A tensor-based selection hyper-heuristic for cross-domain heuristic search

Asta, Shahriar and Özcan, Ender (2015) A tensor-based selection hyper-heuristic for cross-domain heuristic search. Information Sciences, 299 . pp. 412-432. ISSN 0020-0255

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Hyper-heuristics have emerged as automated high level search methodologies that manage a set of low level heuristics for solving computationally hard problems. A generic selection hyper-heuristic combines heuristic selection and move acceptance methods under an iterative single point-based search framework. At each step, the solution in hand is modified after applying a selected heuristic and a decision is made whether the new solution is accepted or not. In this study, we represent the trail of a hyper-heuristic as a third order tensor. Factorization of such a tensor reveals the latent relationships between the low level heuristics and the hyper-heuristic itself. The proposed learning approach partitions the set of low level heuristics into two equal subsets where heuristics in each subset are associated with a separate move acceptance method. Then a multi-stage hyper-heuristic is formed and while solving a given problem instance, heuristics are allowed to operate only in conjunction with the associated acceptance method at each stage. To the best of our knowledge, this is the first time tensor analysis of the space of heuristics is used as a data science approach to improve the performance of a hyper-heuristic in the prescribed manner. The empirical results across six different problem domains from a benchmark indeed indicate the success of the proposed approach.

Item Type: Article
Additional Information: NOTICE: this is the author’s version of a work that was accepted for publication in Information Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences, 299 (2015), 412-432, doi: 10.1016/j.ins.2014.12.020
Keywords: Hyper-heuristic; Data science; Machine learning; Move acceptance; Tensor analysis; Algorithm selection
Schools/Departments: University of Nottingham UK Campus > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1016/j.ins.2014.12.020
Depositing User: Asta, Shahriar
Date Deposited: 27 Jan 2015 09:10
Last Modified: 23 Sep 2016 05:35
URI: http://eprints.nottingham.ac.uk/id/eprint/28208

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