ZCS redux.

Full Abstract

Learning classifier systems traditionally use genetic algorithms to facilitate rule discovery, where rule fitness is payoff based. Current research has shifted to the use of accuracy-based fitness. This paper re-examines the use of a particular payoff-based learning classifier system--ZCS. By using simple difference equation models of ZCS, we show that this system is capable of optimal performance subject to appropriate parameter settings. This is demonstrated for both single- and multistep tasks. Optimal performance of ZCS in well-known, multistep maze tasks is then presented to support the findings from the models.

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Author information

Author/s: Bull, Larry (L); Hurst, Jacob (J);

Affiliation: Faculty of Computing, Engineering and Mathematical Sciences, University of the West of England, Bristol BS16 1QY, UK. larry.bull@uwe.ac.uk

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal: Evolutionary computation (Evol Comput), published in United States. (Language: eng)

Reference: 2002-; vol 10 (issue 2) : pp 185-205

Dates: Created 2002/08/15; Completed 2002/12/13; Revised 2006/11/15;

PMID: 12180172, status: MEDLINE (last retrieval date: 11/6/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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