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Yang, Zan, Qiu, Haobo, Gao, Liang, Xu, Danyang, Liu, Yuanhao (2023) A general framework of surrogate-assisted evolutionary algorithms for solving computationally expensive constrained optimization problems. Information Sciences, 619. 491-508 doi:10.1016/j.ins.2022.11.021

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Reference TypeJournal (article/letter/editorial)
TitleA general framework of surrogate-assisted evolutionary algorithms for solving computationally expensive constrained optimization problems
JournalInformation Sciences
AuthorsYang, ZanAuthor
Qiu, HaoboAuthor
Gao, LiangAuthor
Xu, DanyangAuthor
Liu, YuanhaoAuthor
Year2023 (January)Volume619
PublisherElsevier BV
DOIdoi:10.1016/j.ins.2022.11.021Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID15511387Long-form Identifiermindat:1:5:15511387:1
GUID0
Full ReferenceYang, Zan, Qiu, Haobo, Gao, Liang, Xu, Danyang, Liu, Yuanhao (2023) A general framework of surrogate-assisted evolutionary algorithms for solving computationally expensive constrained optimization problems. Information Sciences, 619. 491-508 doi:10.1016/j.ins.2022.11.021
Plain TextYang, Zan, Qiu, Haobo, Gao, Liang, Xu, Danyang, Liu, Yuanhao (2023) A general framework of surrogate-assisted evolutionary algorithms for solving computationally expensive constrained optimization problems. Information Sciences, 619. 491-508 doi:10.1016/j.ins.2022.11.021
In(2023) Information Sciences Vol. 619. Elsevier BV

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