A hyper-heuristic based on random gradient, greedy and dominance

Özcan, Ender and Kheiri, Ahmed ORCID: https://orcid.org/0000-0002-6716-2130 2012. A hyper-heuristic based on random gradient, greedy and dominance. Gelenbe, Erol, Lent, Ricardo and Sakellari, Georgia, eds. Computer and Information Sciences II: 26th International Symposium on Computer and Information Sciences, Springer, pp. 557-563. (10.1007/978-1-4471-2155-8_71)

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Abstract

Hyper-heuristics have emerged as effective general methodologies that are motivated by the goal of building or selecting heuristics automatically to solve a range of hard computational search problems with less development cost. HyFlex is a publicly available hyper-heuristic tool for rapid development and research which currently provides an interface to four problem domains along with relevant low level heuristics. A multistage hyper-heuristic based on random gradient and greedy with dominance heuristic selection methods is introduced in this study. This hyper-heuristic is implemented as an extension to HyFlex. The empirical results show that our approach performs better than some previously proposed hyper-heuristics over the given problem domains.

Item Type:	Book Section
Date Type:	Publication
Status:	Published
Schools:	Schools > Mathematics
Subjects:	Q Science > QA Mathematics
Publisher:	Springer
ISBN:	9781447121541
Last Modified:	31 Oct 2022 10:41
URI:	https://orca.cardiff.ac.uk/id/eprint/85720

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