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dc.contributor.authorEl-Abd, Mohammed
dc.date.accessioned2017-10-02T07:39:07Z
dc.date.available2017-10-02T07:39:07Z
dc.date.issued2016
dc.identifier.urihttp://ieeexplore.ieee.org/document/7744406/
dc.identifier.urihttp://hdl.handle.net/11675/3000
dc.description.abstractIn 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.
dc.publisherThe IEEE Congress on Evolutionary Computation (CEC)
dc.titleCooperative Co-evolution using LSHADE with Restarts For The CEC15 Benchmarks
dc.typeConference Paper
dc.article.pages4810-4814


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