A learning classifier system based on genetic network programming

Xianneng Li, Kotaro Hirasawa

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

7 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

Fingerprint

Classifiers
Decision making
Evolutionary algorithms
Experiments

Keywords

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

ASJC Scopus subject areas

  • Human-Computer Interaction

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

A learning classifier system based on genetic network programming. / Li, Xianneng; Hirasawa, Kotaro.

Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013. 2013. p. 1323-1328 6721982.

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

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., 6721982, pp. 1323-1328, 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013, Manchester, United Kingdom, 13/10/13. https://doi.org/10.1109/SMC.2013.229
Li X, Hirasawa K. A learning classifier system based on genetic network programming. In Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013. 2013. p. 1323-1328. 6721982 https://doi.org/10.1109/SMC.2013.229
Li, Xianneng ; Hirasawa, Kotaro. / A learning classifier system based on genetic network programming. Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013. 2013. pp. 1323-1328
@inproceedings{3d58c08bfd904b289d984e3230de3b3e,
title = "A learning classifier system based on genetic network programming",
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.",
keywords = "Fitness sharing, Genetic network programming, Learning classifier systems, Niching, Reinforcement learning",
author = "Xianneng Li and Kotaro Hirasawa",
year = "2013",
doi = "10.1109/SMC.2013.229",
language = "English",
isbn = "9780769551548",
pages = "1323--1328",
booktitle = "Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013",

}

TY - GEN

T1 - A learning classifier system based on genetic network programming

AU - Li, Xianneng

AU - Hirasawa, Kotaro

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - Fitness sharing

KW - Genetic network programming

KW - Learning classifier systems

KW - Niching

KW - Reinforcement learning

UR - http://www.scopus.com/inward/record.url?scp=84893547065&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84893547065&partnerID=8YFLogxK

U2 - 10.1109/SMC.2013.229

DO - 10.1109/SMC.2013.229

M3 - Conference contribution

AN - SCOPUS:84893547065

SN - 9780769551548

SP - 1323

EP - 1328

BT - Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013

ER -