Ensemble move acceptance in selection hyper-heuristics

Kheiri, Ahmed; M?s?r, Mustafa; �zcan, Ender

Abstract

Selection hyper-heuristics are high level search methodologies which control a set of low level heuristics while solving a given problem. Move acceptance is a crucial component of selection hyper-heuristics, deciding whether to accept or reject a new solution at each step during the search process. This study investigates group decision making strategies as ensemble methods exploiting the strengths of multiple move acceptance methods for improved performance. The empirical results indicate the success of the proposed methods across six combinatorial optimisation problems from a benchmark as well as an examination timetabling problem.

Citation

Kheiri, A., Mısır, M., & Özcan, E. (2016, October). Ensemble move acceptance in selection hyper-heuristics. Presented at ISCIS: International Symposium on Computer and Information Sciences, Kraków, Poland

• Presentation Conference Type: Edited Proceedings
• Conference Name: ISCIS: International Symposium on Computer and Information Sciences
• Start Date: Oct 27, 2016
• End Date: Oct 28, 2016
• Acceptance Date: Jul 13, 2016
• Online Publication Date: Sep 23, 2016
• Publication Date: Sep 24, 2016
• Deposit Date: Oct 4, 2016
• Publicly Available Date: Oct 4, 2016
• Electronic ISSN: 1865-0929
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Issue: 659
• Pages: 21–29
• Series Title: Communications in computer and information science
• Series Number: 659
• Series ISSN: 1865-0937
• Book Title: Computer and information sciences: 31st International Symposium, ISCIS 2016, Kraków, Poland, October 27–28, 2016, proceedings
• ISBN: 978-3-319-47216-4
• DOI: https://doi.org/10.1007/978-3-319-47217-1_3
• Keywords: Metaheuristic; Optimisation; Parameter control; Timetabling; Group decision making
• Public URL: https://nottingham-repository.worktribe.com/output/809544
• Publisher URL: http://link.springer.com/chapter/10.1007%2F978-3-319-47217-1_3
• Contract Date: Oct 4, 2016