Cooperative co-evolution using LSHADE with restarts for the CEC15 benchmarks

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Abstract

In this paper, we test the performance of an LSHADE Cooperative Co-evolutionary (CC) algorithm using the CEC15 benchmarks. First, we apply the recently proposed Global Differential Grouping (GDG) to learn the underlying interdependencies of the problem variables. GDG divides both separable and non-separable variables among multiple sets. Second, the method adopts the LSHADE algorithm within the CC framework to simultaneously optimize the identified groups. Results are reported for all required problem sizes.

Original languageEnglish
Title of host publication2016 IEEE Congress on Evolutionary Computation, CEC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4810-4814
Number of pages5
ISBN (Electronic)9781509006229
DOIs
StatePublished - 14 Nov 2016
Event2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016

Publication series

Name2016 IEEE Congress on Evolutionary Computation, CEC 2016

Conference

Conference2016 IEEE Congress on Evolutionary Computation, CEC 2016
Country/TerritoryCanada
CityVancouver
Period24/07/1629/07/16

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