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Strength or Accuracy? Fitness Calculation in Learning Classifier Systems

  • Tim Kovacs
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1813)

Abstract

Wilson’s XCS is a clear departure from earlier classifier systems in terms of the way it calculates the fitness of classifiers for use in the genetic algorithm. Despite the growing body of work on XCS and the advantages claimed for it there has been no detailed comparison of XCS and traditional strength-based systems. This work takes a step towards rectifying this situation by surveying a number of issues related to the change in fitness. I distinguish different definitions of overgenerality for strength and accuracy-based fitness and analyse some implications of the use of accuracy, including an apparent advantage in addressing the explore/exploit problem. I analyse the formation of strong overgenerals, a major problem for strength-based systems, and illustrate their dependence on biased reward functions. I consider motivations for biasing reward functions in single step environments, and show that non-trivial multi step environments have biased Q-functions. I conclude that XCS’s accuracy-based fitness appears to have a number of significant advantages over traditional strength-based fitness.

Keywords

strong overgeneral classifiers biased reward functions accuracy-based fitness XCS complete covering maps exploration 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Tim Kovacs
    • 1
  1. 1.School of Computer Science University of BirminghamBirminghamUK

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