Biasing mutations in cooperative coevolution

In coevolution, species are coevolving in a way that the genetic changes of one species in response to another species are reciprocal. One class of coevolution is cooperative coevolution in which species collaborate to solve the problems. The fitness of an individual in a species is assigned based on how well its collaboration with other individuals of another species can perform. As an extension of evolutionary algorithms (EAs), cooperative revolutionary algorithms (CCEAs) operate similar to EAs, except during fitness evaluations. In this paper, we focus on genetic variation operations of a CCEA: mutations. We present how to bias mutations in cooperative coevolution and compare the performance of a CCEA adopting biasing mutations (CCEA-BM) and a conventional CCEA in which all individuals are encoded in binary representations. Our experimental study shows that biasing mutations can improve the performance of a CCEA on function optimization, in particular when high orders of binary representations are used.