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Recent Advances in Selection Hyper-heuristics

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Recent Advances in Selection Hyper-heuristics. / Drake, John H.; Kheiri, Ahmed; Özcan, Ender; Burke, Edmund K.

In: European Journal of Operational Research, Vol. 285, No. 2, 01.09.2020, p. 405-428.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Drake, JH, Kheiri, A, Özcan, E & Burke, EK 2020, 'Recent Advances in Selection Hyper-heuristics', European Journal of Operational Research, vol. 285, no. 2, pp. 405-428. https://doi.org/10.1016/j.ejor.2019.07.073

APA

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

Vancouver

Drake JH, Kheiri A, Özcan E, Burke EK. Recent Advances in Selection Hyper-heuristics. European Journal of Operational Research. 2020 Sep 1;285(2):405-428. https://doi.org/10.1016/j.ejor.2019.07.073

Author

Drake, John H. ; Kheiri, Ahmed ; Özcan, Ender ; Burke, Edmund K. / Recent Advances in Selection Hyper-heuristics. In: European Journal of Operational Research. 2020 ; Vol. 285, No. 2. pp. 405-428.

Bibtex

@article{d3902e43a3bb47f78dc21b25c61d6dfb,
title = "Recent Advances in Selection Hyper-heuristics",
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 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.",
keywords = "Decision support systems, Artificial intelligence, Machine learning, Metaheuristics, Heuristics",
author = "Drake, {John H.} and Ahmed Kheiri and Ender {\"O}zcan and Burke, {Edmund K.}",
year = "2020",
month = sep,
day = "1",
doi = "10.1016/j.ejor.2019.07.073",
language = "English",
volume = "285",
pages = "405--428",
journal = "European Journal of Operational Research",
issn = "0377-2217",
publisher = "Elsevier Science B.V.",
number = "2",

}

RIS

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 -