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Behavioural Diversity and Filtering in GP Navigation Problems

  • David Jackson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5481)

Abstract

Promoting and maintaining diversity in a population is considered an important element of evolutionary computing systems, and genetic programpming (GP) is no exception. Diversity metrics in GP are usually based on structural program characteristics, but even when based on behaviour they almost always relate to fitness. We deviate from this in two ways: firstly, by considering an alternative view of diversity based on the actual activity performed during execution, irrespective of fitness; and secondly, by examining the effects of applying associated diversity-enhancing algorithms to the initial population only. Used together with an extension to this approach that provides for additional filtering of candidate population members, the techniques offer significant performance improvements when applied to the Santa Fe artificial ant problem and a maze navigation problem.

Keywords

Genetic Program Initial Population Behavioural Diversity Diversity Metrics Tree Depth 
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 2009

Authors and Affiliations

  • David Jackson
    • 1
  1. 1.Dept. of Computer ScienceUniversity of LiverpoolLiverpoolUnited Kingdom

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