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

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Title of host publication15th Intl Conf. on Parallel Problem Solving from Nature
DateE-pub ahead of print - 21 Aug 2018
DatePublished (current) - Sep 2018
Pages321-333
Number of pages13
PublisherSPRINGER-VERLAG BERLIN
Original languageEnglish

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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.

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© Springer Nature Switzerland AG 2018. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.

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