Accuracy-based learning classifier system ensembles with rule-sharing

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

Abstract

Learning Classifier Systems (LCS) are a method of evolving compact rule-sets using reinforcement learning. This paper presents an investigation into exploiting the population-based nature of LCS for their use within highly-parallel systems, such as the National Supercomputer. In particular, the use of simple accuracy-based LCS within the ensemble machine approach is examined.

Results indicate that inclusion of a rule migration mechanism inspired by Parallel Genetic Algorithms improves learning speed in comparison to an equivalent single system. A mechanism which exploits the underlying generalization mechanism of LCS is then shown further to improve performance, particularly as task complexity increases. Finally, considerably better than linear speed-up is demonstrated on a well-known benchmark task.

As a result, LCS with rule-sharing was incorporated into the Supercomputer Data Mining Toolkit (EPSRC project (GR/T18455/01)) and this technique is available for use by the UK academic community, currently finding use in data mining e.g. Olympic athlete data (EP/43488/01).

• Journal Article Type: Article
• Publication Date: Sep 1, 2007
• Journal: IEEE Transactions on Evolutionary Computation
• Print ISSN: 1089-778X
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 11
• Issue: 4
• Pages: 496-502
• DOI: https://doi.org/10.1109/TEVC.2006.885163
• Keywords: accuracy-based learning classifier system, rule-sharing
• Public URL: https://uwe-repository.worktribe.com/output/1025103
• Publisher URL: http://dx.doi.org/10.1109/TEVC.2006.885163