Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/18261
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Hyper-heuristics: A survey of the state of the art
Authors: Burke, Edmund
Gendreau, Michel
Hyde, Matthew
Kendall, Graham
Ochoa, Gabriela
Ozcan, Ender
Qu, Rong
Contact Email: gabriela.ochoa@stir.ac.uk
Keywords: Hyper-heuristics
evolutionary computation
metaheuristics
machine learning
combinatorial optimisation
scheduling
Issue Date: Jul-2013
Publisher: Palgrave Macmillan
Citation: Burke E, Gendreau M, Hyde M, Kendall G, Ochoa G, Ozcan E & Qu R (2013) Hyper-heuristics: A survey of the state of the art, Journal of the Operational Research Society, 64 (12), pp. 1695-1724.
Abstract: Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was first used in 2000 to describe heuristics to choose heuristics in the context of combinatorial optimisation. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s. The definition of hyper-heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed.
Type: Journal Article
URI: http://hdl.handle.net/1893/18261
DOI Link: http://dx.doi.org/10.1057/jors.2013.71
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
Affiliation: Deputy Principal's Office
University of Montreal
University of East Anglia
University of Nottingham
Computing Science - CSM Dept
University of Nottingham
University of Nottingham

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