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Alopex-based mutation strategy in Differential Evolution
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-3425-3837
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-9857-4317
2017 (English)In: 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2017, 1978-1984 p.Conference paper, Published paper (Refereed)
Abstract [en]

Differential Evolution represents a class of evolutionary algorithms that are highly competitive for solving numerical optimization problems. In a Differential Evolution algorithm, there are a few alternative mutation strategies, which may lead to good or a bad performance depending on the property of the problem. A new mutation strategy, called DE/Alopex/1, is proposed in this paper. This mutation strategy distinguishes itself from other mutation strategies in that it uses the fitness values of the individuals in the population in order to calculate the probabilities of move directions. The performance of DE/Alopex/1 has been evaluated on the benchmark suite from CEC2013. The results of the experiments show that DE/Alopex/1 outperforms some state-of-the-art mutation strategies. © 2017 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2017. 1978-1984 p.
Keyword [en]
Alopex, Differential Evolution, Evolutionary Algorithm, Mutation strategy
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:mdh:diva-36297DOI: 10.1109/CEC.2017.7969543Scopus ID: 2-s2.0-85027852098ISBN: 9781509046010 OAI: oai:DiVA.org:mdh-36297DiVA: diva2:1137314
Conference
2017 IEEE Congress on Evolutionary Computation, CEC 2017; Donostia-San Sebastian; Spain; 5 June 2017 through 8 June 2017; Category numberCFP17ICE-ART; Code 129053
Available from: 2017-08-31 Created: 2017-08-31 Last updated: 2017-08-31

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CiteExportLink to record
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Citation style
  • apa
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Language
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