Accuracy-based learning classifier system ensembles with rule-sharing
Bull, L., Studley, M., Bagnall, A. and Whittley, I. (2007) Accuracy-based learning classifier system ensembles with rule-sharing. IEEE Transactions on Evolutionary Computation, 11 (4). pp. 496-502. ISSN 1089-778X
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Publisher's URL: http://dx.doi.org/10.1109/TEVC.2006.885163
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).