Balancing exploration and exploitation in genetic programming using inversion with individualized self-adaptation

Abstract

In this article we explore and develop a holistic scheme of self adaptive, individualized genetic operators combined with an adaptive tournament size together with a novel implementation of an inversion genetic operator which is suitable for tree based Genetic Programming. 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 Genetic Programming implementation. Our results also demonstrate that an inversion operator may have a useful role to play in exploitation, particularly when used towards the end of evolution to facilitate gradual convergence of the learning system towards a good solution.