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Recent Advances in Selection Hyper-heuristics

Research output: Contribution to journalJournal article

E-pub ahead of print
<mark>Journal publication date</mark>7/08/2019
<mark>Journal</mark>European Journal of Operational Research
Publication statusE-pub ahead of print
Early online date7/08/19
Original languageEnglish

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.