hong Kong, China, June 2008. IEEE Service Center.

[5555] Miqing Li, Jinhua Zheng, and Guixia Xiao. Uniformity Assessment for Evolutionary Multi-Objective Optimization. In 2008 Congress on Evolutionary Computation (CEC’2008), pages 625–632, Hong Kong, China, June 2008. IEEE Service Center.

[5556] M.S. Li, T.Y. Ji, Q.H. Wu, and P.Z. Wu. Stochastic Economic Dispatch With Solar Farm Integrated by Bacterial Swarm Algorithm. In 2015 IEEE Congress on Evolutionary Computation (CEC’2015), pages 3383–3388, Sendai, Japan, 25-28 May 2015. IEEE Press. ISBN 978-1-4799-7492-4.

[5557] Qian Li, Linyan Sun, and Liang Bao. Enhanced Index Tracking Based on Multi-objective Immune Algorithm. Expert Systems with Applications, 38(5):6101–6106, May 2011.

[5558] R. Li, R. Etemaadi, M.T.M Emmerich, and M.R.V Chaudron. An Evolutionary Multiobjective Optimization Approach to Component-Based Software Architecture Design. In 2011 IEEE Congress on Evolutionary Computation (CEC’2011), pages 432–439, New Orleans, Louisiana, USA, 5-8 June 2011. IEEE Service Center.

[5559] Ran Li, Huizhuo Ma, Feifei Wang, Yihe Wang, Yang Liu, and Zenghui Li. Game Optimization Theory and Application in Distribution System Expansion Planning, Including Distributed Generation. Energies, 6(2):1101–1124, February 2013.

[5560] Rong Li, Timothy R. Mersch, Oriana X. Wen, Assem Kaylani, and Georgios C. Anagnostopoulos. Multi-objective Memetic Evolution of ARTbased Classifiers. In 2010 IEEE Congress on Evolutionary Computation (CEC’2010), pages 3353–3360, Barcelona, Spain, July 18–23 2010. IEEE Press.

[5561] Rui Li, Jeroen Eggermont, Ofer M. Shir, Michael T. M. Emmerich, Thomas Bäck, Jouke Dijkstra, and Johan H. C. Reiber. Mixed-Integer Evolution Strategies with Dynamic Niching. In Günter Rudolph, Thomas Jansen, Simon Lucas, Carlo Poloni, and Nicola Beume, editors, Parallel Problem Solving from Nature–PPSN X, pages 246–255. Springer. Lecture Notes in Computer Science Vol. 5199, Dortmund, Germany, September 2008.

[5562] Rui Li, Yirong Guo, Yujuan Xing, and Ming Li. A Novel Multi-Swarm Particle Swarm Optimization algorithm Applied in Active Contour Model. In S.M. Zhou and W. Wang, editors, Proceedings of The 2009 WRI Global Congress on Intelligent Systems, pages 139–143, Xiamen, China, May 19-21 2009. IEEE Computer Society Press. ISBN 978-0-7695-3571-5.

[5563] Seereeram Li, Ravichandran Mehra, Robert Smith, and Randal Beard. Multispacecraft Trajectory Optimization and Control using Genetic Algorithm Techniques. In IEEE Aerospace Conference Proceedings, volume 7, pages 99–108. IEEE Press, 2000.

[5564] Shaobo Li, Xin Ma, Qin Li, and Guanci Yang. Multi-Objective Evolutionary Algorithm Based on Improved Clonal Selection. In Yuanxu Yu, Zhengtao Yu, and Jingying Zhao, editors, Computer Science for Environmental Engineering and EcoInformatics, International Workshop, CSEEE 2011, pages 218–223. Springer. Communications in Computer and Information Science Vol. 159, Kunming, China, July 29-31 2011.

[5565] Sheng-Tun Li, Chih-Chuan Chen, and Jian Wei Li. A Multi-objective Particle Swarm Optimization Algorithm for Rule Discovery. In Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP 2007), pages 597–600, Kaohsiung, Taiwan, November 26-28 2007. IEEE Computer Society.

[5566] Wei Li, Lei Wang, Bin Wang, Rong Fei, and Xinghong Hei. Cloud Droplets Evolution Algorithm for Multi-objective Optimization. In 2013 25th Chinese Control and Decision Conference (CCDC), pages 681–686, Guiyang, China, May 25-27 2013. IEEE. ISBN 978-1-4673-5532-2.

[5567] Weidong Li, Lihui Wang, Xinyu Li, and Liang Gao. Intelligent Optimisation for Integrated Process Planning and Scheduling. In Lihui Wang, Amos H.C. Ng, and Kalyanmoy Deb, editors, Multi-objective Evolutionary Optimisation for Product Design and Manufacturing, chapter 10, pages 305–324. Springer, London, UK, 2011. ISBN 978-0-85729-617-7.

[5568] Weihong Li, Lijuan Liu, and Weiguo Gong. Multi-objective Uniform Design as a SVM Model Selection Tool for Face Recognition. Expert Systems with Applications, 38(6):6689–6695, June 2011.

[5569] Weiqi Li. Finding Pareto-Optimal Set by Merging Attractors for a Biobjective Traveling Salesmen Problem. In Carlos A. Coello Coello, Arturo Hernández Aguirre, and Eckart Zitzler, editors, 3rd International Conference on Evolutionary Multi-Criterion Optimization (EMO 2005), pages 797–810, Guanajuato, México, March 2005. Springer. Lecture Notes in Computer Science Vol. 3410.

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[5781] Min Liu. Development of Multiobjective Optimization Procedures for Seismic Design of Steel Moment Frame Structures. PhD thesis, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA, 2003.

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[5783] Min Liu, Jinhua Zheng, Junnian Wang, Yuzhen Liu, and Lei Jiang. An Adaptive Diversity Introduction Method for Dynamic Evolutionary Multiobjective Optimization. In 2014 IEEE Congress on Evolutionary Computation (CEC’2014), pages 3160–3167, Beijing, China, 6-11 July 2014. IEEE Press. ISBN 978-1-4799-1488-3.

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[6894] Anh Nguyen, Tommaso Urli, and Markus Wagner. Single- and Multi-Objective Genetic Programming: New Bounds for Weighted ORDER and MAJORITY. In Frank Neumann and Kenneth De Jong, editors, Proceedings of the 2013 ACM Workshop on Foundations of Genetic Algorithms (FOGA XII), pages 161–172. ACM Press, Adelaide, Australia, January 16–20 2013.

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[6896] Anh Quang Nguyen, Andrew M. Sutton, and Frank Neumann. Population Size Matters: Rigorous Runtime Results for Maximizing the Hypervolume Indicator. In 2013 Genetic and Evolutionary Computation Conference (GECCO’2013), pages 1613–1620, New York, USA, July 2013. ACM Press. ISBN 978-1-4503-1963-8.

[6897] Anh Quang Nguyen, Markus Wagner, and Frank Neumann. User Preferences for Approximation-Guided Multi-objective Evolution. In Grant Dick, Will N. Browne, Peter Whigham, Mengjie Zhang, Lam Thu Bui, Hisao Ishibuchi, Yaochu Jin, Xiaodong Li, Yuhui Shi, Pramod Singh, Kay Chen Tan, and Ke Tang, editors, Simulated Evolution and Learning, 10th International Conference, SEAL 2014, pages 251–262. Springer. Lecture Notes in Computer Science Vol. 8886, Dunedin, New Zealand, December 15-18 2014.

[6898] Khai Phuc Nguyen, Goro Fujita, and Vo Ngoc Dieu. Optimal placement and sizing of Static Var Compensator using Cuckoo search algorithm. In 2015 IEEE Congress on Evolutionary Computation (CEC’2015), pages 267–274, Sendai, Japan, 25-28 May 2015. IEEE Press. ISBN 978-1-4799-7492-4.

[6899] Long Nguyen, Lam Thu Bui, and Hussein Abbass. A New Niching Method for the Direction-Based Multi-Objective Evolutionary Algorithm. In Proceedings of the 2013 IEEE Symposium on Computational Intelligence in Multicriteria Decision Making (MCDM’2013), pages 1–8, Singapore, April 16–19 2013. IEEE Press.

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[6901] Minh Luan Nguyen, Siu Cheung Hui, and Alvis C.M. Fong. An Efficient Multi-Objective Optimization Approach for Online Test Paper Generation. In 2011 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM’2011), pages 182–189, Paris, France, April 11–15 2011. IEEE Press. ISBN 978-1-61284-067-3.

[6902] Quang Uy Nguyen, Xuan Hoai Nguyen, Michael O’Neill, and Alexandros Agapitos. An Investigation of Fitness Sharing with Semantic and Syntactic Distance Metrics. In Alberto Moraglio, Sara Silva, Krzysztof Krawiec, Penousal Machado, and Carlos Cotta, editors, Genetic Programming, 15th European Conference, EuroGP 2012, pages 109–120, Málaga, Spain, April 11-13 2012. Springer. Lecture Notes in Computer Science Vol. 7244.

[6903] Su Nguyen, Mengjie Zhang, Mark Johnston, and Kay Chen Tan. A coevolution genetic programming method to evolve scheduling policies for dynamic multi-objective job shop scheduling problems. In 2012 IEEE Congress on Evolutionary Computation (CEC’2012), pages 3261–3268, Brisbane, Australia, June 10-15 2012. IEEE Press.

[6904] Su Nguyen, Mengjie Zhang, Mark Johnston, and Kay Chen Tan. Learning Reusable Initial Solutions for Multi-objective Order Acceptance and Scheduling Problems with Genetic Programming. In Krzysztof Krawiec, Alberto Moraglio, Ting Hu, A. Şima Etaner-Uyar, and Bin Hu, editors, Genetic Programming, 16th European Conference, EuroGP 2013, pages 157–168. Springer. Lecture Notes in Computer Science Vol. 7831, Vienna, Austria, April 3-5 2013.

[6905] Su Nguyen, Mengjie Zhang, Mark Johnston, and Kay Chen Tan. Automatic Design of Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling via Cooperative Coevolution Genetic Programming. IEEE Transactions on Evolutionary Computation, 18(2):193–208, April 2014.

[6906] Su Nguyen, Mengjie Zhang, and Kay Chen Tan. Enhancing Genetic Programming Based Hyper-Heuristics for Dynamic Multi-Objective Job Shop Scheduling Problems. In 2015 IEEE Congress on Evolutionary Computation (CEC’2015), pages 2781–2788, Sendai, Japan, 25-28 May 2015. IEEE Press. ISBN 978-1-4799-7492-4.

[6907] Ji Ni and Peter Rockett. Tikhonov Regularization as a Complexity Measure in Multiobjective Genetic Programming. IEEE Transactions on Evolutionary Computation, 19(2):157–166, April 2015.

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[6911] Christos A. Nicolaou, Joannis Apostolakis, and Costas S. Pattichis. De Novo Drug Using Multiobjective Evolutionary Graphs. Journal of Chemical Information and Modeling, 49(2):295–307, February 2009.

[6912] Matteo Nicolini. Evaluating performance of multi-objective genetic algorithms for water distribution system optimization. In Liong et al., editor, Hydroinformatics, pages 850–857. World Scientific, 2004.

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[6927] Taher Niknam. A New Hybrid Algorithm for Multi-Objective Distribution Feeder Reconfiguration. Cybernetics and Systems, 40(6):508–527, 2009.

[6928] Taher Niknam. An efficient hybrid evolutionary algorithm based on PSO and HBMO algorithms for multi-objective Distribution Feeder Reconfiguration. Enegy Conversion and Management, 50(8):2074–2082, August 2009.

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[6936] Taher Niknam, Abdollah Kavousi Fard, and Alireza Seifi. Distribution feeder reconfiguration considering fuel cell/wind/photovoltaic power plants. Renewable Energy, 37(1):213–225, January 2012.

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[7277] Han-Young Park, Akhil Datta-Gupta, and Michael J. King. Handling Conflicting Multiple Objectives Using Pareto-based Evolutionary Algorithm During History Matching of Reservoir Performance. Journal of Petroleum Science and Engineering, 125:48–66, January 2015.

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[8781] Ankur Sinha. Bilevel Multi-objective Optimization Problem Solving Using Progressively Interactive EMO. In Ricardo H.C. Takahashi, Kalyanmoy Deb, Elizabeth F. Wanner, and Salvatore Grecco, editors, Evolutionary Multi-Criterion Optimization, 6th International Conference, EMO 2011, pages 269– 284, Ouro Preto, Brazil, April 2011. Springer. Lecture Notes in Computer Science Vol. 6576.

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[8784] Ankur Sinha, Pekka Korhonen, Jyrki Wallenius, and Kalyanmoy Deb. An Interactive Evolutionary Multi-Objective Optimization Algorithm with a Limited Number of Decision Maker Calls. European Journal of Operational Research, 233(3):674–688, March 16 2014.

[8785] Ankur Sinha, Pekka Malo, and Kalyanmoy Deb. Towards Understanding Bilevel Multi-objective Optimization with Deterministic Lower Level Decisions. In António Gaspar-Cunha, Carlos Henggeler Antunes, and Carlos Coello Coello, editors, Evolutionary Multi-Criterion Optimization, 8th International Conference, EMO 2015, pages 426–443. Springer. Lecture Notes in Computer Science Vol. 9018, Guimarães, Portugal, March 29 - April 1 2015.

[8786] Ankur Sinha, Pekka Malo, and Kalyanmoy Deb. Transportation Policy Formulation as a Multi-objective Bilevel Optimization Problem. In 2015 IEEE Congress on Evolutionary Computation (CEC’2015), pages 1651– 1658, Sendai, Japan, 25-28 May 2015. IEEE Press. ISBN 978-1-4799-7492-4.

[8787] Ankur Sinha, Pekka Malo, and Kalyanmoy Deb. Approximated Set-Valued Mapping Approach for Handling Multiobjective Bilevel Problems. Computers & Operations Research, 77:194–209, January 2017.

[8788] Ankur Sinha, Pekka Malo, and Kalyanmoy Deb. Evolutionary Bilevel Optimization: An Introduction and Recent Advances. In Slim Bechikh, Rituparna Datta, and Abhishek Gupta, editors, Recent Advances in Evolutionary Multi-objective Optimization, pages 71–103. Springer International Publishing, Switzerland, 2017. ISBN 978-3-319-42977-9.

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[9722] Lingfeng Wang and Chanan Singh. Multi-Objective Stochastic Power Dispatch Through A Modified Particle Swarm Optimization Algorithm. In 2006 Swarm Intelligence Symposium (SIS’06), pages 128–135, Indianapolis, Indiana, USA, May 2006. IEEE Press.

[9723] Lingfeng Wang and Chanan Singh. Environmental/economic Power Dispatch Using a Fuzzified Multi-objective Particle Swarm Optimization Algorithm. Electric Power Systems Research, 77(12):1654–1664, October 2007.

[9724] Lingfeng Wang and Chanan Singh. PSO-Based Multi-Criteria Optimum Design of A Grid-Connected Hybrid Power System with Multiple Renewable Sources of Energy. In Proceedings of the 2007 IEEE Swarm Intelligence Symposium (SIS 2007), pages 250–257, Honolulu, Hawaii, USA, April 2007. IEEE Press.

[9725] Lingfeng Wang and Chanan Singh. Stochastic Combined Heat and Power Dispatch Based on Multi-objective Particle Swarm Optimization. International Journal of Electrical Power & Energy Systems, 30(3):226–234, March 2008.

[9726] Lingfeng Wang and Chanan Singh. Reserve-constrained Multiarea Environmental/economic Dispatch Based on Particle Swarm Optimization with Local Search. Engineering Applications of Artificial Intelligence, 22(2):298–307, March 2009.

[9727] Lingfeng Wang and Chanan Singh. Risk and Cost Tradeoff in Economic Dispatch Including Wind Power Penetration Based on Multi-Objective Memetic Particle Swarm Optimization. In Chi-Keong Goh, Yew-Soon Ong, and Kay Chen Tan, editors, Multi-Objective Memetic Algorithms, chapter 10, pages 209–230. Springer, Studies in Computational Intelligence, Vol. 171, Berlin, Germany, 2009. ISBN 978-3-540-88050-9.

[9728] Lingjuan Wang, Chengjian Wei, and Shuai Huang. Computing Nash Equilibria with Particle Swarm Optimization Algorithm. Dynamics of Continuous Discrete and Impulsive System-Series B-Applications & Algorithms, 13:26– 30, December 2006.

[9729] Lingling Wang and Yuanxiang Li. Dynamical Multi-objective Optimization Using Evolutionary Algorithm for Engineering. In Zhihua Cai, Chengyu Hu, Zhuo Kang, and Yong Liu, editors, Advances in Computation and Intelligence, 5th International Symposium, ISICA 2010, pages 304–311. Springer. Lecture Notes in Computer Science Vol. 6382, Wuhan, China, October 22-24 2010.

[9730] Long Wang, Tong guang Wang, and Yuan Luo. Improved Non-dominated Sorting Genetic Algorithm (NSGA)-II in Multi-objective Optimization Studies of Wind Turbine Blades. Applied Mathematics and Mechanics-English Edition, 32(6):739–748, June 2011.

[9731] Luyi Wang, Hiroyuki Ishida, Tomoyuki Hiroyasu, and Mitsunori Miki. Examination of Multi-Objective Optimization Method for Global Search Using DI-RECT and GA. In 2008 Congress on Evolutionary Computation (CEC’2008), pages 2446–2451, Hong Kong, China, June 2008. IEEE Service Center.

[9732] Maocai Wang, Yun Wu, Guangming Dai, and Hanping Hu. A Improved NSGA-II Algorithm for Constrained Multi-objective Optimization Problems. In S. Zeng, Y. Liu, Q. Zhang, and L. Kang, editors, Progress in Intelligence Computation and Applications, Proceedings, pages 117–119, Wuhan, China, September 21-23 2007. China Univ GeosCiences Press. ISBN 978-7-5625-2204-1.

[9733] Mingna Wang, Dayong Qin, Chuiyu Lu, and Yunpeng Li. Modeling Anthropogenic Impacts and Hydrological Processes on a Wetland in China. Water Resources Management, 24(11):2743–2757, September 2010.

[9734] N. Wang and K. Tai. A Hybrid Genetic Algorithm for Multiobjective Structural Optimization. In 2007 IEEE Congress on Evolutionary Computation (CEC’2007), pages 2948–2955, Singapore, September 2007. IEEE Press.

[9735] N. Wang and K. Tai. Handling Objectives as Adaptive Constraints for Multiobjective Structural Optimization. In 2007 IEEE Congress on Evolutionary Computation (CEC’2007), pages 3922–3929, Singapore, September 2007. IEEE Press.

[9736] N. F. Wang and K. Tai. Target Matching Problems and an Adaptive Constraint Strategy for Multiobjective Design Optimization Using Genetic Algorithms. Computers & Structures, 88(19-20):1064–1076, October 2010.

[9737] N. F. Wang, Y. W. Yang, and K. Tai. Optimization of Structures Under Load Uncertainties Based on Hybrid Genetic Algorithm. In 2008 Congress on Evolutionary Computation (CEC’2008), pages 4039–4044, Hong Kong, China, June 2008. IEEE Service Center.

[9738] N. F. Wang and X. M. Zhang. Topology Optimization of Compliant Mechanisms Using Pairs of Curves. Engineering Optimization, 47(11):1497–1522, November 2 2015.

[9739] N. F. Wang, X. M. Zhang, and Y. W. Yang. A Hybrid Genetic Algorithm for Constrained Multi-objective Optimization Under Uncertainty and Target Matching Problems. Applied Soft Computing, 13(8):3636–3645, August 2013.

[9740] Nenzi Wang and Kuo-Chiang Cha. Multi-objective Optimization of Air Bearings Using Hypercube-dividing Method. Tribology International, 43(9):1631–1638, September 2010.

[9741] N.F. Wang and Y.W. Yang. Target Geometry Matching Problem for Hybrid Genetic Algorithm Used to Design Structures Subjected to Uncertainty. In 2009 IEEE Congress on Evolutionary Computation (CEC’2009), pages 1644–1651, Trondheim, Norway, May 2009. IEEE Press.

[9742] Nianfeng Wang and Kang Tai. A Structural Optimization Problem Formulation for Design of Compliant Gripper Using a Genetic Algorithm. In C. A. Motasoares, J. A. C. Martins, H. C. Rodrigues, Jorge A. C. Ambrósio, C. A. B. Pina, C. M. Motasoares, E. B. R. Pereira, and J. Folgado, editors, III European Conference on Computational Mechanics. Solids, Structures and Coupled Problems in Engineering: Book of Abstracts, page 456, Lisbon, Portugal, June 5-8 2006. Springer. ISBN 978-1-4020-4994-1.

[9743] N.Z. Wang. Multi-criterion Optimization for Heel-toe Running. Journal of Biomechanics, 38(8):1712–1716, August 2005.

[9744] Pan Wang, Jianjian Zhang, Li Xu, Hong Wang, Shan Feng, and Haoshen Zhu. How to Measure Adaptation Complexity in Evolvable Systems - A New Synthetic Approach of Constructing Fitness Functions. Expert Systems with Applications, 38(8):10414–10419, August 2011.

[9745] Pu Wang, Michael Emmerich, Rui Li, Ke Tang, Thomas Baeck, and Xin Yao. Convex Hull-Based Multiobjective Genetic Programming for Maximizing Receiver Operating Characteristic Performance. IEEE Transactions on Evolutionary Computation, 19(2):188–200, April 2015.

[9746] Pu Wang, Ke Tang, Thomas Weise, E.P.K. Tsang, and Xin Yao. Multiobjective Genetic Programming for Maximizing ROC Performance. Neurocomputing, 125:102–118, February 11 2014.

[9747] Qi Wang, Michele Guidolin, Dragan Savic, and Zoran Kapelan. Two-Objective Design of Benchmark Problems of a Water Distribution System via MOEAs: Towards the Best-Known Approximation of the True Pareto Front. Journal of Water Resources Planning and Management, 141(3), March 2015. Article Number: 04014060.

[9748] Qi Wang, Dragan A. Savic, and Zoran Kapelan. Hybrid Metaheuristics for Multi-Objective Design of Water Distribution Systems. Journal of Hydroinformatics, 16(1):165–177, 2014.

[9749] Rui Wang. Preference-inspired Co-evolutionary Algorithms. PhD thesis, Department of Automatic Control and Systems Engineering, University of Sheffield, UK, December 2013.

[9750] Rui Wang, Peter J. Fleming, and Robin C. Purshouse. General Framework for Localised Multi-Objective Evolutionary Algorithms. Information Sciences, 258:29–53, February 10 2014.

[9751] Rui Wang, Robin C. Purshouse, and Peter J. Fleming. Local Preference-Inspired Co-Evolutionary Algorithms. In 2012 Genetic and Evolutionary Computation Conference (GECCO’2012), pages 513–520, Philadelphia, USA, July 2012. ACM Press. ISBN 978-1-4503-1177-9.

[9752] Rui Wang, Robin C. Purshouse, and Peter J. Fleming. On Finding Well-Spread Pareto Optimal Solutions by Preference-inspired Co-evolutionary Algorithm. In 2013 Genetic and Evolutionary Computation Conference (GECCO’2013), pages 695–702, New York, USA, July 2013. ACM Press. ISBN 978-1-4503-1963-8.

[9753] Rui Wang, Robin C. Purshouse, and Peter J. Fleming. Preference-inspired Coevolutionary Algorithm Using Adaptively Generated Goal Vectors. In 2013 IEEE Congress on Evolutionary Computation (CEC’2013), pages 916–923, Cancún, México, 20-23 June 2013. IEEE Press. ISBN 978-1-4799-0454-9.

[9754] Rui Wang, Robin C. Purshouse, and Peter J. Fleming. Preference-Inspired Coevolutionary Algorithms for Many-Objective Optimization. IEEE Transactions on Evolutionary Computation, 17(4):474–494, August 2013.

[9755] Rui Wang, Robin C. Purshouse, and Peter J. Fleming. ”Whatever Works Best for You”- A New Method for a Priori and Progressive Multi-objective Optimisation. In Robin C. Purshouse, Peter J. Fleming, Carlos M. Fonseca, Salvatore Greco, and Jane Shaw, editors, Evolutionary Multi-Criterion Optimization, 7th International Conference, EMO 2013, pages 337–351. Springer. Lecture Notes in Computer Science Vol. 7811, Sheffield, UK, March 19-22 2013.

[9756] Rui Wang, Robin C. Purshouse, Ioannis Giagkiozis, and Peter J. Fleming. The iPICEA-g: A New Hybrid Evolutionary Multi-criteria Decision Making Approach Using the Brushing Technique. European Journal of Operational Research, 243(2):442–453, June 1 2015.

[9757] Rui Wang, Qingfu Zhang, and Tao Zhang. Pareto Adaptive Scalarising Functions for Decomposition Based Algorithms. In António Gaspar-Cunha, Carlos Henggeler Antunes, and Carlos Coello Coello, editors, Evolutionary Multi-Criterion Optimization, 8th International Conference, EMO 2015, pages 248–262. Springer. Lecture Notes in Computer Science Vol. 9018, Guimarães, Portugal, March 29 - April 1 2015.

[9758] Rui Wang, Tao Zhang, and Bo Guo. An Enhanced MOEA/D Using Uniform Directions and a Pre-organization Procedure. In 2013 IEEE Congress on Evolutionary Computation (CEC’2013), pages 2390–2397, Cancún, México, 20-23 June 2013. IEEE Press. ISBN 978-1-4799-0454-9.

[9759] S. Wang and J.H. Dai. Solution of the Problem of Time-energy Minimization in Orbital Transfer Via the Improved SPEA. Chinese Astronomy and Astrophysics, 27(4):447–453, October-December 2003.

[9760] Shanfeng Wang, Maoguo Gong, Lijia Ma, Qing Cai, and Licheng Jiao. Decomposition Based Multiobjective Evolutionary Algorithm for Collaborative Filtering Recommender Systems. In 2014 IEEE Congress on Evolutionary Computation (CEC’2014), pages 672–679, Beijing, China, 6-11 July 2014. IEEE Press. ISBN 978-1-4799-1488-3.

[9761] Shen Wang and Mahdi Mahfouf. Efficient Multi-objective Optimization with Fitness Landscape – A Special Application to the Optimal Design of Alloysteels. In 2010 IEEE Congress on Evolutionary Computation (CEC’2010), pages 2060–2067, Barcelona, Spain, July 18-23 2010. IEEE Press.

[9762] Shuai Wang, Shaukat Ali, and Arnaud Gotlieb. Cost-effective Test Suite Minimization in Product Lines Using Search Techniques. Journal of Systems and Software, 103:370–391, May 2015.

[9763] Shuan Wang, Dapeng Hua, Zhiguo Zhang, Ming Li, Ke Yao, and Zhanyou Wen. Robust Controller Design for Main Steam Pressure Based on SPEA2. In De-Shuang Huang, Yong Gan, Prashan Premaratne, and Kyungsook Han, editors, Bio-Inspired Computing and Applications, 7th International Conference on Intelligent Computing, ICIC 2011, pages 176–182, Zhengzhou, China, August 11-14 2012. Springer. Lecture Notes in Computer Science Vol. 6840.

[9764] Shuyan Wang, Changwen Zheng, and Yuxin Wang. A Time-Fuel Optimal Algorithm for Spacecraft: Formation Reconfiguration. In 2007 IEEE Congress on Evolutionary Computation (CEC’2007), pages 994–998, Singapore, September 2007. IEEE Press.

[9765] Tianri Wang, Shunsheng Guo, and Yi Liu. Pareto Process Optimization of Product Development Project Using Bi-objective Hybrid Genetic Algorithm. Advances in Engineering Software, 65:12–22, November 2013.

[9766] W. M. Wang, R. Zmeureanu, and H. Rivard. Applying Multi-objective Genetic Algorithms in Green Building Design Optimization. Building and Environment, 40(11):1512–1525, November 2005.

[9767] Wenliang Wang. New Application of Multiobjective Optimization Algorithm: Optical Thin Film Design. In Proceedings of the Second International Joint Conference on Computational Sciences and Optimization (CSO 2009), pages 769–772, Sanya, Hainan, China, April 24-26 2009. IEEE Computer Society Press.

[9768] Wenliang Wang. Design of Nonpolarizing Antireflection Coating by Using Multiobjective Optimization Algorithm. Optik, 124(16):2482–2486, 2013.

[9769] W.L. Wang, X.J. Yang, G.X. Xu, and Y. Huang. Multi-objective Design Optimization of the Complete Valve System in an Adjustable Linear Hydraulic Damper. Proceedings of the Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science, 225(C3):679–699, 2011.

[9770] W.M. Wang, H. Rivard, and R. Zmeureanu. An Object-oriented Framework for Simulation-based Green Building Design Optimization with Genetic Algorithms. Advanced Engineering Informatics, 19(1):5–23, January 2005.

[9771] X. D. Wang, C. Hirsch, Sh. Kang, and C. Lacor. Multi-objective Optimization of Turbomachinery using Improved NSGA-II and Approximation Model. Computer Methods in Applied Mechanics and Engineering, 200(9-12):883– 895, 2011.

[9772] Xianpeng Wang and Lixin Tang. A PSO-Based Hybrid Multi-Objective Algorithm for Multi-Objective Optimization Problems. In Ying Tan, Yuhui Shi, Yi Chai, and Guoyin Wang, editors, Advances in Swarm Intelligence, Second International Conference, ICSI 2011, pages 26–33. Springer. Lecture Notes in Computer Science Vol. 6729, Chongqing, China, June 12-15 2011.

[9773] Xianpeng Wang and Lixin Tang. Multi-objective Optimization Using a Hybrid Differential Evolution Algorithm. In 2012 IEEE Congress on Evolutionary Computation (CEC’2012), pages 1084–1089, Brisbane, Australia, June 10-15 2012. IEEE Press.

[9774] Xianpeng Wang and Lixin Tang. Multiobjective Operation Optimization of Naphtha Pyrolysis Process Using Parallel Differential Evolution. Industrial & Engineering Chemistry Research, 52(40):14415–14428, October 9 2013.

[9775] Xianpeng Wang and Lixin Tang. An Adaptive Multi-Population Differential Evolution Algorithm for Continuous Multi-Objective Optimization. Information Sciences, 348:124–141, June 20 2016.

[9776] Xiaojuan Wang, Liang Gao, Chaoyong Zhang, and Xinyu Shao. A Multiobjective Genetic Algorithm Based on Immune and Entropy Principle for Flexible Job-shop Scheduling Problem. The International Journal of Advanced Manufacturing Technology, 51(5-8):757–767, November 2010.

[9777] Xiaoqing Wang, Jiafu Tang, and Kai leung Yung. Optimization of the Multiobjective Dynamic Cell Formation Problem Using a Scatter Search Approach. International Journal of Advances Manufacturing Technology, 44(3-4):318– 329, September 2009.

[9778] Xingwei Wang, Pengcheng Liu, and Min Huang. Genetic Algorithm and Pareto Optimum Based QoS Multicast Routing Scheme in NGI. In Yuping Wang, Yiu ming Cheung, and Hailin Liu, editors, Computational Intelligence and Security, International Conference, CIS 2006, pages 115–122, Guangzhou, China, November 2007. Springer. Lecture Notes in Computer Science 4456.

[9779] X.L. Wang and M. Mahfouf. ACSAMO: An Adaptive Multiobjective Optimization Algorithm using the Clonal Selection Principle. In 2nd European Symposium on Nature-Inspired Smart Information Systems, Puerto de la Cruz, Tenerife, Spain, November 29–December 1 2006.

[9780] Xuesong Wang, Minglin Hao, Yuhu Cheng, and Ruhai Lei. PDE-PEDA: A New Pareto-Based Multi-objective Optimization Algorithm. Journal of Universal Computer Science, 15(4):722–741, 2009.

[9781] Y. B. Wang, P. T. Wu, X. N. Xao, J. L. Li, L. Lv, and H. B. Shao. The Optimization for Crop Planning and Some Advances for Water-Saving Crop Planning in the Semiarid Loess Plateau of China. Journal of Agronomy and Crop Science, 196(1):55–65, February 2010.

[9782] Yalin Wang, Xiaofang Chen, Weihua Gui, Chunhua Yang, Lou Caccetta, and Honglei Xu. A Hybrid Multiobjective Differential Evolution Algorithm and its Application to the Optimization of Grinding and Classification. Journal of Applied Mathematics, 2013. Article number: 841780.

[9783] Yan Wang and Jian chao Zeng. A Multi-objective Artificial Physics Optimization Algorithm Based on Ranks of Individuals. Soft Computing, 17(6):939– 952, June 2013.

[9784] Yan Wang, Jian-Chao Zeng, and Ying Tan. An Artificial Physics Optimization Algorithm for Multi-Objective Problems Based on Virtual Force Sorting Proceedings. In Bijaya Ketan Panigrahi, Swagatam Das, Ponnuthurai Nagaratnam Suganthan, and Subhransu Sekhar Dash, editors, Swarm, Evolutionary, and Memetic Computing, First International Conference on Swarm, Evolutionary and Memetic Computing, SEMCCO 2010, pages 615–622. Springer-Verlag. Lecture Notes in Computer Science Vol. 6466, Chennai, India, December 16-18 2010.

[9785] Yao Wang and Mark Wineberg. Estimation of Evolvability Genetic Algorithm and Dynamic Environments. Genetic Programming and Evolvable Machines, 7(4):355–382, December 2006.

[9786] Yao-Nan Wang, Liang-Hong Wu, and Xiao-Fang Yuan. Multi-objective Selfadaptive Differential Evolution with Elitist Archive and Crowding Entropybased Diversity Measure. Soft Computing, 14(3):193–209, February 2010.

[9787] Yaping Wang and Pham Hoang. A Multi-Objective Optimization of Imperfect Preventive Maintenance Policy for Dependent Competing Risk Systems with Hidden Failure. IEEE Transactions on Reliability, 60(4):770–781, December 2011.

[9788] Ye Wang, Hongongfu Zuo, and Defeng Lv. Improved Multiobjective Maintenance Optimization of Aircraft Equipment using Strength Pareto Genetic Algorithms with Immunity. In Fourth International Conference on Natural Computation (ICNC 2008), pages 621–624, Jinan, Shandong, China, October 18-20 2008. IEEE Computer Society Press.

[9789] Yen-Wen Wang, Chin-Yuan Fan, and Chen-Hao Liu. Applying Subpopulation Memetic Algorithm for Multi-objective Scheduling Problems. In 6th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2009), pages 376–379, Milan, Italy, July 2009. INSTICC.

[9790] Y.H. Wang, S.Y. Yang, G.Z. Ni, P.H. Ni, and S.L. Ho. An Emigration Genetic Algorithm for Vector Optimizations of Electromagnetic Devices. International Journal of Applied Electromagnetics and Mechanics, 19(1-4):103–109, 2004.

[9791] Yi Wang, Haozhong Cheng, Chun Wang, Zechun Hu, Liangzhong Yao, Zeliang Ma, and Zhonglie Zhu. Pareto Optimality-based Multi-objective Transmission Planning Considering Transmission Congestion. Electric Power Systems Research, 78(9):1619–1626, September 2008.

[9792] Yong Wang and Zixing Cai. A Constrained Optimization Evolutionary Algorithm Based on Multiobjective Optimization Techniques. In 2005 IEEE Congress on Evolutionary Computation (CEC’2005), volume 2, pages 1081– 1087, Edinburgh, Scotland, September 2005. IEEE Service Center.

[9793] Yong Wang and Zixing Cai. Combining Multiobjective Optimization with Differential Evolution to Solve Constrained Optimization Problems. IEEE Transactions on Evolutionary Computation, 16(1):117–134, February 2012.

[9794] Yong Wang, Zixing Cai, Guanqi Guo, and Yuren Zhou. Multiobjective Optimization and Hybrid Evolutionary Algorithm to Solve Constrained Optimization Problems. IEEE Transactions on Systems, Man and Cybernetics Part B–Cybernetics, 37(3):560–575, June 2007.

[9795] Yong Wang, Jian Xiang, and Zixing Cai. A Regularity Model-based Multiobjective Estimation of Distribution Algorithm with Reducing Redundant Cluster Operator. Applied Soft Computing, 12(11):3526–3538, November 2012.

[9796] Yu Wang and Bin Li. Investigation of Memory-Based Multi-Objective Optimization Evolutionary Algorithm in Dynamic Environment. In 2009 IEEE Congress on Evolutionary Computation (CEC’2009), pages 630–637, Trondheim, Norway, May 2009. IEEE Press.

[9797] Yu Wang, Bin Li, and Yunbi Chen. Digital IIR Filter Design Using Multiobjective Optimization Evolutionary Algorithm. Applied Soft Computing, 11(2):1851–1857, March 2011.

[9798] Yuan Wang, James Alfred Walker, Simon J. Bale, Martin A. Trefzer, and Andy M. Tyrrell. Two-phase Multiobjective Genetic Algorithm for Constrained Circuit Clustering on FPGAs. In 2015 IEEE Congress on Evolutionary Computation (CEC’2015), pages 1183–1190, Sendai, Japan, 25-28 May 2015. IEEE Press. ISBN 978-1-4799-7492-4.

[9799] Yuhuai Wang, Shiyou Yang, Guangzheng Ni, S.L. Ho, and Z.J. Liu. An Emigration Genetic Algorithm and its Application to Multiobjective Optimal Designs of Electromagnetic Devices. IEEE Transactions on Magnetics, 40(2):1240–1243, March 2004.

[9800] Yujia Wang and Yupu Yang. Handling Multiobjective Problems with a Novel Interactive Multi-Swarm PSO. In De-Shuang Huang, Donald C. Wunsch II, Daniel S. Levine, and Kang-Hyun Jo, editors, Advanced Intelligent Computing Theories and Applications with Aspects of Artificial Intelligence, 4th International Conference on Intelligent Computing, ICIC’2008, pages 575–582. Springer, Lecture Notes in Artificial Intelligence, Vol. 5227, Shanghai, China, September 15-18 2008. ISBN 978-3-540-85983-3.

[9801] Yujia Wang and Yupu Yang. Particle Swarm Optimization with Preference Order Ranking for Multi-objective Optimization. Information Sciences, 179(12):1944–1959, May 30 2009.

[9802] Yujia Wang and Yupu Yang. Particle Swarm with Equilibrium Strategy of Selection for Multi-objective Optimization. European Journal of Operational Research, 200(1):187–197, January 1 2010.

[9803] Yuping Wang and Chuangyin Dang. Improving Multiobjective Evolutionary Algorithm by Adaptive Fitness and Space Division. In Lipo Wang, Ke Chen, and Yew-Soon Ong, editors, Advances in Natural Computation. First International Conference, ICNC 2005, pages 392–398. Springer, Lecture Notes in Computer Science, Vol. 3612, Changsha, China, 2005.

[9804] Yuping Wang and Chuangyin Dang. An Evolutionary Algorithm for Dynamic Multi-objective Optimization. Applied Mathematics and Computation, 205(1):6–18, November 1 2008.

[9805] Yuping Wang, Chuangyin Dang, Hecheng Li, Lixia Han, and Jingxuan Wei. A Clustering Multi-objective Evolutionary Algorithm Based on Orthogonal and Uniform Design. In 2009 IEEE Congress on Evolutionary Computation (CEC’2009), pages 2927–2933, Trondheim, Norway, May 2009. IEEE Press.

[9806] Yuping Wang, Dalian Liu, and Yiu-Ming Cheung. Preference Bi-objective Evolutionary Algorithm for Constrained Optimization. In Computational Intelligence and Security. International Conference, CIS 2005, pages 184–191, Xi’an, China, December 2005. Springer, Lecture Notes in Artificial Intelligence Vol. 3801.

[9807] Zai Wang, Tianshi Chen, Ke Tang, and Xin Yao. A Multi-Objective Approach to Redundancy Allocation Problem in Parallel-Series Systems. In 2009 IEEE Congress on Evolutionary Computation (CEC’2009), pages 582–589, Trondheim, Norway, May 2009. IEEE Press.

[9808] Zai Wang, Ke Tang, and Xin Yao. A Multi-objective Approach to Testing Resource Allocation in Modular Software Systems. In 2008 Congress on Evolutionary Computation (CEC’2008), pages 1148–1153, Hong Kong, China, June 2008. IEEE Service Center.

[9809] Zai Wang, Ke Tang, and Xin Yao. Multi-Objective Approaches to Optimal Testing Resource Allocation in Modular Software Systems. IEEE Transactions on Reliability, 59(3):563–575, September 2010.

[9810] Zai Wang, Zhenyu Yang, Ke Tang, and Xin Yao. Adaptive Differential Evolution for Multi-objective Optimization. In Yong Shi, Shouyang Wang, Yi Peng, Jianping Li, and Yong Zeng, editors, Cutting-Edge Research Topics on Multiple Criteria Decision Making (MCDM’2009), pages 9–16. Springer, Communications in Computer and Information Science, Vol. 35, Chengdu, China, 2009.

[9811] Zhao Wang, Maoguo Gong, Qing Cai, Lijia Ma, and Licheng Jiao. Deployment Optimization of Near Space Airships Based on MOEA/D with Local Search. In 2014 IEEE Congress on Evolutionary Computation (CEC’2014), pages 2345–2352, Beijing, China, 6-11 July 2014. IEEE Press. ISBN 978-1-4799-1488-3.

[9812] Zhaohua Wang, Jianhua Yin, and Weimin Ma. A Reverse Logistics Optimization Model for Hazardous Waste in the Perspective of Fuzzy Multi-Objective Programming Theory. In 2008 Congress on Evolutionary Computation (CEC’2008), pages 1576–1580, Hong Kong, China, June 2008. IEEE Service Center.

[9813] Zhenkun Wang, Qingfu Zhang, Maoguo Gong, and Aimin Zhou. A Replacement Strategy for Balancing Convergence and Diversity in MOEA/D. In 2014 IEEE Congress on Evolutionary Computation (CEC’2014), pages 2132–2139, Beijing, China, 6-11 July 2014. IEEE Press. ISBN 978-1-4799-1488-3.

[9814] Zhenkun Wang, Qingfu Zhang, Aimin Zhou, Maoguo Gong, and Licheng Jiao. Adaptive Replacement Strategies for MOEA/D. IEEE Transactions on Cybernetics, 46(2):474–486, February 2016.

[9815] Zhi-Gang Wang, M. Rahman, and Yoke-San Wong. Multi-Niche Crowding in the Development of Parallel Genetic Simulated Annealing. In Hans-Georg Beyer et al., editor, 2005 Genetic and Evolutionary Computation Conference (GECCO’2005), volume 2, pages 1555–1556, USA, June 2005. ACM Press.

[9816] Zhichun Wang, Minqiang Li, and Juanzi Li. A Multi-objective Evolutionary Algorithm for Feature Selection Based on Mutual Information with a New Redundancy Measure. Information Sciences, 307:73–88, June 20 2015.

[9817] Zutong Wang, Jiansheng Guo, Mingfa Zheng, and Qifang He. A Hybrid Algorithm for Stochastic Multiobjective Programming Problem. In António Gaspar-Cunha, Carlos Henggeler Antunes, and Carlos Coello Coello, editors, Evolutionary Multi-Criterion Optimization, 8th International Conference, EMO 2015, pages 218–232. Springer. Lecture Notes in Computer Science Vol. 9018, Guimarães, Portugal, March 29 - April 1 2015.

[9818] Zutong Wang, Jiansheng Guo, Mingfa Zheng, and Youshe Yang. A New Approach for Uncertain Multiobjective Programming Problem Based on P-E Principle. Journal of Industrial and Management Optimization, 11(1):13–26, January 2015.

[9819] Elizabeth F. Wanner, Frederico G. Guimar aes, Ricardo H.C. Takahashi, and Peter J. Fleming. Local Search with Quadratic Approximations into Memetic Algorithms for Optimization with Multiple Criteria. Evolutionary Computation, 16(2):185–224, Summer 2008.

[9820] Elizabeth F. Wanner, Frederico G. Guimaraes, Ricardo H.C. Takahashi, and Peter J. Fleming. A Quadratic Approximation-Based Local Search Procedure for Multiobjective Genetic Algorithms. In 2006 IEEE Congress on Evolutionary Computation (CEC’2006), pages 3361–3368, Vancouver, BC, Canada, July 2006. IEEE.

[9821] Ben Ward and Dragan A. Savic. A multi-objective optimisation model for sewer rehabilitation considering critical risk of failure. Water Science and Technology, 66(11):2410–2417, 2012.

[9822] Aryeh Warmflash, Paul Francois, and Eric D. Siggia. Pareto evolution of gene networks: an algorithm to optimize multiple fitness objectives. Physical Biology, 9(5), October 2012. Article Number: 056001.

[9823] S. Watanabe, T. Hiroyasu, and M. Miki. Parallel Evolutionary Multi-Criterion Optimization for Mobile Telecommunication Networks Optimization. In K.C. Giannakoglou, D.T. Tsahalis, J. Periaux, K.D. Papailiou, and T. Fogarty, editors, Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems. Proceedings of the EUROGEN’2001. Athens.

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[9824] Shinya Watanabe, Yuta Chiba, and Masahiro Kanazaki. A Proposal on Analysis Support System Based on Association Rule Analysis for Nondominated Solutions. In 2014 IEEE Congress on Evolutionary Computation (CEC’2014), pages 880–887, Beijing, China, 6-11 July 2014. IEEE Press. ISBN 978-1-4799-1488-3.

[9825] Shinya Watanabe and Tomoyuki Hiroyasu. Multi-Objective Rectangular Packing Problem. In Carlos A. Coello Coello and Gary B. Lamont, editors, Applications of Multi-Objective Evolutionary Algorithms, pages 581– 602. World Scientific, Singapore, 2004.

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[10051] Jiuping Xu and Zhimiao Tao. A Class of Multi-objective Equilibrium Chance Maximization Model with Twofold Random Phenomenon and its Application to Hydropower Station Operation. Mathematics and Computers in Simulation, 85:11–33, November 2012.

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[10145] Kaifeng Yang, Michael T.M. Emmerich, Rui Li, Ji Wang, and Thomas Bäck. Power Distribution Network Reconfiguration by Evolutionary Integer Programming. In Thomas Bartz-Beielstein, Jürgen Branke, Bogdan Filipič, and Jim Smith, editors, Parallel Problem Solving from Nature - PPSN XIII, 13th International Conference, pages 11–23. Springer. Lecture Notes in Computer Science Vol. 8672, Ljubljana, Slovenia, September 13-17 2014.

[10146] Kaifeng Yang, Daniel Gaida, Thomas Bäck, and Michael Emmerich. Expected Hypervolume Improvement Algorithm for PID Controller Tuning and the Multiobjective Dynamical Control of a Biogas Plant. In 2015 IEEE Congress on Evolutionary Computation (CEC’2015), pages 1934– 1942, Sendai, Japan, 25-28 May 2015. IEEE Press. ISBN 978-1-4799-7492-4.

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[10152] Ming-Der Yang, Yi-Ping Chen, Yu-Hao Lin, Yu-Feng Ho, and Ji-Yuan Lin. Multiobjective Optimization Using Nondominated Sorting Genetic Algorithm-II for Allocation of Energy Conservation and Renewable Energy Facilities in a Campus. Energy and Buildings, 122:120–130, June 15 2016.

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[10445] Q. Zhang, M. Mahfouf, G. Panoutsos, K. Beamish, and I. Norris. Knowledge discovery for friction stir welding via data driven approaches Part 2multiobjective modelling using fuzzy rule based systems. Science and Technology of Welding and Joining, 17(8):681–693, November 2012.

[10446] Q. Zhang, H. Manier, and M.-A. Manier. A General Model for Job Shop Problems Using Imune-Genetic Algorithm and Multiobjective Optimization Techniques. In 6th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2009), pages 390–393, Milan, Italy, July 2009. INSTICC.

[10447] Qian Zhang and Mahdi Mahfouf. A New Reduced Space Searching Algorithm (RSSA) and Its Application in Optimal Design of Alloy Steels. In 2007 IEEE Congress on Evolutionary Computation (CEC’2007), pages 1815–1822, Singapore, September 2007. IEEE Press.

[10448] Qian Zhang and Mahdi Mahfouf. A Modified PSO with a Dynamically Varying Population and Its Application to the Multi-Objective Optimal Design of Alloy Steels. In 2009 IEEE Congress on Evolutionary Computation (CEC’2009), pages 3241–3248, Trondheim, Norway, May 2009. IEEE Press.

[10449] Qian Zhang and Mahdi Mahfouf. A Hierarchical Mamdani-Type Fuzzy Modelling Approach With New Training Data Selection and Multi-Objective Optimisation Mechanisms: A Special Application for the Prediction of Mechanical Properties of Alloy Steels. Applied Soft Computing, 11(2):2419–2443, March 2011.

[10450] Qian Zhang, Mahdi Mahfouf, George Panoutsos, Kathryn Beamish, and Ian Norris. Multiple Characterisation Modelling of Friction Stir Welding Using a Genetic Multi-objective Data-driven Fuzzy Modelling Approach. In 2011 IEEE International Conference on Fuzzy Systems (FUZZ 2011), pages 2288– 2295, Taipei, Taiwan, June 27-30 2011. IEEE Press. ISBN 978-1-4244-7317-5.

[10451] Qian Zhang, Mahdi Mahfouf, John R. Yates, Christophe Pinna, George Panoutsos, Soufiene Boumaiza, Richard J. Greene, and Luis de Leon. Modeling and Optimal Design of Machining-Induced Residual Stresses in Aluminium Alloys Using a Fast Hierarchical Multiobjective Optimization Algorithm. Materials and Manufacturing Processes, 26(3):508–520, 2011.

[10452] Qian Zhang and Mandi Mahfouf. A nature-inspired multi-objective optimisation strategy based on a new reduced space searching algorithm for the design of alloy steels. Engineering Applications of Artificial Intelligence, 23(5):660– 675, August 2010.

[10453] Qingfu Zhang and Hui Li. MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition. IEEE Transactions on Evolutionary Computation, 11(6):712–731, December 2007.

[10454] Qingfu Zhang, Hui Li, Dietmar Maringer, and Edward Tsang. MOEA/D with NBI-style Tchebycheff approach for portfolio management. In 2010 IEEE Congress on Evolutionary Computation (CEC’2010), pages 3308– 3315, Barcelona, Spain, July 18–23 2010. IEEE Press.

[10455] Qingfu Zhang, Wudong Liu, and Hui Li. The Performance of a New Version of MOEA/D on CEC09 Unconstrained MOP Test Instances. In 2009 IEEE Congress on Evolutionary Computation (CEC’2009), pages 203–208, Trondheim, Norway, May 2009. IEEE Press.

[10456] Qingfu Zhang, Wudong Liu, Edward Tsang, and Botond Virginas. Expensive Multiobjective Optimization by MOEA/D with Gaussian Process Model. IEEE Transactions on Evolutionary Computation, 14(3):456–474, June 2010.

[10457] Qingfu Zhang, Aimin Zhou, and Yaochu Jin. RM-MEDA: A Regularity Model-Based Multiobjective Estimation of Distribution Algorithm. IEEE Transactions on Evolutionary Computation, 12(1):41–63, February 2008.

[10458] Rui Zhang, Jianzhong Zhou, Li Mo, Shuo Ouyang, and Xiang Liao. Economic environmental dispatch using an enhanced multi-objective cultural algorithm. Electric Power Systems Research, 99:18–29, June 2013.

[10459] Rui Zhang, Jianzhong Zhou, and Yongqiang Wang. Multi-objective optimization of hydrothermal energy system considering economic and environmental aspects. International Journal of Electrical Power & Energy Systems, 42(1):384–395, November 2012.

[10460] Tao Zhang, W. A. Chaovalitwongse, and Yuejie Zhang. Scatter search for the stochastic travel-time vehicle routing problem with simultaneous pickups and deliveries. Computers & Operations Research, 39(10):2277–2290, October 2012.

[10461] Tao Zhang, Tiesong Hu, Xuning Guo, Zhong Chen, and Yue Zheng. Solving high dimensional bilevel multiobjective programming problem using a hybrid particle swarm optimization algorithm with crossover operator. Knowledge-Based Systems, 53:13–19, November 2013.

[10462] Tao Zhang, Tiesong Hu, Jia wei Chen, Zhongping Wan, and Xuning Guo. Solving Bilevel Multiobjective Programming Problem by Elite Quantum Behaved Particle Swarm Optimization. Abstract and Applied Analysis, 2012. Article Number: 102482.

[10463] Tao Zhang, Tiesong Hu, Yue Zheng, and Xuning Guo. An Improved Particle Swarm Optimization for Solving Bilevel Multiobjective Programming Problem. Journal of Applied Mathematics, 2012. Article Number: 626717.

[10464] Taohong Zhang, Linxin Li, Fujun Liang, and Bingru Yang. Parameter optimization of laser die-surface hardening using the particle swarm optimization technique. International Journal of Advanced Manufacturing Technology, 36(11-12):1104–1112, April 2008.

[10465] Tiantian Zhang, Michael Georgiopoulos, and Georgios C. Anagnostopoulos. S-Race: A Multi-Objective Racing Algorithm. In 2013 Genetic and Evolutionary Computation Conference (GECCO’2013), pages 1565–1572, New York, USA, July 2013. ACM Press. ISBN 978-1-4503-1963-8.

[10466] Tiantian Zhang, Michael Georgiopoulos, and Georgios C. Anagnostopoulos. SPRINT Multi-Objective Model Racing. In 2015 Genetic and Evolutionary Computation Conference (GECCO 2015), pages 1383–1390, Madrid, Spain, July 11-15 2015. ACM Press. ISBN 978-1-4503-3472-3.

[10467] Wen Zhang and Yutian Liu. Multi-objective reactive power and voltage control based on fuzzy optimization strategy and fuzzy adaptive particle swarm. International Journal of Electrical Power & Energy Systems, 30(9):525–532, November 2008.

[10468] Wenlong Zhang, Chao Wang, Yi Li, Peifang Wang, Qing Wang, and Dawei Wang. Seeking Sustainability: Multiobjective Evolutionary Optimization for Urban Wastewater Reuse in China. Environmental Science & Technology, 48(2):1094–1102, January 21 2014.

[10469] Wenqiang Zhang and Shigeru Fujimura. Multiobjective process planning and scheduling using improved vector evaluated genetic algorithm with archive. IEEJ Transactions on Electrical and Electronic Engineering, 7(3):258–267, May 2012.

[10470] Wenqiang Zhang and Mitsuo Gen. Process Planning and Scheduling in Distributed Manufacturing System Using Multiobjective Genetic Algorithm. IEEJ Transactions on Electrical and Electronic Engineering, 5(1):62–72, January 2010.

[10471] Wenqiang Zhang and Mitsuo Gen. An efficient multiobjective genetic algorithm for mixed-model assembly line balancing problem considering demand ratio-based cycle time. Journal of Intelligent Manufacturing, 22(3):367–378, January 2011.

[10472] Wenqiang Zhang, Mitsuo Gen, and Jungbok Jo. Hybrid sampling strategybased multiobjective evolutionary algorithm for process planning and scheduling problem. Journal of Intelligent Manufacturing, 25(5):881–897, October 2014.

[10473] Wenqiang Zhang, Lin Lin, Mitsuo Gen, and Chen-Fu Chien. Hybrid Sampling Strategy-based Multiobjective Evolutionary Algorithm. In C. H. Dagli, editor, Complex Adaptive Systems 2012, pages 96–101, Washington, D. C., November 14-16 2012. Elsevier Science BV.

[10474] Wenzhu Zhang, Kyung Sup Kwak, and Chengxiao Feng. Network Selection Algorithm for Heterogeneous Wireless Networks Based on Multi-Objective Discrete Particle Swarm Optimization. KSII Transactions on Internet and Information Systems, 6(7):1802–1814, July 25 2012.

[10475] X. Zhang, R. C. Izaurralde, D. Manowitz, T. O. West, W. M. Post, A. M. Thomson, V. P. Bandaruw, J. Nichols, and J. R. Williams. An integrative modeling framework to evaluate the productivity and sustainability of biofuel crop production systems. Global Change Biology Bioenergy, 2(5):258–277, October 2010.

[10476] X. Zhang, R. Srinivasan, and M. Van Liew. Multi-site Calibration of the SWAT Model for Hydrologic Modeling. Transactions of the ASABE, 51(6):2039–2049, November-December 2008.

[10477] Xiangrong Zhang, Bin Lu, Shuiping Gou, and Licheng Jiao. Immune Multiobjective Optimization Algorithm Using Unsupervised Feature Selection. In Franz Rothlauf et al., editor, Applications of Evolutionary Computing. EvoWorkshops 2006: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoINTER-ACTION, EvoMUSART, and EvoSTOC, pages 484–494, Budapest, Hungary, April 2006. Springer, Lecture Notes in Computer Science Vol. 3907.

[10478] Xiao Zhang, Yu Zhou, Qingfu Zhang, Victor C.S. Lee, and Minming Li. Multi-objective Optimization of Barrier Coverage with Wireless Sensors. In António Gaspar-Cunha, Carlos Henggeler Antunes, and Carlos Coello Coello, editors, Evolutionary Multi-Criterion Optimization, 8th International Conference, EMO 2015, pages 557–572. Springer. Lecture Notes in Computer Science Vol. 9019, Guimarães, Portugal, March 29 - April 1 2015.

[10479] Xiaohua Zhang, Hongyun Meng, and Licheng Jiao. Improving PSO-Based Multiobjective Optimization Using Competition and Immunity Clonal. In Yue Hao et al., editor, Computational Intelligence and Security. International Conference, CIS 2005, pages 839–845, Xi’an, China, December 2005. Springer, Lecture Notes in Artificial Intelligence Vol. 3801.

[10480] Xiaoli Zhang and Carl A. Nelson. Multiple-Criteria Kinematic Optimization for the Design of Spherical Serial Mechanisms Using Genetic Algorithms. Journal of Mechanical Design, 133(1), January 2011. Article Number 011005.

[10481] Xingdong Zhang and Marc P. Armstrong. Genetic algorithms and the corridor location problem: multiple objectives and alternative solutions. Environment and Planning B–Planning & Design, 35(1):148–168, January 2008.

[10482] Xingyi Zhang, Ye Tian, Ran Cheng, and Jin Yaochu. An Efficient Approach to Nondominated Sorting for Evolutionary Multiobjective Optimization. IEEE Transactions on Evolutionary Computation, 19(2):201–213, April 2015.

[10483] Xingyi Zhang, Ye Tian, and Yaochu Jin. A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization. IEEE Transactions on Evolutionary Computation, 19(6):761–776, December 2015.

[10484] Xuejun Zhang, Xiangmin Guan, Yanbo Zhu, and Jiaxing Lei. Strategic flight assignment approach based on multi-objective parallel evolution algorithm with dynamic migration interval. Chinese Journal of Aeronautics, 28(2):556– 563, April 2015.

[10485] Xuesong Zhang. Evaluating and Developing Parameter Optimization and Uncertainty Analysis Methods for a Computationally Intensive Distributed Hydrological Model. PhD thesis, Texas A&M University, USA, August 2008.

[10486] Xuesong Zhang, Peter Beeson, Robert Link, David Manowitz, Roberto C. Izaurralde, Ali Sadeghi, Allison M. Thomson, Ritvik Sahajpal, Raghavan Srinivasan, and Jeffrey G. Arnold. Efficient multi-objective calibration of a computationally intensive hydrologic model with parallel computing software in Python. Environmental Modelling & Software, 46:208–218, August 2013.

[10487] Xuesong Zhang, Raghavan Srinivasan, Jeff Arnold, R. Cesar Izaurralde, and David Bosch. Simultaneous calibration of surface flow and baseflow simulations: a revisit of the SWAT model calibration framework. Hydrological Processes, 25(14):2313–2320, July 1 2011.

[10488] Xuesong Zhang, Raghavan Srinivasan, and Michael Van Liew. On the use of multi-algorithm, genetically adaptive multi-objective method for multisite calibration of the SWAT model. Hydrological Processes, 24(8):955–969, April 15 2010.

[10489] Yan Zhang, Kus Hidajat, and Ajay K. Ray. Optimal design and operation of SMB bioreactor: production of high fructose syrup by isomerization of glucose. Biochemical Engineering Journal, 21(2):111–121, October 2004.

[10490] Yang Zhang, HongYu Li, Mahesan Niranjan, and Peter Rockett. Applying Cost-Sensitive Multiobjective Genetic Programming to Feature Extraction for Spam E-mail Filtering. In Michael O’Neill, Leonardo Vanneschi, Steven Gustafson, Anna Isabel Esparcia Alcázar, Ivanoe De Falco, Antonio Della Cioppa, and Ernesto Tarantino, editors, Genetic Programming, 11th European Conference, EuroGP 2008, pages 325–336. Springer. Lecture Notes in Computer Science Vol. 4971, Naples, Italy, March 2008.

[10491] Yang Zhang and Peter Rockett. A Comparison of three evolutionary strategies for multiobjective genetic programming. Artificial Intelligence Review, 27(2-3):149–163, March 2007.

[10492] Yang Zhang and Peter I. Rockett. Evolving Optimal Feature Extraction using Multi-objective Genetic Programming: A Methodology and Preliminary Study on Edge Detection. In Hans-Georg Beyer et al., editor, 2005 Genetic and Evolutionary Computation Conference (GECCO’2005), volume 1, pages 795–802, New York, USA, June 2005. ACM Press.

[10493] Yang Zhang and Peter I Rockett. Multiobjective Genetic Programming Feature Extraction with Optimized Dimensionality. In Janusz Kacprzyk, editor, Soft Computing in Industrial Applications, chapter 15, pages 159–168. Springer. Advances in Soft Computing, Vol. 39, Berlin, 2007.

[10494] Yang Zhang and Peter I. Rockett. Application of Multiobjective Genetic Programming to the Design of Robot Failure Recognition Systems. IEEE Transactions on Automation Science and Engineering, 6(2):372–376, April 2009.

[10495] Yang Zhang and Peter I. Rockett. A generic multi-dimensional feature extraction method using multiobjective genetic programming. Evolutionary Computation, 17(1):89–115, Spring 2009.

[10496] Yang Zhang and Peter I. Rockett. A generic optimising feature extraction method using multiobjective genetic programming. Applied Soft Computing, 11(1):1087–1097, January 2011.

[10497] Yicha Zhang, Weijun Wang, and Alain Bernard. Embedding Multi-Attribute Decision Making into Evolutionary Optimization to Solve the Many-Objective Combinatorial Optimization Problems. Journal of Grey System, 28(3):124–143, 2016.

[10498] Yifeng Zhang and Kai-Yew Lum. Integrated-Optimal Design of Airplane and Flight Control Using Genetic Algorithms. In 2007 IEEE Congress on Evolutionary Computation (CEC’2007), pages 2980–2987, Singapore, September 2007. IEEE Press.

[10499] Ying Zhang. MEMS Design Synthesis Based on Hybrid Evolutionary Computation. PhD thesis, Civil and Environmental Engineering, University of California, Berkeley, USA, 2006.

[10500] Ying Zhang, Raffi Kamalian, Alice M. Agogino, and Carlo H. Séquin. Design Synthesis of Microelectromechanical Systems Using Genetic Algorithms with Component-Based Genotype Representation. In Maarten Keijzer et al., editor, 2006 Genetic and Evolutionary Computation Conference (GECCO’2006), volume 1, pages 731–738, Seattle, Washington, USA, July 2006. ACM Press. ISBN 1-59593-186-4.

[10501] Yong Zhang, Xiao bei Wu, Zong yi Xing, and Wei-Li Hu. On generating interpretable and precise fuzzy systems based on Pareto multi-objective cooperative co-evolutionary algorithm. Applied Soft Computing, 11(1):1284–1294, January 2011.

[10502] Yong Zhang, Dun-Wei Gong, and Zhonghai Ding. A bare-bones multiobjective particle swarm optimization algorithm for environmental/economic dispatch. Information Sciences, 192:213–227, June 1 2012.

[10503] Yong Zhang, Dun-Wei Gong, and Na Gong. Multi-Objective Optimization Problems Using Cooperative Evolvement Particle Swarm Optimizer. Journal of Computational and Theoretical Nanoscience, 10(3):655–663, March 2013.

[10504] Yong Zhang, Guangyong Sun, Guangyao Li, Zhen Luo, and Qing Li. Optimization of foam-filled bitubal structures for crashworthiness criteria. Materials & Design, 38:99–109, June 2012.

[10505] Yong Zhang, Guangyong Sun, Xipeng Xu, Guangyao Li, and Qing Li. Multiobjective crashworthiness optimization of hollow and tubes for multiple load cases. Thin-Walled Structures, 82:331–342, September 2014.

[10506] Yong Zhang, Dun wei Gong, and Zhong hai Ding. Handling multi-objective optimization problems with a multi-swarm cooperative particle swarm optimizer. Expert Systems with Applications, 38(11):13933–13941, October 2011.

[10507] Yong Zhang, Dun wei Gong, and Jian hua Zhang. Robot path planning in uncertain environment using multi-objective particle swarm optimization. Neurocomputing, 103:172–185, March 1 2013.

[10508] Yong Zhang, Changhong Xia, Dunwei Gong, and Xiaoyan Sun. Multiobjective PSO Algorithm for Feature Selection Problems with Unreliable Data. In Ying Tan, Yuhui Shi, and Carlos A. Coello Coello, editors, Advances in Swarm Intelligence, 5th International Conference, ICSI 2014, pages 386– 393. Springer. Lecture Notes in Computer Science Vol. 8794, Hefei, China, October 17-20 2014.

[10509] Yongguo Zhang, Yayi Xu, Mingfa Zheng, and Liu Ningning. The Properties of Birandom Multiobjective Programming Problems. In Ying Tan, Yuhui Shi, Yi Chai, and Guoyin Wang, editors, Advances in Swarm Intelligence, Second International Conference, ICSI 2011, pages 34–40. Springer. Lecture Notes in Computer Science Vol. 6729, Chongqing, China, June 12-15 2011.

[10510] Yu Zhang, Sanbo Hu, Jinglai Wu, Yunqing Zhang, and Liping Chen. Multiobjective optimization of double suction centrifugal pump using Kriging metamodels. Advances in Engineering Software, 74:16–26, August 2014.

[10511] Yuanyuan Zhang, Mark Harman, Anthony Finkelstein, and S. Afshin Mansouri. Comparing the performance of metaheuristics for the analysis of multistakeholder tradeoffs in requirements optimisation. Information and Software Technology, 53(7):761–773, July 2011.

[10512] Yuanyuan Zhang, Mark Harman, and Soo Ling Lim. Empirical evaluation of search based requirements interaction management. Information and Software Technology, 55(1):126–152, January 2013.

[10513] Yuanyuan Zhang, Mark Harman, and S. Afshin Mansouri. The Multi-Objective Next Release Problem. In Dirk Thierens, editor, 2007 Genetic and Evolutionary Computation Conference (GECCO’2007), volume 1, pages 1129–1136, London, UK, July 2007. ACM Press.

[10514] Yudong Zhang, Yan Jun, Geng Wei, and Lenan Wu. Find multi-objective paths in stochastic networks via chaotic immune PSO. Expert Systems with Applications, 37(3):1911–1919, March 15 2010.

[10515] Yue Zhang, Fei Tao, Yuanjun Laili, Baocun Hou, Lin Lv, and Lin Zhang. Green partner selection in virtual enterprise based on Pareto genetic algorithms. International Journal of Advanced Manufacturing Technology, 67(9-12):2109–2125, August 2013.

[10516] Yue-Yu Zhang, Weiguo Gao, Shiyou Chen, Hongjun Xiang, and Xin-Gao Gong. Inverse design of materials by multi-objective differential evolution. Computational Materials Science, 98:51–55, February 15 2015.

[10517] Yutong Zhang, Mingxing Zhou, and Zhongzhou Jiang. A multi-agent genetic algorithm for big optimization problems. In 2015 IEEE Congress on Evolutionary Computation (CEC’2015), pages 703–707, Sendai, Japan, 25-28 May 2015. IEEE Press. ISBN 978-1-4799-7492-4.

[10518] Yuzhen Zhang, Guangming Dai, Lei Peng, and Maocai Wang. HMOEDA LLE: A Hybrid Multi-objective Estimation of Distribution Algorithm Combining Locally Linear Embedding. In 2014 IEEE Congress on Evolutionary Computation (CEC’2014), pages 707–714, Beijing, China, 6-11 July 2014. IEEE Press. ISBN 978-1-4799-1488-3.

[10519] Z. Zhang, M. Mazzotti, and M. Morbidelli. Multiobjective optimization of simulated moving bed and Varicol processes using genetic algorithm. Journal of Chromatography A, 989(1):95–108, March 7 2003.

[10520] Zhuhong Zhang. Immune optimization algorithm for constrained nonlinear multiobjective optimization problems. Applied Soft Computing, 7(3):840– 857, June 2007.

[10521] Zhuhong Zhang. Multiobjective optimization immune algorithm in dynamic environments and its application to greenhouse control. Applied Soft Computing, 8(2):959–971, March 2008.

[10522] Zhuhong Zhang and Shuqu Qian. Multi-Objective Immune Optimization in Dynamic Environments and Its Application to Signal Simulation. In 2009 International Conference on Measuring Technology and Mechatronics Automation (ICMTMA’09), pages 246–250, Zhangjiajie, Hunan, China, April 2009. IEEE Computer Society.

[10523] Zhuhong Zhang and Shuqu Qian. Artificial immune system in dynamic environments solving time-varying non-linear constrained multi-objective problems. Soft Computing, 15(7):1333–1349, July 2011.

[10524] Zhuhong Zhang and Xin Tu. Probabilistic dominance-based multi-objective immune optimization algorithm in noisy environments. Journal of Computational and Theoretical Nanoscience, 4(7-8):1380–1387, November-December 2007.

[10525] Ziyang Zhang, K. Hidajat, Ajay K. Ray, and M. Morbidelli. Multiobjective Optimization of SMB and Varicol Process for Chiral Separation. AIChE Journal, 48(12):2800–2816, December 2002.

[10526] Ziyang Zhang, Ajay K. Ray, and K. Hidajat. Multiobjective optimization of simulated countercurrent moving bed chromatographic reactor (SCMCR) for MTBE synthesis. Industrial and Engineering Chemistry Research, 41(13):3213–3232, June 2002.

[10527] An Zhao, Zhounian Lai, Peng Wu, Linlin Cao, and Dazhuan Wu. Multi-Objective Optimization of a Low Specific Speed Centrifugal Pump Using an Evolutionary Algorithm. Engineering Optimization, 48(7):1251–1274, 2016.

[10528] Bo Zhao and Yi jia Cao. Multiple objective particle swarm optimization technique for economic load dispatch. Journal of Zhejiang University SCIENCE, 6A(5):420–427, 2005.

[10529] C.W. Zhao and Z.M. Wu. A genetic algorithm for manufacturing cell formation with multiple routes and multiple objectives. International Journal of Production Research, 38(2):385–395, January 20 2000.

[10530] F.Q. Zhao, Q.Y. Zhang, D.M. Yu, and X.H. Chen. A hybrid algorithm based on PSO and simulated annealing and its applications for partner selection in virtual enterprise. In Advances in Intelligent Computing, Pt 1, Proceedings, pages 380–389. Springer. Lecture Notes in Computer Science Vol. 3644, 2005.

[10531] H. L. Zhao and T. T. Lee. Research on multiobjective optimization control for nonlinear unknown systems. In O. Nasaoui, H. Frigui, and J. M. Keller, editors, Proceedings of the 12th IEEE International Conference on Fuzzy Systems, Vols 1 and 2, pages 402–407, St Louis, Mo, May 25-28 2003. IEEE. ISBN 0-7803-7810-5.

[10532] Han-Hua Zhao, Zhaoheng Liu, and My-Thien Dao. Reliability optimization using multiobjective ant colony system approaches. Reliability Engineering & System Safety, 92(1):109–120, January 2007.

[10533] Huimin Zhao. A multi-objective genetic programming approach to developing pareto optimal decision trees. Decision Support Systems, 43(3):809–826, April 2007.

[10534] Jiaqi Zhao, Vitor Basto Fernandes, Licheng Jiao, Iryna Yevseyeya, Asep Maulana, Rui Li, Thomas Back, Ke Tang, and Michael T. M. Emmerich. Multiobjective Optimization of Classifiers by Means of 3D Convex-Hull-Based Evolutionary Algorithms. Information Sciences, 367:80–104, November 1 2016.

[10535] Lihua Zhao, Lizhong Xu, Xianjian Xiao, Xiaofeng Ding, and Jun Liu. Research on Algorithm and Project Application for Distribution Automation Optimization Planning. In International Conference on Sustainable Power Generation and Supply, 2009. (SUPERGEN’09), pages 1–7, Nanjing, China, April 2009. IEEE Computer Society.

[10536] S. Z. Zhao, M. Willjuice Iruthayarajan, S. Baskar, and P. N. Suganthan. Multiobjective robust PID controller tuning using two lbests multi-objective particle swarm optimization. Information Sciences, 181(16):3323–3335, August 15 2011.

[10537] S.-Z. Zhao and P.N. Suganthan. Two-lbests based multi-objective particle swarm optimizer. Engineering Optimization, 43(1):1–17, January 2011.

[10538] Sen Zhao, Zhifeng Hao, Shusen Liu, Weidi Xu, and Han Huang. Multiobjective Evolutionary Algorithm Based on Layer Strategy. In Ying Tan, Yuhui Shi, and Zhen Ji, editors, Advances in Swarm Intelligence, Third International Conference, ICSI 2012, pages 546–553, Shenzhen, China, June 17-20 2012. Springer. Lecture Notes in Computer Science Vol. 7331.

[10539] Shi-Zheng Zhao and Ponnuthurai Nagaratnam Suganthan. Multi-Objective Evolutionary Algorithm with Ensemble of External Archives. International Journal of Innovative Computing Information and Control, 6(4):1713–1726, April 2010.

[10540] Shi-Zheng Zhao, Ponnuthurai Nagaratnam Suganthan, and Qingfu Zhang. Decomposition-Based Multiobjective Evolutionary Algorithm with an Ensemble of Neighborhood Sizes. IEEE Transactions on Evolutionary Computation, 16(3):442–446, June 2012.

[10541] Shuguang Zhao and Licheng Jiao. Multi-objective evolutionary design and knowledge discovery of logic circuits on an adaptive genetic algorithm. Genetic Programming and Evolvable Machines, 7(3):195–210, October 2006.

[10542] Shuguang Zhao, Licheng Jiao, Jianxun Zhao, and Yuping Wang. Evolutionary Design of Analog Circuits with a Uniform-Design Based Multi-Objective Adaptive Genetic Algorithm. In Jason Lohn, David Gwaltney, Gregory Hornby, Ricardo Zebulum, Didier Keymeulen, and Adrian Stoica, editors, 2005 NASA/DoD Conference on Evolvable Hardware, pages 26–29, Los Alamitos, California, July 2005. IEEE Computer Society Press.

[10543] Shuguang Zhao, Licheng Jiao, and Jun Zhao. Multi-objective Evolutionary Design and Knowledge Discovery of Logic Circuits with an Improved Genetic Algorithm. In Computational Intelligence and Security. International Conference, CIS 2005, pages 273–278, Xi’an, China, December 2005. Springer, Lecture Notes in Artificial Intelligence Vol. 3801.

[10544] Shuguang Zhao, Xinquan Lai, and Mingying Zhao. A Uniform-Design Based Multi-objective Adaptive Genetic Algorithm and Its Application to Automated Design of Electronic Circuits. In Licheng Jiao, Lipo Wang, Xinbo Gao, Jing Liu, and Feng Wu, editors, 2006 Second International Conference on Advances in Natural Computation (ICNC 2006), pages 653–656. Springer. Lecture Notes in Computer Science, Vol. 4221, Xi’an, China, 2006. ISBN 3-540-45901-4.

[10545] Shuguang Zhao, Jianxun Zhao, and Licheng Jia. Adaptive Genetic Algorithm Based Approach for Evolutionary Design and Multi-objective Optimization of Logic Circuits. In Jason Lohn, David Gwaltney, Gregory Hornby, Ricardo Zebulum, Didier Keymeulen, and Adrian Stoica, editors, 2005 NASA/DoD Conference on Evolvable Hardware, pages 67–72, Los Alamitos, California, July 2005. IEEE Computer Society Press.

[10546] Wenjing Zhao, Jing Liu, Hussein A. Abbass, and Axel Bender. A Multi-Objective Risk-Based Approach for Airlift Task Scheduling Using Stochastic Bin Packing. In 2010 IEEE Congress on Evolutionary Computation (CEC’2010), pages 442–449, Barcelona, Spain, July 18–23 2010. IEEE Press.

[10547] Xuancai Zhao, Qiuzhen Lin, Jianyong Chen, Xiaomin Wang, Jianping Yu, and Zhong Ming. Optimizing Security and Quality of Service in a Real-Time Database System Using Multi-Objective Genetic Algorithm. Expert Systems with Applications, 64:11–23, December 1 2016.

[10548] Yong Zhao, Carlos A. Brizuela, and Nobuo Sannomiya. Application of the Partial Enumeration Selection Method in Genetic Algorithms to Solving a Multi-Objective Flowshop Problem. In 2001 IEEE International Conference on Systems, Man, and Cybernetics, volume 4, pages 2365–2370, 2001.

[10549] Yongxiang Zhao, Shengwu Xiong, and Meifang Li. Constrained Single- and Multiple-Objective Optimization with Differential Evolution. In Third International Conference on Natural Computation (ICNC 2007), pages 451–455, Haikou, Hainan, China, August 24-27 2007. IEEE Computer Society.

[10550] Zeng Zhen. Coordinated Rescheduling of Precast Production. PhD thesis, Department of Civil Engineering, National University of Singapore, Singapore, 2006.

[10551] Bing Zheng, Zhong kai Li, and Yi xiong Feng. An Exploratory Study of Sorting Particle Swarm Optimizer for Multiobjective Optimization. In J. Xu, editor, 2009 Fourth International Conference on Bio-Inspired Computing: Theories and Applications, pages 112–119, Beijing, China, October 16-19 2009. IEEE Press. ISBN 978-1-4244-3865-5.

[10552] Bojin Zheng. A Highly Efficient Multi-objective Optimization Evolutionary Algorithm. In Third International Conference on Natural Computation (ICNC 2007), pages 549–554, Haikou, China, August 24-27 2007. IEEE Computer Society Press.

[10553] Bojin Zheng. A new Dynamic Multi-objective Optimization Evolutionary Algorithm. In Third International Conference on Natural Computation (ICNC 2007), pages 565–570, Haikou, China, August 24-27 2007. IEEE Computer Society Press.

[10554] D. X. M. Zheng, S. T. Ng, and M. M. Kumaraswamy. Applying pareto ranking and niche formation to genetic algorithm-based multiobjective time-cost optimization. Journal of Construction Engineering and Management-ASCE, 131(1):81–91, January 2005.

[10555] D.X.M. Zheng, S.T. Ng, and M.M. Kumaraswamy. Applying a genetic algorithm-based multiobjective approach for time-cost optimization. Journal of Construction Engineering and Management–ASCE, 130(2):168–176, March-April 2004.

[10556] Feifei Zheng, Angus R. Simpson, and Aaron C. Zecchin. An Efficient Hybrid Approach for Multiobjective Optimization of Water Distribution Systems. Water Resources Research, 50(5):3650–3671, May 2014.

[10557] Feifei Zheng and Aaron Zecchin. An efficient decomposition and dual-stage multi-objective optimization method for water distribution systems with multiple supply sources. Environmental Modelling & Software, 55:143–155, May 2014.

[10558] Feifei Zheng, Aaron C. Zecchin, Holger R. Maier, and Angus R. Simpson. Comparison of the Searching Behavior of NSGA-II, SAMODE, and Borg MOEAs Applied to Water Distribution System Design Problems. Journal of Water Resources Planning and Management, 142(7), July 2016. Article Number: 04016017.

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