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Hyper-heuristics: A survey and taxonomy

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dc.contributor.author Dökeroğlu, Tansel
dc.contributor.author Küçükyılmaz, Tayfun
dc.contributor.author Talbi, El-Ghazali
dc.date.accessioned 2024-06-05T10:59:46Z
dc.date.available 2024-06-05T10:59:46Z
dc.date.issued 2024-01
dc.identifier.citation Dökeroğlu, Tansel; Küçükyılmaz, Tayfun; Talbi, El-Ghazali (2024). "Hyper-heuristics: A survey and taxonomy", Computers and Industrial Engineering, Vol. 187. tr_TR
dc.identifier.issn 0360-8352
dc.identifier.uri http://hdl.handle.net/20.500.12416/8472
dc.description.abstract Hyper-heuristics are search techniques for selecting, generating, and sequencing (meta)-heuristics to solve challenging optimization problems. They differ from traditional (meta)-heuristics methods, which primarily employ search space-based optimization strategies. Due to the remarkable performance of hyper-heuristics in multi-objective and machine learning-based optimization, there has been an increasing interest in this field. With a fresh perspective, our work extends the current taxonomy and presents an overview of the most significant hyper-heuristic studies of the last two decades. Four categories under which we analyze hyper-heuristics are selection hyper-heuristics (including machine learning techniques), low-level heuristics, target optimization problems, and parallel hyper-heuristics. Future research prospects, trends, and prospective fields of study are also explored. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1016/j.cie.2023.109815 tr_TR
dc.rights info:eu-repo/semantics/openAccess tr_TR
dc.subject Hyper-Heuristics tr_TR
dc.subject Metaheuristics tr_TR
dc.subject Optimization tr_TR
dc.subject Survey tr_TR
dc.title Hyper-heuristics: A survey and taxonomy tr_TR
dc.type article tr_TR
dc.relation.journal Computers and Industrial Engineering tr_TR
dc.contributor.authorID 234173 tr_TR
dc.identifier.volume 187 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Yazılım Mühendisliği Bölümü tr_TR


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