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Investigating Scaling of an Abstracted LCS Utilising Ternary and S-Expression Alphabets

  • Charalambos Ioannides
  • Will Browne
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4998)

Abstract

Utilising the expressive power of S-Expressions in Learning Classifier Systems often prohibitively increases the search space due to increased flexibility of the encoding. This work shows that selection of appropriate S-Expression functions through domain knowledge improves scaling in problems, as expected. It is also known that simple alphabets perform well on relatively small sized problems in a domain, e.g. ternary alphabet in the 6, 11 and 20 bit MUX domain. Once fit ternary rules have been formed it was investigated whether higher order learning was possible and whether this staged learning facilitated selection of appropriate functions in complex alphabets, e.g. selection of S-Expression functions. This novel methodology is shown to provide compact results (135-MUX) and exhibits potential for scaling well (1034-MUX), but is only a small step towards introducing abstraction to LCS.

Keywords

Genetic Programming Learn Classifier System Accurate Classifier High Order Learning Multiplexer Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Charalambos Ioannides
    • 1
  • Will Browne
    • 1
  1. 1.CyberneticsUniversity of ReadingReadingUK

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