Guided Mutations in Cooperative Coevolutionary Algorithms for Function Optimization

Cooperative coevolution is becoming increasingly popular in solving difficult optimization problems. Its performance to solve the problems is influenced by many algorithm decisions. In this paper, a self-adaptive mutation operator "guided mutation" is proposed. The basic idea behind guided mutation is to maintain searching directions and searching step sizes at individual level, and these two strategy parameters are adaptively updated. Guided mutation is adopted in cooperative coevolutionary algorithm and its performance on the common test problems is compared. Experimental results show that guided mutation can improve cooperative coevolution in solving some problem domains. The reasons behind the differences in the performance of the various cooperative coevolutions are also discussed.