Lawrence Bull Larry.Bull@uwe.ac.uk
School Director (Research & Enterprise) and Professor
Learning classifier system ensembles with rule-sharing
Bull, Larry; Studley, Matthew; Bagnall, Anthony; Whittley, Ian
Authors
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Peer Reviewed | Not Peer Reviewed |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 |
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