Research Paper

Evolutionary Intelligence

, Volume 3, Issue 1, pp 31-50

A learning classifier system with mutual-information-based fitness

  • Robert Elliott SmithAffiliated withDepartment of Computer Science, University College London
  • , Max Kun JiangAffiliated withDepartment of Computer Science, University College London Email author 
  • , Jaume BacarditAffiliated withSchool of Computer Science, University of Nottingham
  • , Michael StoutAffiliated withSchool of Computer Science, University of Nottingham
  • , Natalio KrasnogorAffiliated withSchool of Computer Science, University of Nottingham
  • , Jonathan D. HirstAffiliated withSchool of Chemistry, University of Nottingham

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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