On accuracy-based fitness
type: Item
instance of: scholarly article
title: On accuracy-based fitness
zbMATH Open document ID: 1027.68657
author: Yanyan Li
published in: Soft Computing
publication date: 2003-03-18T00:00:00Z
review text: Learning classifier systems use genetic algorithms to facilitate rule-discovery, where rule fitness has traditionally been payoff prediction-based. Current research has shifted to the use of accuracy-based fitness. This paper presents a simple Markov model of the algorithm in such systems, allowing comparison between the two forms of rule utility measure. Using a single-step task the previously discussed benefits of accuracy over prediction are clearly shown with regard to overgeneral rules. The effects of a niche-based algorithm (maximal generality) are also briefly examined, as are the effects of mutation under the two fitness schemes. Finally, the behaviour of the genetic algorithm during the solution of multi-step tasks is investigated.
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 68U99
zbMATH DE Number: 1883898
zbMATH Keywords: learning classifier systems
zbMATH Keywords: genetic algorithms
MaRDI profile type: Publication
full work available at URL: s005000100112
OpenAlex ID: W2065759098
DOI: 10.1007/S005000100112
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description: scientific article; zbMATH DE number 1883898