Information theoretic fitness measures for learning classifier systems

Karthik Kuber, Chilukuri K. Mohan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

During the course of evolution in a genetic algorithm, and in particular a Learning Classifier System, we conjecture that there is benefit in using multiple fitness functions including information theoretic fitness measures. We discuss multiple ways to evaluate rules and groups of rules, some utilizing the data, and others without. For example, to evaluate the fitness of any rule against a set of data, we propose the use of information theoretic fitness measures Sufficiency and Quality, which are derived from the classical concepts of entropy and mutual information, in order to assign fitness values to classifiers. We present the experimental results for one of the methods, viz. evaluating individual rules against the data showing that their use reduces the number of generations needed to achieve peak performance.

Original languageEnglish (US)
Title of host publicationProceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10 - Companion Publication
Pages1885-1892
Number of pages8
DOIs
StatePublished - Aug 30 2010
Event12th Annual Genetic and Evolutionary Computation Conference, GECCO-2010 - Portland, OR, United States
Duration: Jul 7 2010Jul 11 2010

Publication series

NameProceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10 - Companion Publication

Other

Other12th Annual Genetic and Evolutionary Computation Conference, GECCO-2010
CountryUnited States
CityPortland, OR
Period7/7/107/11/10

Keywords

  • Genetic algorithms
  • Information theory
  • Learning classifier systems
  • Quality
  • Sufficiency
  • XCS

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science

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  • Cite this

    Kuber, K., & Mohan, C. K. (2010). Information theoretic fitness measures for learning classifier systems. In Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10 - Companion Publication (pp. 1885-1892). (Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10 - Companion Publication). https://doi.org/10.1145/1830761.1830821