Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Recent Advances in Selection Hyper-heuristics. / Drake, John H.; Kheiri, Ahmed; Özcan, Ender et al.
In: European Journal of Operational Research, Vol. 285, No. 2, 01.09.2020, p. 405-428.Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Recent Advances in Selection Hyper-heuristics
AU - Drake, John H.
AU - Kheiri, Ahmed
AU - Özcan, Ender
AU - Burke, Edmund K.
PY - 2020/9/1
Y1 - 2020/9/1
N2 - 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.
AB - 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.
KW - Decision support systems
KW - Artificial intelligence
KW - Machine learning
KW - Metaheuristics
KW - Heuristics
U2 - 10.1016/j.ejor.2019.07.073
DO - 10.1016/j.ejor.2019.07.073
M3 - Journal article
VL - 285
SP - 405
EP - 428
JO - European Journal of Operational Research
JF - European Journal of Operational Research
SN - 0377-2217
IS - 2
ER -