Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
<mark>Journal publication date</mark> | 1/09/2020 |
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<mark>Journal</mark> | European Journal of Operational Research |
Issue number | 2 |
Volume | 285 |
Number of pages | 24 |
Pages (from-to) | 405-428 |
Publication Status | Published |
Early online date | 7/08/19 |
<mark>Original language</mark> | English |
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 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. 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 for 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. Here we will focus on the first of these two categories, selection hyper-heuristics. This paper gives a brief history of this emerging area, reviews contemporary selection hyper-heuristic literature, and discusses recent selection 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.