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Performance Assessment of Recursive Probability Matching for Adaptive Operator Selection in Differential Evolution

  • Mudita Sharma
  • Manuel López-Ibáñez
  • Dimitar Kazakov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11102)

Abstract

Probability Matching is one of the most successful methods for adaptive operator selection (AOS), that is, online parameter control, in evolutionary algorithms. In this paper, we propose a variant of Probability Matching, called Recursive Probability Matching (RecPM-AOS), that estimates reward based on progress in past generations and estimates quality based on expected quality of possible selection of operators in the past. We apply RecPM-AOS to the online selection of mutation strategies in differential evolution (DE) on the bbob benchmark functions. The new method is compared with two AOS methods, namely, PM-AdapSS, which utilises probability matching with relative fitness improvement, and F-AUC, which combines the concept of area under the curve with a multi-arm bandit algorithm. Experimental results show that the new tuned RecPM-AOS method is the most effective at identifying the best mutation strategy to be used by DE in solving most functions in bbob among the AOS methods.

Keywords

Parameter control Probability matching Differential evolution Black-box optimisation 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Mudita Sharma
    • 1
  • Manuel López-Ibáñez
    • 2
  • Dimitar Kazakov
    • 1
  1. 1.University of YorkYorkUK
  2. 2.University of ManchesterManchesterUK

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