Assessing hyper-heuristic performance

Pillay, Nelishia; Qu, Rong

Abstract

Limited attention has been paid to assessing the generality performance of hyper-heuristics. The performance of hyper-heuristics has been predominately assessed in terms of optimality which is not ideal as the aim of hyper-heuristics is not to be competitive with state of the art approaches but rather to raise the level of generality, i.e. the ability of a technique to produce good results for different problem instances or problems rather than the best results for some instances and poor results for others. Furthermore from existing literature in this area it is evident that different hyper-heuristics aim to achieve different levels of generality and need to be assessed as such. To cater for this the paper firstly presents a new taxonomy of four different levels of generality that can be attained by a hyper-heuristic based on a survey of the literature. The paper then proposes a performance measure to assess the performance of different types of hyper-heuristics at the four levels of generality in terms of generality rather than optimality. Three case studies from the literature are used to demonstrate the application of the generality performance measure. The paper concludes by examining how the generality measure can be combined with measures of other performance criteria, such as optimality, to assess hyper-heuristic performance on more than one criterion.

Citation

Pillay, N., & Qu, R. (2021). Assessing hyper-heuristic performance. Journal of the Operational Research Society, 72(11), 2503-2516. https://doi.org/10.1080/01605682.2020.1796538

• Journal Article Type: Article
• Acceptance Date: Jul 12, 2020
• Online Publication Date: Aug 7, 2020
• Publication Date: 2021
• Deposit Date: Aug 18, 2020
• Publicly Available Date: Aug 8, 2021
• Journal: Journal of the Operational Research Society
• Print ISSN: 0160-5682
• Electronic ISSN: 1476-9360
• Publisher: Taylor and Francis
• Peer Reviewed: Peer Reviewed
• Volume: 72
• Issue: 11
• Pages: 2503-2516
• DOI: https://doi.org/10.1080/01605682.2020.1796538
• Keywords: Marketing; Management Science and Operations Research; Strategy and Management; Management Information Systems
• Public URL: https://nottingham-repository.worktribe.com/output/4838638
• Publisher URL: https://www.tandfonline.com/doi/full/10.1080/01605682.2020.1796538
• Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis inJournal of the Operational Research Society on 7 August 2020, available online: http://www.tandfonline.com/10.1080/01605682.2020.1796538.