On the use of rule-sharing in learning classifier system ensembles

L Bull; M Studley; AJ Bagnall; IM Whittley

doi:10.1109/CEC.2005.1554739

On the use of rule-sharing in learning classifier system ensembles

L Bull
, M Studley
, AJ Bagnall
, IM Whittley

Research output: Contribution to conference › Paper

9 Citations (Scopus)

Abstract

This paper presents an investigation into exploiting the population-based nature of learning classifier systems for their use within highly-parallel systems. In particular, the use of simple accuracy-based learning classifier systems 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

Original language	English
Pages	612-617
Number of pages	6
DOIs	https://doi.org/10.1109/CEC.2005.1554739
Publication status	Published - Sept 2005
Event	2005 Congress on Evolutionary Computation - Edinburgh, Scotland Duration: 5 Sept 2005 → …

Conference

Conference	2005 Congress on Evolutionary Computation
City	Edinburgh, Scotland
Period	5/09/05 → …

Access to Document

10.1109/CEC.2005.1554739

Cite this

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title = "On the use of rule-sharing in learning classifier system ensembles",

abstract = "This paper presents an investigation into exploiting the population-based nature of learning classifier systems for their use within highly-parallel systems. In particular, the use of simple accuracy-based learning classifier systems 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",

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AB - This paper presents an investigation into exploiting the population-based nature of learning classifier systems for their use within highly-parallel systems. In particular, the use of simple accuracy-based learning classifier systems 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

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