ABSTRACT OF Ph. D. THESIS
On
Performance Improvement of Genetic Algorithm used for Multiobjective Optimization
By
Dr.  RAHILA PATEL(SHEIKH) & Dr M M RAGHUWANSHI

The research work is aimed at the performance enhancement of Real-Coded Genetic 
Algorithms used for multi-objective optimization. Study started with identifying 
the potential areas in Multi-objective Genetic Algorithms (MOGAs) where modification 
might enhance their performance. Operators play very important role in the 
functioning of GAs. In GAs the roles of selection and recombination (or crossover) 
operators are very well defined. Selection operator controls the direction of a 
search and the recombination operator generates new vistas for the search. The 
efficacy of GAs on a particular problem hinges quite strongly on the degree of 
exploration and exploitation of search space by the recombination operator and the 
direction of search in the search space set by the selection operator. GA uses 
simple selection operators for solving single objective optimization problems (SOPs) 
but special selection operators are needed to solve MOPs. The study investigates 
the behavior of several selection schemes and proposes special scheme for performance 
enhancement of MOGA and used them in new MOGA framework.