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Comparing the Niches of CMA-ES, CHC and Pattern Search Using Diverse Benchmarks

  • Darrell Whitley
  • Monte Lunacek
  • Artem Sokolov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4193)

Abstract

This paper explores two questions: 1) On a relatively difficult and varied set of test problems, can we observe differences in evolutionary search algorithm performance related to problem features? 2) How do the evolutionary algorithms compare to Pattern Search algorithms, a more traditional optimization tool popular in the larger scientific community? The results suggest there are consistent differences in algorithm performance that can be related back to problem features. Some new ideas for the construction of benchmark problems are also introduced.

Keywords

Genetic Algorithm Search Space Local Search Problem Feature Mesh Adaptive Direct Search 
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 2006

Authors and Affiliations

  • Darrell Whitley
    • 1
  • Monte Lunacek
    • 1
  • Artem Sokolov
    • 1
  1. 1.Computer ScienceColorado State UniversityFort Collins

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