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 contribution | Toward a better understanding of rule initialization and deletion |
---|---|
Original language | English |
Pages (from-to) | 2777-2780 |
Journal | Workshop Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) |
Publication status | Published - 2007 |
Bibliographical note
ISBN: 9781595936981Publisher: ACM
Name and Venue of Conference: Workshop Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)
Other identifier: 2000682