Recent advances in selection hyper-heuristics

Drake, John H.; Kheiri, Ahmed; �zcan, Ender; Burke, Edmund K.

Abstract

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.

Citation

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. https://doi.org/10.1016/j.ejor.2019.07.073

• 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
• DOI: https://doi.org/10.1016/j.ejor.2019.07.073
• Keywords: Management Science and Operations Research; Modelling and Simulation; Information Systems and Management
• Public URL: https://nottingham-repository.worktribe.com/output/2452757
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0377221719306526