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Learning classifier system ensembles with rule-sharing
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AI
DOI：10.1109/TEVC.2006.885163.png)
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Abstract
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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.
Keywords:
data mining
genetic algorithms (GAs)
parallel systems
reinforcement learning
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12
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1.9K
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MACHINE LEARNING
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MACHINE LEARNINGIF2.9
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