A hyper-heuristic with a round robin neighbourhood selection

Kheiri, Ahmed ORCID: https://orcid.org/0000-0002-6716-2130 and Özcan, Ender 2013. A hyper-heuristic with a round robin neighbourhood selection. Presented at: EvoMUSART 2013: European Conference on Evolutionary Computation in Combinatorial Optimization, Vienna, Austria, 3-5 April 2013. Published in: Middendorf, Martin and Blum, Christian eds. Evolutionary Computation in Combinatorial Optimization: 13th European Conference, EvoCOP 2013, Vienna, Austria, April 3-5, 2013. Proceedings. Lecture Notes in Computer Science. , vol.7832 Springer, pp. 1-12. 10.1007/978-3-642-37198-1_1

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Abstract

An iterative selection hyper-heuristic passes a solution through a heuristic selection process to decide on a heuristic to apply from a fixed set of low level heuristics and then a move acceptance process to accept or reject the newly created solution at each step. In this study, we introduce Robinhood hyper-heuristic whose heuristic selection component allocates equal share from the overall execution time for each low level heuristic, while the move acceptance component enables partial restarts when the search process stagnates. The proposed hyper-heuristic is implemented as an extension to a public software used for benchmarking of hyper-heuristics, namely HyFlex. The empirical results indicate that Robinhood hyper-heuristic is a simple, yet powerful and general multistage algorithm performing better than most of the previously proposed selection hyper-heuristics across six different Hyflex problem domains.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Mathematics
Subjects:	Q Science > QA Mathematics
Publisher:	Springer
ISBN:	9783642371974
ISSN:	0302-9743
Last Modified:	31 Oct 2022 10:41
URI:	https://orca.cardiff.ac.uk/id/eprint/85719

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