A Learning Classifier System Based on Genetic Network Programming

被引：6

作者：

Li, Xianneng ^{[1
]}

Hirasawa, Kotaro ^{[1
]}

机构：

[1] Waseda Univ, Grad Sch Informat Prod & Syst, Tokyo, Japan

来源：

2013 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2013) | 2013年

关键词：

learning classifier systems; genetic network programming; niching; fitness sharing; reinforcement learning; ALGORITHM;

D O I：

10.1109/SMC.2013.229

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：1323 / 1328

页数：6

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