Operator Self-adaptation in Genetic Programming

  • Min Hyeok Kim
  • Robert Ian (Bob) McKay
  • Nguyen Xuan Hoai
  • Kangil Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6621)


We investigate the application of adaptive operator selection rates to Genetic Programming. Results confirm those from other areas of evolutionary algorithms: adaptive rate selection out-performs non-adaptive methods, and among adaptive methods, adaptive pursuit out-performs probability matching. Adaptive pursuit combined with a reward policy that rewards the overall fitness change in the elite worked best of the strategies tested, though not uniformly on all problems.


Genetic Programming Adaptive Operator Selection Adaptive Pursuit Probability Matching Evolutionary Algorithm Tree Adjoining Grammar Grammar Guided Genetic Programming 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Min Hyeok Kim
    • 1
  • Robert Ian (Bob) McKay
    • 1
  • Nguyen Xuan Hoai
    • 2
  • Kangil Kim
    • 1
  1. 1.Seoul National UniversityKorea
  2. 2.Hanoi UniversityVietnam

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