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Backpropagation in Accuracy-Based Neural Learning Classifier Systems

  • Conference paper
Learning Classifier Systems (IWLCS 2003, IWLCS 2004, IWLCS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4399))

Abstract

Learning Classifier Systems traditionally use a binary string rule representation with wildcards added to allow for generalizations over the problem encoding. We have presented a neural network-based representation to aid their use in complex problem domains. Here each rule’s condition and action are represented by a small neural network, evolved through the actions of the genetic algorithm. In this paper we present results from the use of backpropagation in conjunction with the genetic algorithm within XCS. After describing the minor changes required to the standard production system functionality, performance is presented from using backpropagation in a number of ways within the system. Results from both continuous and discrete action tasks indicate that significant decreases in the time taken to reach optimal behaviour can be obtained from the incorporation of the local learning algorithm.

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Authors and Affiliations

  1. Faculty of Computing, Engineering & Mathematical Sciences, University of the West of England, Bristol BS16 1QY, U.K.

    Toby O’Hara & Larry Bull

Authors
  1. Toby O’Hara
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  2. Larry Bull
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Editor information

Tim Kovacs Xavier Llorà Keiki Takadama Pier Luca Lanzi Wolfgang Stolzmann Stewart W. Wilson

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© 2007 Springer Berlin Heidelberg

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O’Hara, T., Bull, L. (2007). Backpropagation in Accuracy-Based Neural Learning Classifier Systems. In: Kovacs, T., Llorà, X., Takadama, K., Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds) Learning Classifier Systems. IWLCS IWLCS IWLCS 2003 2004 2005. Lecture Notes in Computer Science(), vol 4399. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71231-2_3

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  • DOI: https://doi.org/10.1007/978-3-540-71231-2_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71230-5

  • Online ISBN: 978-3-540-71231-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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