A comparison of crossover control mechanisms within single-point selection hyper-heuristics using HyFlex

Drake, John H.; �zcan, Ender; Burke, Edmund K.

Abstract

Hyper-heuristics are search methodologies which operate at a higher level of abstraction than traditional search and optimisation techniques. Rather than operating on a search space of solutions directly, a hyper-heuristic searches a space of low-level heuristics or heuristic components. An iterative selection hyper-heuristic operates on a single solution, selecting and applying a low-level heuristic at each step before deciding whether to accept the resulting solution. Crossover low-level heuristics are often included in modern selection hyper-heuristic frameworks, however as they require multiple solutions to operate, a strategy is required to manage potential solutions to use as input. In this paper we investigate the use of crossover control schemes within two existing selection hyper-heuristics and observe the difference in performance when the method for managing potential solutions for crossover is modified. Firstly, we use the crossover control scheme of AdapHH, the winner of an international competition in heuristic search, in a Modified Choice Function - All Moves selection hyper-heuristic. Secondly, we replace the crossover control scheme within AdapHH with another method taken from the literature. We observe that the performance of selection hyper-heuristics using crossover low level heuristics is not independent of the choice of strategy for managing input solutions to these operators.

Citation

Drake, J. H., Özcan, E., & Burke, E. K. (2015). A comparison of crossover control mechanisms within single-point selection hyper-heuristics using HyFlex. In 2015 IEEE Congress on Evolutionary Computation (CEC). https://doi.org/10.1109/CEC.2015.7257316

• Conference Name: 2015 IEEE Congress on Evolutionary Computation (CEC2015)
• End Date: May 28, 2015
• Acceptance Date: May 25, 2015
• Publication Date: May 25, 2015
• Deposit Date: Jun 13, 2016
• Publicly Available Date: Jun 13, 2016
• Peer Reviewed: Peer Reviewed
• Book Title: 2015 IEEE Congress on Evolutionary Computation (CEC)
• DOI: https://doi.org/10.1109/CEC.2015.7257316
• Public URL: https://nottingham-repository.worktribe.com/output/751389
• Publisher URL: http://dx.doi.org/10.1109/CEC.2015.7257316
• Additional Information: Published in: 2015 IEEE Congress on Evolutionary Computation (CEC) proceedings, 25-28 May 2015, Sendai, Japan. IEEE, 2015, ISBN 9781479974924, pp. 3397-3403.; © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.; ; doi:10.1109/CEC.2015.7257316