Differential Evolution with Reversible Linear Transformations

Jakub M. Tomczak, Ewelina Wȩglarz-Tomczak, Agoston E. Eiben

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Differential evolution (DE) is a well-known type of evolutionary algorithms (EA). Similarly to other EA variants it can suffer from small populations and loose diversity too quickly. This paper presents a new approach to mitigate this issue: We propose to generate new candidate solutions by utilizing reversible linear transformations applied to a triplet of solutions from the population. In other words, the population is enlarged by using newly generated individuals without evaluating their fitness. We assess our methods on three problems: (i) benchmark function optimization, (ii) discovering parameter values of the gene repressilator system, (iii) learning neural networks. The empirical results indicate that the proposed approach outperforms vanilla DE and a version of DE with applying differential mutation three times on all testbeds.

Original languageEnglish
Title of host publicationGECCO '20
Subtitle of host publicationProceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery, Inc
Pages205-206
Number of pages2
ISBN (Electronic)9781450371278
DOIs
Publication statusPublished - Jul 2020
Event2020 Genetic and Evolutionary Computation Conference, GECCO 2020 - Cancun, Mexico
Duration: 8 Jul 202012 Jul 2020

Conference

Conference2020 Genetic and Evolutionary Computation Conference, GECCO 2020
CountryMexico
CityCancun
Period8/07/2012/07/20

Keywords

  • Black-box optimization
  • Population-based algorithms
  • Reversible computation

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