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Recent advances in selection hyper-heuristics


John H. Drake

Ahmed Kheiri

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Professor of Computer Science and Operational Research

Edmund K. Burke


Hyper-heuristics have emerged as a way to raise the level of generality of search techniques for computational search problems. This is in contrast to many approaches, which represent customised methods for a single problem domain or a narrow class of problem instances. The current state-of-the-art in hyper-heuristic research comprises a set of methods that are broadly concerned with intelligently selecting or generating a suitable heuristic in a given situation. Hyper-heuristics can be considered as search methods that operate on lower-level heuristics or heuristic components, and can be categorised into two main classes: heuristic selection and heuristic generation. The term hyper-heuristic was defined in the early 2000s as a heuristic to choose heuristics, but the idea of designing high-level heuristic methodologies can be traced back to the early 1960s. This paper gives a brief history of this emerging area, reviews contemporary hyper-heuristic literature, and discusses recent hyper-heuristic frameworks. In addition, the existing classification of selection hyper-heuristics is extended, in order to reflect the nature of the challenges faced in contemporary research. Unlike the survey on hyper-heuristics published in 2013, this paper focuses only on selection hyper-heuristics and presents critical discussion, current research trends and directions for future research.


Drake, J. H., Kheiri, A., Özcan, E., & Burke, E. K. (2020). Recent advances in selection hyper-heuristics. European Journal of Operational Research, 285(2), 405-428.

Journal Article Type Article
Acceptance Date Jul 31, 2019
Online Publication Date Aug 7, 2019
Publication Date Sep 1, 2020
Deposit Date Nov 1, 2019
Publicly Available Date Aug 8, 2021
Journal European Journal of Operational Research
Print ISSN 0377-2217
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 285
Issue 2
Pages 405-428
Keywords Management Science and Operations Research; Modelling and Simulation; Information Systems and Management
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