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Bounding the Population Size in XCS to Ensure Reproductive Opportunities
Martin V. Butz and David E. Goldberg
Illinois Genetic Algorithms Laboratory (IlliGAL)
University of Illinois at Urbana-Champaign
104 S. Mathews
61801 Urbana, IL, USA
{butz,deg}@illigal.ge.uiuc.edu
Abstract. Despite several recent successful comparisons and applications of the accuracy-based learning classifier system XCS, it is hardly understood how crucial parameters should be set in XCS nor how XCS can be expect to scale up in larger problems. Previous research identified a covering challenge in XCS that needs to be obeyed to ensure that the genetic learning process takes place. Furthermore, a schema challenge was identified that, once obeyed, ensures the existence of accurate classifiers. This paper departs from these challenges deriving a reproductive opportunity bound. The bound assures that more accurate classifiers get a chance for reproduction. The relation to the previous bounds as well as to the specificity pressure in XCS are discussed as well. The derived bound shows that XCS scales in a machine learning competitive way.
LNCS 2724, p. 1844 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003