An iterated multi-stage selection hyper-heuristic
There is a growing interest towards the design of reusable general purpose search methods that are applicable to different problems instead of tailored solutions to a single particular problem. Hyper-heuristics have emerged as such high level methods that explore the space formed by a set of heuristi...
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| Format: | Article |
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Elsevier
2016
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| Online Access: | https://eprints.nottingham.ac.uk/30192/ |
| _version_ | 1848793939607289856 |
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| author | Kheiri, Ahmed Özcan, Ender |
| author_facet | Kheiri, Ahmed Özcan, Ender |
| author_sort | Kheiri, Ahmed |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | There is a growing interest towards the design of reusable general purpose search methods that are applicable to different problems instead of tailored solutions to a single particular problem. Hyper-heuristics have emerged as such high level methods that explore the space formed by a set of heuristics (move operators) or heuristic components for solving computationally hard problems. A selection hyper-heuristic mixes and controls a predefined set of low level heuristics with the goal of improving an initially generated solution by choosing and applying an appropriate heuristic to a solution in hand and deciding whether to accept or reject the new solution at each step under an iterative framework. Designing an adaptive control mechanism for the heuristic selection and combining it with a suitable acceptance method is a major challenge, because both components can influence the overall performance of a selection hyper-heuristic. In this study, we describe a novel iterated multi-stage hyper-heuristic approach which cycles through two interacting hyper-heuristics and operates based on the principle that not all low level heuristics for a problem domain would be useful at any point of the search process. The empirical results on a hyper-heuristic benchmark indicate the success of the proposed selection hyper-heuristic across six problem domains beating the state-of-the-art approach. |
| first_indexed | 2025-11-14T19:08:16Z |
| format | Article |
| id | nottingham-30192 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:08:16Z |
| publishDate | 2016 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-301922020-05-04T17:38:55Z https://eprints.nottingham.ac.uk/30192/ An iterated multi-stage selection hyper-heuristic Kheiri, Ahmed Özcan, Ender There is a growing interest towards the design of reusable general purpose search methods that are applicable to different problems instead of tailored solutions to a single particular problem. Hyper-heuristics have emerged as such high level methods that explore the space formed by a set of heuristics (move operators) or heuristic components for solving computationally hard problems. A selection hyper-heuristic mixes and controls a predefined set of low level heuristics with the goal of improving an initially generated solution by choosing and applying an appropriate heuristic to a solution in hand and deciding whether to accept or reject the new solution at each step under an iterative framework. Designing an adaptive control mechanism for the heuristic selection and combining it with a suitable acceptance method is a major challenge, because both components can influence the overall performance of a selection hyper-heuristic. In this study, we describe a novel iterated multi-stage hyper-heuristic approach which cycles through two interacting hyper-heuristics and operates based on the principle that not all low level heuristics for a problem domain would be useful at any point of the search process. The empirical results on a hyper-heuristic benchmark indicate the success of the proposed selection hyper-heuristic across six problem domains beating the state-of-the-art approach. Elsevier 2016-04-01 Article PeerReviewed Kheiri, Ahmed and Özcan, Ender (2016) An iterated multi-stage selection hyper-heuristic. European Journal of Operational Research, 250 (1). pp. 77-90. ISSN 0377-2217 Heuristics Combinatorial Optimisation Hyper-heuristic Meta-heuristic Hybrid Approach http://www.sciencedirect.com/science/article/pii/S0377221715008255 doi:10.1016/j.ejor.2015.09.003 doi:10.1016/j.ejor.2015.09.003 |
| spellingShingle | Heuristics Combinatorial Optimisation Hyper-heuristic Meta-heuristic Hybrid Approach Kheiri, Ahmed Özcan, Ender An iterated multi-stage selection hyper-heuristic |
| title | An iterated multi-stage selection hyper-heuristic |
| title_full | An iterated multi-stage selection hyper-heuristic |
| title_fullStr | An iterated multi-stage selection hyper-heuristic |
| title_full_unstemmed | An iterated multi-stage selection hyper-heuristic |
| title_short | An iterated multi-stage selection hyper-heuristic |
| title_sort | iterated multi-stage selection hyper-heuristic |
| topic | Heuristics Combinatorial Optimisation Hyper-heuristic Meta-heuristic Hybrid Approach |
| url | https://eprints.nottingham.ac.uk/30192/ https://eprints.nottingham.ac.uk/30192/ https://eprints.nottingham.ac.uk/30192/ |