How to use crowding selection in Grammar-based Classifier System

  • Authors:

  • Olgierd Unold;Lukasz Cielecki

  • Affiliations:

  • Wroclaw University of Technology, Poland;Wroclaw University of Technology, Poland

  • Venue:
  • ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
  • Year:
  • 2005

  • GCS with Real-Valued Input

    IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks

  • Real-Valued GCS Classifier System

    International Journal of Applied Mathematics and Computer Science

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Abstract

Grammar-based classifier system (GCS) is a new version of Learning Classifier Systems (LCS) in which classifiers are represented by context-free grammar in Chomsky Normal Form. GCS evolves one grammar during induction (the Michigan approach) what gives it an ability to find the proper set of rules very quickly. However it is quite sensitive to any variations of learning parameters. This paper investigates the role of crowding selection in GCS. To evaluate the performance of GCS depending on crowding factor and crowding subpopulation we used context-free language in the form of so-called toy language. The set of experiments was performed to obtain the answer for the raised question in the title.