Learning Classifier System Ensembles With Rule-Sharing

Larry Bull, Matthew Studley, Anthony J. Bagnall, Ian M. Whittley

Research output: Contribution to journalArticlepeer-review

47 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)496-502
Number of pages7
JournalIEEE Transactions on Evolutionary Computation
Volume11
Issue number4
DOIs
Publication statusPublished - 2007

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