Learning Classifier System Ensembles With Rule-Sharing

Bull, L, Studley, M, Bagnall, AJ and Whittley, IM (2007) Learning Classifier System Ensembles With Rule-Sharing. IEEE Transactions on Evolutionary Computation, 11 (4). pp. 496-502. ISSN 1089-778X

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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.

Item Type: Article
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: Vishal Gautam
Date Deposited: 07 Mar 2011 13:39
Last Modified: 24 Jul 2019 15:26
URI: https://ueaeprints.uea.ac.uk/id/eprint/22527
DOI: 10.1109/TEVC.2006.885163

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