Improving Performance of GP by Adaptive Terminal Selection

Abstract

Genetic Programming (GP) is an evolutionary search algorithm which searches a computer program capable of producing the desired solution for a given problem. For the purpose, it is necessary that GP system has access to a set of features that are at least a superset of the features necessary to solve the problem. However, when the feature set given to GP is redundant, GP suffers substantial loss of its efficiency. This paper presents a new approach in GP to acquire relevant terminals from a redundant set of terminals. We propose the adaptive mutation based on terminal weighting mechanism for eliminating irrelevant terminals from the redundant terminal set. We show empirically that the proposed method is effective for finding relevant terminals and improving performance of GP in the experiments on symbolic regression problems.