Towards building block propagation in XCS: a negative result and its implications

  • Authors:
  • Kurian K. Tharakunnel;Martin V. Butz;David E. Goldberg

  • Affiliations:
  • Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL;Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL;Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL

  • Venue:
  • GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
  • Year:
  • 2003

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Abstract

The accuracy-based classifier system XCS is currently the most successful learning classifier system. Several recent studies showed that XCS can produce machine-learning competitive results. Nonetheless, until now the evolutionary mechanisms in XCS remained somewhat ill-understood. This study investigates the selectorecombinative capabilities of the current XCS system. We reveal the accuracy dependence of XCS's evolutionary algorithm and identify a fundamental limitation of the accuracy-based fitness approach in certain problems. Implications and future research directions conclude the paper.