Research Output

A novel similarity-based mutant vector generation strategy for differential evolution

  The mutant vector generation strategy is an essential component of Differential Evolution (DE), introduced to promote diversity, resulting in exploration of novel areas of the search space. However, it is also responsible for promoting intensification, to improve those solutions located in promising regions. In this paper we introduce a novel similarity-based mutant vector generation strategy for DE, with the goal of inducing a suitable balance between exploration and exploitation, adapting its behaviour depending on the current state of the search. In order to achieve this balance, the strategy considers similarities among individuals in terms of their Euclidean distance in the decision space. A variant of DE incorporating the novel mutant vector generation strategy is compared to well-known explorative and exploitative adaptive DE variants. An experimental evaluation performed on a well-known suite of large-scale continuous problems shows that the new DE algorithm that makes use of the similarity-based approach provides better performance in comparison to the explorative and exploitative DE variants for a wide range of the problems tested, demonstrating the ability of the new component to properly balance exploration and exploitation.

  • Date:

    02 July 2018

  • Publication Status:

    Published

  • Publisher

    Association for Computing Machinery

  • DOI:

    10.1145/3205455.3205628

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    006.3 Artificial intelligence

  • Funders:

    Edinburgh Napier Funded; Spanish Ministry of Economy, Industry and Competitiveness

Citation

Segredo, E., Lalla-Ruiz, E., & Hart, E. (2017). A novel similarity-based mutant vector generation strategy for differential evolution. In H. Aguirre (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference 2018

Authors

Keywords

Global optimization, Differential evolution, Similarity, Diversity, Large-scale optimization

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