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Learning classifier system ensembles with rule-sharing

Bull, Larry; Studley, Matthew; Bagnall, Anthony; Whittley, Ian

Authors

Lawrence Bull Larry.Bull@uwe.ac.uk
School Director (Research & Enterprise) and Professor

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Dr Matthew Studley Matthew2.Studley@uwe.ac.uk
Professor of Ethics & Technology/School Director (Research & Enterprise)

Anthony Bagnall

Ian Whittley



Abstract

This paper presents an investigation into exploiting the population-based nature of learning classifier systems (LCSs) for their use within highly parallel systems. In particular, the use of simple payoff and accuracy-based LCSs within the ensemble machine approach is examined. Results indicate that inclusion of a rule migration mechanism inspired by parallel genetic algorithms is an effective way to improve learning speed in comparison to equivalent single systems. Presentation of a mechanism which exploits the underlying niche-based generalization mechanism of accuracy-based systems is then shown to further improve their performance, particularly, as task complexity increases. This is not found to be the case for payoff-based systems. Finally, considerably better than linear speedup is demonstrated with the accuracy-based systems on a version of the well-known Boolean logic benchmark task used throughout. © 2006 IEEE.

Citation

Bull, L., Studley, M., Bagnall, A., & Whittley, I. (2007). Learning classifier system ensembles with rule-sharing. IEEE Transactions on Evolutionary Computation, 11(4), 496-502. https://doi.org/10.1109/TEVC.2006.885163

Journal Article Type Article
Publication Date Aug 1, 2007
Journal IEEE Transactions on Evolutionary Computation
Print ISSN 1089-778X
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Not Peer Reviewed
Volume 11
Issue 4
Pages 496-502
DOI https://doi.org/10.1109/TEVC.2006.885163
Keywords boolean functions, data analysis, data mining, evolutionary computation, genetic algorithms, large-scale systems, machine learning, machine learning algorithms, parallel processing, production systems, boolean algebra, data mining, genetic algorithms, lea
Public URL https://uwe-repository.worktribe.com/output/1032038
Publisher URL http://dx.doi.org/10.1109/TEVC.2006.885163