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A fuzzy classifier system using the Pittsburgh approach

  • Classifier Systems
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Parallel Problem Solving from Nature — PPSN III (PPSN 1994)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 866))

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Abstract

This paper describes a fuzzy classifier system using the Pittsburgh model. In this model genetic operations and fitness assignment apply to complete rule-sets, rather than to individual rules, thus overcoming the problem of conflicting individual and collective interests of classifiers. The fuzzy classifier system presented here dynamically adjusts both membership functions and fuzzy relations. A modified crossover operator for particular use in Pittsburgh-style fuzzy classifier systems, with variable length rule-sets, is introduced and evaluated. Experimental results of the new system, which appear encouraging, are presented and discussed.

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Author information

Authors and Affiliations

  1. University of the West of England, BS16 1QY, Bristol, UK

    Brian Carse & Terence C. Fogarty

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  1. Brian Carse
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  2. Terence C. Fogarty
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Yuval Davidor Hans-Paul Schwefel Reinhard Männer

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© 1994 Springer-Verlag Berlin Heidelberg

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Carse, B., Fogarty, T.C. (1994). A fuzzy classifier system using the Pittsburgh approach. In: Davidor, Y., Schwefel, HP., Männer, R. (eds) Parallel Problem Solving from Nature — PPSN III. PPSN 1994. Lecture Notes in Computer Science, vol 866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58484-6_270

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  • DOI: https://doi.org/10.1007/3-540-58484-6_270

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-58484-1

  • Online ISBN: 978-3-540-49001-2

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