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

Segredo, Eduardo; Lalla-Ruiz, Eduardo; Hart, Emma

Authors

Eduardo Segredo

Eduardo Lalla-Ruiz



Contributors

Hernan Aguirre
Editor

Abstract

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.

Citation

Segredo, E., Lalla-Ruiz, E., & Hart, E. (2018). A novel similarity-based mutant vector generation strategy for differential evolution. In H. Aguirre (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference 2018. https://doi.org/10.1145/3205455.3205628

Conference Name The Genetic and Evolutionary Computation Conference 2018 (GECCO 2018)
Conference Location Kyoto, Japan
Start Date Jul 15, 2018
End Date Jul 19, 2018
Acceptance Date Mar 24, 2018
Online Publication Date Jul 2, 2018
Publication Date 2018
Deposit Date Apr 3, 2018
Publicly Available Date Mar 29, 2024
Journal Proceedings of the Genetic and Evolutionary Computation Conference
Publisher Association for Computing Machinery (ACM)
Book Title Proceedings of the Genetic and Evolutionary Computation Conference 2018
ISBN 9781450356183
DOI https://doi.org/10.1145/3205455.3205628
Keywords Global optimization, Differential evolution, Similarity, Diversity, Large-scale optimization
Public URL http://researchrepository.napier.ac.uk/Output/1140654

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Copyright Statement
© ACM 2018. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Segredo, E., Lalla-Ruiz, E., & Hart, E. (2018). A Novel Similarity-based Mutant Vector Generation Strategy for Differential Evolution. In H. Aguirre (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference 2018, http://dx.doi.org/10.1145/3205455.3205628







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