Hyper-heuristics: a survey of the state of the art

Edmund K Burke, Michel Gendreau, Matthew Hyde, Graham Kendall, Gabriela Ochoa, Ender Özcan and Rong Qu
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Edmund K Burke: University of Stirling, Scotland, UK
Michel Gendreau: University of Montreal, Montreal, Canada
Matthew Hyde: University of East Anglia, Norwich, UK
Graham Kendall: University of Nottingham, Nottingham, UK
Gabriela Ochoa: University of Stirling, Scotland, UK
Ender Özcan: University of Nottingham, Nottingham, UK
Rong Qu: University of Nottingham, Nottingham, UK

Journal of the Operational Research Society, 2013, vol. 64, issue 12, 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.

Date: 2013
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