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• Peer Reviewed: Not Peer Reviewed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, learning, artificial intelligence, parallel algorithms, pattern classification, boolean logic, data mining, genetic algorithm, learning classifier system, parallel system, rule migration mechanism, data mining, genetic algorithms, parallel systems, reinforcement learning
• Public URL: https://uwe-repository.worktribe.com/output/1032038
• Publisher URL: http://dx.doi.org/10.1109/TEVC.2006.885163