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State of XCS Classifier System Research

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Book cover Learning Classifier Systems (IWLCS 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1813))

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Abstract

XCS is a new kind of learning classifier system that differs from the traditional kind primarily in its definition of classifier fitness and its relation to contemporary reinforcement learning. Advantages of XCS include improved performance and an ability to form accurate maximal generalizations. This paper reviews recent research on XCS with respect to representation, internal state, predictive modeling, noise, and underlying theory and technique. A notation for environmental regularities is introduced.

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Author information

Authors and Affiliations

  1. The University of Illinois, Urbana-Champaign, IL, 61801, USA

    Stewart W. Wilson

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

    Stewart W. Wilson

Authors
  1. Stewart W. Wilson
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Editor information

Editors and Affiliations

  1. Dipartimento di Elettronica ed Informatzione, Politecnico di Milano, Piazza Leonardo da Vinci n. 32, 20133, Milano, Italy

    Pier Luca Lanzi

  2. Institut für Psychologie III, Universität Würzburg, Röntgenring 11, 97070, Würzburg, Germany

    Wolfgang Stolzmann

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

    Stewart W. Wilson

  4. Department of General Engineering, University of Illinois at Urbana-Champaign, USA

    Stewart W. Wilson

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© 2000 Springer-Verlag Berlin Heidelberg

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Wilson, S.W. (2000). State of XCS Classifier System Research. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds) Learning Classifier Systems. IWLCS 1999. Lecture Notes in Computer Science(), vol 1813. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45027-0_3

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  • DOI: https://doi.org/10.1007/3-540-45027-0_3

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67729-1

  • Online ISBN: 978-3-540-45027-6

  • eBook Packages: Springer Book Archive

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