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Genetic and Evolutionary Computation Conference

GECCO 2004: Genetic and Evolutionary Computation – GECCO 2004 pp 824–835Cite as

Classifier Systems for Continuous Payoff Environments

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3103))

Abstract

Recognizing that many payoff functions are continuous and depend on the input state x, the classifier system architecture XCS is extended so that a classifier’s prediction is a linear function of x. On a continuous nonlinear problem, the extended system, XCS-LP, exhibits high performance and low error, as well as dramatically smaller evolved populations compared with XCS. Linear predictions are seen as a new direction in the quest for powerful generalization in classifier systems.

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References

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Author information

Authors and Affiliations

  1. Prediction Dynamics, Concord, MA, 01742, USA

    Stewart W. Wilson

  2. Department of General Engineering, The University of Illinois at Urbana-Champaign, IL, 61801, USA

    Stewart W. Wilson

Authors
  1. Stewart W. Wilson
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Editor information

Editors and Affiliations

  1. Indian Institute of Technology Kanpur, Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory (KanGAL), Kanpur, 208016, Uttar Pradesh, India

    Kalyanmoy Deb

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© 2004 Springer-Verlag Berlin Heidelberg

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Wilson, S.W. (2004). Classifier Systems for Continuous Payoff Environments. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24855-2_96

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  • DOI: https://doi.org/10.1007/978-3-540-24855-2_96

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22343-6

  • Online ISBN: 978-3-540-24855-2

  • eBook Packages: Springer Book Archive

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