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Evolutionary Operator Self-adaptation with Diverse Operators

  • MinHyeok Kim
  • Robert Ian (Bob) McKay
  • Dong-Kyun Kim
  • Xuan Hoai Nguyen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7244)

Abstract

Operator adaptation in evolutionary computation has previously been applied to either small numbers of operators, or larger numbers of fairly similar ones. This paper focuses on adaptation in algorithms offering a diverse range of operators. We compare a number of previously-developed adaptation strategies, together with two that have been specifically designed for this situation. Probability Matching and Adaptive Pursuit methods performed reasonably well in this scenario, but a strategy combining aspects of both performed better. Multi-Arm Bandit techniques performed well when parameter settings were suitably tailored to the problem, but this tailoring was difficult, and performance was very brittle when the parameter settings were varied.

Keywords

Adaptive operator selection Adaptive pursuit Probability matching Multi-armed bandit Evolutionary algorithm 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • MinHyeok Kim
    • 1
  • Robert Ian (Bob) McKay
    • 1
  • Dong-Kyun Kim
    • 2
  • Xuan Hoai Nguyen
    • 3
  1. 1.Seoul National UniversityKorea
  2. 2.University of TorontoCanada
  3. 3.Hanoi UniversityVietnam

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