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Towards Building Block Propagation in XCS: A Negative Result and Its Implications
Kurian K. Tharakunnel, 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
{kurian,butz,deg}@illigal.ge.uiuc.edu
Abstract. The accuracy-based classifier system XCS is currently the most successful learning classifier system. Several recent studies showed that XCS can produce machine-learning competitive results. Nonetheless, until now the evolutionary mechanisms in XCS remained somewhat ill-understood. This study investigates the selectorecombinative capabilities of the current XCS system. We reveal the accuracy dependence of XCS's evolutionary algorithm and identify a fundamental limitation of the accuracy-based fitness approach in certain problems. Implications and future research directions conclude the paper.
LNCS 2724, p. 1906 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003