A Classification of Hyper-heuristic Approaches

Edmund K. Burke (), Matthew Hyde (), Graham Kendall (), Gabriela Ochoa (), Ender Özcan () and John R. Woodward ()
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Edmund K. Burke: University of Nottingham
Matthew Hyde: The University of Nottingham
Graham Kendall: The University of Nottingham
Gabriela Ochoa: The University of Nottingham
Ender Özcan: The University of Nottingham
John R. Woodward: The University of Nottingham

Chapter Chapter 15 in Handbook of Metaheuristics, 2010, pp 449-468 from Springer

Abstract: Abstract The current state of the art in hyper-heuristic research comprises a set of approaches that share the common goal of automating the design and adaptation of heuristic methods to solve hard computational search problems. The main goal is to produce more generally applicable search methodologies. In this chapter we present an overview of previous categorisations of hyper-heuristics and provide a unified classification and definition, which capture the work that is being undertaken in this field. We distinguish between two main hyper-heuristic categories: heuristic selection and heuristic generation. Some representative examples of each category are discussed in detail. Our goals are to clarify the mainfeatures of existing techniques and to suggest new directions for hyper-heuristic research.

Keywords: Local Search; Genetic Programming; Tabu Search; Variable Neighbourhood Search; Local Search Heuristic (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1007/978-1-4419-1665-5_15

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