Toward a better understanding of rule initialization and deletion

Tim Kovacs, Bull Larry

Research output: Contribution to journalArticle (Academic Journal)peer-review

Abstract

A number of heuristics have been used in Learning Classifier Systems to initialise parameters of new rules, to adjust fitness of parent rules when they generate offspring, and to select rules for deletion. Some have not been studied in the literature before. We study the interaction of these heuristics in an attempt to improve performance and detect any unnecessary methods. We evaluate the two published methods for initialisation of new rules in XCS and find the one based on parental values results in better evolutionary search but larger population sizes than the one based on population means. In preliminary work we demonstrate that when the difficulty of the 6 multiplexer is increased by reducing the population size limit and turning off subsumption we can improve performance by discounting the fitness of both parents and children.
Translated title of the contributionToward a better understanding of rule initialization and deletion
Original languageEnglish
Pages (from-to)2777-2780
JournalWorkshop Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)
Publication statusPublished - 2007

Bibliographical note

ISBN: 9781595936981
Publisher: ACM
Name and Venue of Conference: Workshop Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)
Other identifier: 2000682

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