A Learning Classifier System Based on Genetic Network Programming

Xianneng Li, Kotaro Hirasawa

Abstract: Recent advances in Learning Classifier Systems (LCSs) have shown their sequential decision-making ability with a generalization property. In this paper, a novel LCS named extended rule-based Genetic Network Programming (XrGNP) is proposed. Different from most of the current LCSs, the rules are represented and discovered through a graph-based evolutionary algorithm GNP, which consequently has the distinct expression ability to model and evolve the decision-making rules. XrGNP is described in details in which its unique features are explicitly mapped. Experiments on benchmark and real-world multi-step problems demonstrate the effectiveness of XrGNP.