Game Model Based Co-evolutionary Algorithm and its Application for Multiobjective Optimization Problems

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