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Genetic programming bloat with dynamic fitness

  • W. B. Langdon
  • R. Poli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1391)

Abstract

In artificial evolution individuals which perform as their parents are usually rewarded identically to their parents. We note that Nature is more dynamic and there may be a penalty to pay for doing the same thing as your parents. We report two sets of experiments where static fitness functions are firstly augmented by a penalty for unchanged offspring and secondly the static fitness case is replaced by randomly generated dynamic test cases. We conclude genetic programming, when evolving artificial ant control programs, is surprisingly little effected by large penalties and program growth is observed in all our experiments.

Keywords

Genetic Programming Food Pellet High Penalty Program Size Program Length 
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 1998

Authors and Affiliations

  • W. B. Langdon
    • 1
  • R. Poli
    • 1
  1. 1.School of Computer ScienceUniversity of BirminghamBirminghamUK

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