Dökeroğlu, TanselKüçükyılmaz, TayfunTalbi, El-Ghazali2024-06-052024-06-052024Dökeroğlu, Tansel; Küçükyılmaz, Tayfun; Talbi, El-Ghazali (2024). "Hyper-heuristics: A survey and taxonomy", Computers and Industrial Engineering, Vol. 187.0360-8352http://hdl.handle.net/20.500.12416/8472Hyper-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.eninfo:eu-repo/semantics/openAccessHyper-HeuristicsMetaheuristicsOptimizationSurveyArticle18710.1016/j.cie.2023.109815