An improved co-evolution genetic algorithm for combinatorial optimization problems

Abstract

This paper presents an improved co-evolution genetic algorithm (ICGA), which uses the methodology of game theory to solve the mode deception and premature convergence problem. In ICGA, groups become different players in the game. Mutation operator is designed to simulate the situation in the evolutionary stable strategy. Information transfer mode is added to ICGA to provide greater decision-making space. ICGA is used to solve large-scale deceptive problems and an optimal control problem. Results of numerical tests validate the algorithm's excellent performance. © 2011 Springer-Verlag.