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Evolutionary Intelligence

, Volume 3, Issue 1, pp 31–50 | Cite as

A learning classifier system with mutual-information-based fitness

  • Robert Elliott Smith
  • Max Kun Jiang
  • Jaume Bacardit
  • Michael Stout
  • Natalio Krasnogor
  • Jonathan D. Hirst
Research Paper

Abstract

This paper introduces a new variety of learning classifier system (LCS), called MILCS, which utilizes mutual information as fitness feedback. Unlike most LCSs, MILCS is specifically designed for supervised learning. We present experimental results, and contrast them to results from XCS, UCS, GAssist, BioHEL, C4.5 and Naïve Bayes. We discuss the explanatory power of the resulting rule sets. MILCS is also shown to promote the discovery of default hierarchies, an important advantage of LCSs. Final comments include future directions for this research, including investigations in neural networks and other systems.

Keywords

Evolutionary computation Learning classifier systems Machine learning Information theory Mutual information Supervised learning Protein structure prediction Explanatory power 

Notes

Acknowledgments

The authors greatly acknowledge support provided by the UK Engineering and Physical Sciences Research Council (EPSRC) under grant GR/T07541/01 & GR/T07534/01.

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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Robert Elliott Smith
    • 1
  • Max Kun Jiang
    • 1
  • Jaume Bacardit
    • 2
  • Michael Stout
    • 2
  • Natalio Krasnogor
    • 2
  • Jonathan D. Hirst
    • 3
  1. 1.Department of Computer ScienceUniversity College LondonLondonUK
  2. 2.School of Computer ScienceUniversity of NottinghamNottinghamUK
  3. 3.School of ChemistryUniversity of NottinghamNottinghamUK

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