Learning classifier system ensembles with rule-sharing

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

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.

• 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• Peer Reviewed: Not Peer Reviewed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