How to cite it
Carlos A. 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.
Most real-world engineering optimization problems are multiobjective in nature,
since they normally have several (possibly conflicting) objectives that must be
satisfied at the same time. The word "optimum" has several interpretations within
this context, and it is up to the designer to decide which fits better to his/her
application. Currently, there are more than 20 mathematical programming multiobjective optimization techniques, each one corresponding to a different understanding
of the term "optimum". On the other hand, genetic algorithms (GAs) have been
viewed to be, since their early days, well suited for multiobjective optimization
problems. Consequently, several GA-based techniques have been developed since then.
The purpose of this research has been to develop a platform that allows the testing
and comparison of existing and future multiobjective optimization techniques. Two
new multiobjective optimization GA-based methods based on the notion of min-max
optimum are proposed, showing that at least one of them is able to produce better
results than any other technique tested. Also, a method for adjusting the parameters
of the GA for single-objective numerical optimization is proposed, showing the
suitability of the GA as a numerical optimization technique when used properly.
Then, a brief study of the importance of population policies and proper niching
parameters is included. This work tries to narrow the gap between theory and practice
in the context of engineering optimization. Finally, some insights on the
importance of choosing a good chromosomic representation and the use of a proper
fitness function are provided, derived from the analysis of a more general design
This PhD thesis can be downloaded here.
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