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International Workshop on Learning Classifier Systems

IWLCS 2000: Advances in Learning Classifier Systems pp 29–36Cite as

Simple Markov Models of the Genetic Algorithm in Classifier Systems: Multi-step Tasks

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1996))

Abstract

Michigan-style Classifier Systems use Genetic Algorithms to facilitate rule-discovery. This paper presents a simple Markov model of the algorithm in such systems, with the aim of examining the effects of different types of interdependence between niches in multi-step tasks. Using the model it is shown that the existence of, what is here termed, partner rule variance can have significant and detrimental effects on the Genetic Algorithm’s expected behaviour. Suggestions are made as to how to reduce these effects, making connections with other recent work in the area.

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Author information

Authors and Affiliations

  1. Intelligent Computer Systems Centre Faculty of Computer Studies & Mathematics, University of the West of England, BS16 1QY, Bristol, UK

    Larry Bull

Authors
  1. Larry Bull
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Editor information

Editors and Affiliations

  1. Politecnico di Milano Dipartimento di Elettronica e Informazione, Artificial Intelligence and Robotics Laboratory, Piazza Leonardo da Vinci 32, 20133, Milan, Italy

    Pier Luca Lanzi

  2. DaimlerChrysler AG Research and Technology,Cognition and Robotics, Alt-Moabit 96A, 10559, Berlin, Germany

    Wolfgang Stolzmann

  3. Prediction Dynamics, Concord, MA 01742, USA

    Stewart W. Wilson

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© 2001 Springer-Verlag Berlin Heidelberg

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Bull, L. (2001). Simple Markov Models of the Genetic Algorithm in Classifier Systems: Multi-step Tasks. In: Luca Lanzi, P., Stolzmann, W., Wilson, S.W. (eds) Advances in Learning Classifier Systems. IWLCS 2000. Lecture Notes in Computer Science(), vol 1996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44640-0_3

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  • DOI: https://doi.org/10.1007/3-540-44640-0_3

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42437-6

  • Online ISBN: 978-3-540-44640-8

  • eBook Packages: Springer Book Archive

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