Improved learning in genetic rule-based classifier systems

Alastair D. McAulay, Jae C Oh

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

1 Scopus citations

Abstract

Many learning algorithms tend to converge into local minima that often represent partial solutions. Schemes are presented that greatly minimize the risk of converging to a partial solution and maximize the rule discovery process for rule-based learning. For the experiments, a genetic algorithm rule-based learning system called a classifier system has been used. The new strategies are supported by presenting accelerations and completion of learning in higher order letter image classification problems.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE International Conference on Systems, Man and Cybernetics
PublisherIEEE Computer Society
Pages1393-1398
Number of pages6
Volume2
StatePublished - 1991
Externally publishedYes
EventConference Proceedings of the 1991 IEEE International Conference on Systems, Man, and Cybernetics - Charlottesville, VA, USA
Duration: Oct 13 1991Oct 16 1991

Other

OtherConference Proceedings of the 1991 IEEE International Conference on Systems, Man, and Cybernetics
CityCharlottesville, VA, USA
Period10/13/9110/16/91

ASJC Scopus subject areas

  • Hardware and Architecture
  • Control and Systems Engineering

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  • Cite this

    McAulay, A. D., & Oh, J. C. (1991). Improved learning in genetic rule-based classifier systems. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (Vol. 2, pp. 1393-1398). IEEE Computer Society.