Modified choice function heuristic selection for the multidimensional knapsack problem
Hyper-heuristics are a class of high-level search methods used to solve computationally difficult problems, which operate on a search space of low-level heuristics rather than solutions directly. Previous work has shown that selection hyper-heuristics are able to solve many combinatorial optimisatio...
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| Format: | Book Section |
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Springer
2014
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| Online Access: | https://eprints.nottingham.ac.uk/33941/ |
| _version_ | 1848794739207307264 |
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| author | Drake, John H. Özcan, Ender Burke, Edmund K. |
| author_facet | Drake, John H. Özcan, Ender Burke, Edmund K. |
| author_sort | Drake, John H. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Hyper-heuristics are a class of high-level search methods used to solve computationally difficult problems, which operate on a search space of low-level heuristics rather than solutions directly. Previous work has shown that selection hyper-heuristics are able to solve many combinatorial optimisation problems, including the multidimensional 0-1 knapsack problem (MKP). The traditional framework for iterative selection hyper-heuristics relies on two key components, a heuristic selection method and a move acceptance criterion. Existing work has shown that a hyper-heuristic using Modified Choice Function heuristic selection can be effective at solving problems in multiple problem domains. Late Acceptance Strategy is a hill climbing metaheuristic strategy often used as a move acceptance criteria in selection hyper-heuristics. This work compares a Modified Choice Function - Late Acceptance Strategy hyper-heuristic to an existing selection hyper-heuristic method from the literature which has previously performed well on standard MKP benchmarks. |
| first_indexed | 2025-11-14T19:20:59Z |
| format | Book Section |
| id | nottingham-33941 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:20:59Z |
| publishDate | 2014 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-339412020-05-04T16:55:29Z https://eprints.nottingham.ac.uk/33941/ Modified choice function heuristic selection for the multidimensional knapsack problem Drake, John H. Özcan, Ender Burke, Edmund K. Hyper-heuristics are a class of high-level search methods used to solve computationally difficult problems, which operate on a search space of low-level heuristics rather than solutions directly. Previous work has shown that selection hyper-heuristics are able to solve many combinatorial optimisation problems, including the multidimensional 0-1 knapsack problem (MKP). The traditional framework for iterative selection hyper-heuristics relies on two key components, a heuristic selection method and a move acceptance criterion. Existing work has shown that a hyper-heuristic using Modified Choice Function heuristic selection can be effective at solving problems in multiple problem domains. Late Acceptance Strategy is a hill climbing metaheuristic strategy often used as a move acceptance criteria in selection hyper-heuristics. This work compares a Modified Choice Function - Late Acceptance Strategy hyper-heuristic to an existing selection hyper-heuristic method from the literature which has previously performed well on standard MKP benchmarks. Springer 2014-10-18 Book Section PeerReviewed Drake, John H., Özcan, Ender and Burke, Edmund K. (2014) Modified choice function heuristic selection for the multidimensional knapsack problem. In: Genetic and evolutionary computing. Advances in Intelligent Systems and Computing (329). Springer, pp. 225-234. ISBN 9783319122854 Hyper-heuristics Choice Function Heuristic Selection Multidimensional Knapsack Problem Combinatorial Optimization http://link.springer.com/chapter/10.1007%2F978-3-319-12286-1_23 doi:10.1007/978-3-319-12286-1_23 doi:10.1007/978-3-319-12286-1_23 |
| spellingShingle | Hyper-heuristics Choice Function Heuristic Selection Multidimensional Knapsack Problem Combinatorial Optimization Drake, John H. Özcan, Ender Burke, Edmund K. Modified choice function heuristic selection for the multidimensional knapsack problem |
| title | Modified choice function heuristic selection for the multidimensional knapsack problem |
| title_full | Modified choice function heuristic selection for the multidimensional knapsack problem |
| title_fullStr | Modified choice function heuristic selection for the multidimensional knapsack problem |
| title_full_unstemmed | Modified choice function heuristic selection for the multidimensional knapsack problem |
| title_short | Modified choice function heuristic selection for the multidimensional knapsack problem |
| title_sort | modified choice function heuristic selection for the multidimensional knapsack problem |
| topic | Hyper-heuristics Choice Function Heuristic Selection Multidimensional Knapsack Problem Combinatorial Optimization |
| url | https://eprints.nottingham.ac.uk/33941/ https://eprints.nottingham.ac.uk/33941/ https://eprints.nottingham.ac.uk/33941/ |