Dynamic heuristic set selection for cross-domain selection hyper-heuristics

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dc.contributor.author Hassan, Ahmed
dc.contributor.author Pillay, Nelishia
dc.date.accessioned 2022-07-14T06:47:56Z
dc.date.issued 2021-11
dc.description.abstract Selection hyper-heuristics have proven to be effective in solving various real-world problems. Hyper-heuristics differ from traditional heuristic approaches in that they explore a heuristic space rather than a solution space. These techniques select constructive or perturbative heuristics to construct a solution or improve an existing solution respectively. Previous work has shown that the set of problem-specific heuristics made available to the hyper-heuristic for selection has an impact on the performance of the hyper-heuristic. Hence, there have been initiatives to determine the appropriate set of heuristics that the hyper-heuristic can select from. However, there has not been much research done in this area. Furthermore, previous work has focused on determining a set of heuristics that is used throughout the lifespan of the hyper-heuristic with no change to this set during the application of the hyper-heuristic. This paper investigates dynamic heuristic set selection (DHSS) which applies dominance to select the set of heuristics at different points during the lifespan of a selection hyper-heuristic. The DHSS approach was evaluated on the benchmark set for the CHeSC cross-domain hyper-heuristic challenge. DHSS was found to improve the performance of the best performing hyper-heuristic for this challenge. en_US
dc.description.department Computer Science en_US
dc.description.embargo 2022-11-04
dc.description.librarian hj2022 en_US
dc.description.sponsorship The Multichoice Research Chair in Machine Learning at the University of Pretoria, South Africa and the National Research Foundation of South Africa. en_US
dc.description.uri https://www.springer.com/series/558 en_US
dc.identifier.citation Hassan, A., Pillay, N. (2021). Dynamic Heuristic Set Selection for Cross-Domain Selection Hyper-heuristics. In: Aranha, C., Martín-Vide, C., Vega-Rodríguez, M.A. (eds) Theory and Practice of Natural Computing. TPNC 2021. Lecture Notes in Computer Science, vol. 13082. Springer, Cham. https://doi.org/10.1007/978-3-030-90425-8_3. en_US
dc.identifier.isbn 978-3-030-90425-8 (online)
dc.identifier.isbn 978-3-030-90424-1 (print)
dc.identifier.issn 0302-9743 (print)
dc.identifier.issn 1611-3349 (online)
dc.identifier.other 10.1007/978-3-030-90425-8_3
dc.identifier.uri https://repository.up.ac.za/handle/2263/86158
dc.language.iso en en_US
dc.publisher Springer en_US
dc.rights © 2021 Springer Nature Switzerland AG. The original publication is available at : https://www.springer.com/series/558. en_US
dc.subject Dynamic heuristic set selection (DHSS) en_US
dc.subject Selection perturbative hyper-heuristics en_US
dc.subject Cross-domain hyper-heuristics en_US
dc.title Dynamic heuristic set selection for cross-domain selection hyper-heuristics en_US
dc.type Postprint Article en_US


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