Individualized self-adaptive genetic operators with adaptive selection in Genetic Programming

In this paper we investigate a new method for improving generalization performance of Genetic Programming(GP) on Binary Classification tasks. The scheme of self adaptive, individualized genetic operators combined with adaptive tournament size is designed to provide balanced, self-adaptive exploration and exploitation. We test this scheme on several benchmark Binary Classification problems and find that the proposed techniques deliver superior performance when compared with both a tuned GP configuration and a feedback adaptive GP implementation.