A learning classifier system based on genetic network programming

Xianneng Li, Kotaro Hirasawa

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

8 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
Pages1323-1328
Number of pages6
DOIs
Publication statusPublished - 2013
Event2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 - Manchester, United Kingdom
Duration: 2013 Oct 132013 Oct 16

Other

Other2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
CountryUnited Kingdom
CityManchester
Period13/10/1313/10/16

Keywords

  • Fitness sharing
  • Genetic network programming
  • Learning classifier systems
  • Niching
  • Reinforcement learning

ASJC Scopus subject areas

  • Human-Computer Interaction

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  • Cite this

    Li, X., & Hirasawa, K. (2013). A learning classifier system based on genetic network programming. In Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 (pp. 1323-1328). [6721982] https://doi.org/10.1109/SMC.2013.229