Extended rule-based genetic network programming

Xianneng Li, Kotaro Hirasawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Recent advances in rule-based systems, i.e., Learning Classifier Systems (LCSs), have shown their sequential decisionmaking 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 "if-then" decision-making rules. Experiments on a benchmark multi-step problem (so-called Reinforcement Learning problem) demonstrate its effectiveness.

Original languageEnglish
Title of host publicationGECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion
Pages155-156
Number of pages2
DOIs
Publication statusPublished - 2013
Event15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013 - Amsterdam
Duration: 2013 Jul 62013 Jul 10

Other

Other15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013
CityAmsterdam
Period13/7/613/7/10

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Keywords

  • Genetic network programming
  • Learning classifier systems
  • XrGNP

ASJC Scopus subject areas

  • Computational Mathematics

Cite this

Li, X., & Hirasawa, K. (2013). Extended rule-based genetic network programming. In GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion (pp. 155-156) https://doi.org/10.1145/2464576.2464655