Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
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Sefrioui introduced the Nash Genetic Algorithm in 1998.This approach combines genetic algorithms with Nash's idea. Another central achievement of Game Theory is the introduction of an Evolutionary Stable Strategy, introduced by Maynard Smith in 1982. In this paper, we will try to find ESS as a solution of MOPs using our game model based co-evolutionary algorithm.We present A Game model based co-evolutionary algorithm (GMBCA) to solve this class of problems and its performance is analyzed in comparing its results with those obtained with four others algorithms. Finally, the GMBCA is applied to solve the nutrition decision making problem to map the Pareto-optimum front. The results in the problem show its effectiveness.