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

Asta, Shahriar; Özcan, Ender

Authors

Shahriar Asta



Abstract

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 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.

Journal Article Type Article
Publication Date Apr 1, 2015
Journal Information Sciences
Print ISSN 0020-0255
Electronic ISSN 1872-6291
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 299
APA6 Citation Asta, S., & Özcan, E. (2015). A tensor-based selection hyper-heuristic for cross-domain heuristic search. Information Sciences, 299, https://doi.org/10.1016/j.ins.2014.12.020
DOI https://doi.org/10.1016/j.ins.2014.12.020
Keywords Hyper-Heuristic, Data Science, Machine Learning, Move Acceptance, Tensor Analysis, Algorithm Selection
Publisher URL http://www.sciencedirect.com/science/article/pii/S0020025514011591
Copyright Statement Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0





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