Rule networks in learning classifier systems

Karthik Kuber, Stuart W. Card, Kishan G. Mehrotra, Chilukuri K. Mohan

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

Abstract

Interrelationships between rules can be used to develop network models that can usefully represent the dynamics of Learning Classifier Systems. We examine two different kinds of rule networks and study their significance by testing them on the 20-mux problem. Through this experimentation, we establish that there is latent information in the evolving rule networks alongside the usual information that we gain from the XCS. We analyze these interrelationships using metrics from Network Science. We also show that these network measures behave as reliable indicators of rule set convergence.

Original languageEnglish (US)
Title of host publicationGECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference
PublisherAssociation for Computing Machinery
Pages977-982
Number of pages6
ISBN (Print)9781450328814
DOIs
StatePublished - Jan 1 2014
Event16th Genetic and Evolutionary Computation Conference, GECCO 2014 - Vancouver, BC, Canada
Duration: Jul 12 2014Jul 16 2014

Publication series

NameGECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference

Other

Other16th Genetic and Evolutionary Computation Conference, GECCO 2014
CountryCanada
CityVancouver, BC
Period7/12/147/16/14

Keywords

  • Convergence detection
  • Evolutionary algorithms
  • Genetic algorithms
  • Learning Classifier Systems
  • Network Science
  • XCS

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Applied Mathematics

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  • Cite this

    Kuber, K., Card, S. W., Mehrotra, K. G., & Mohan, C. K. (2014). Rule networks in learning classifier systems. In GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference (pp. 977-982). (GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference). Association for Computing Machinery. https://doi.org/10.1145/2598394.2611382