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A classification of hyper-heuristic approaches: revisited

Burke, Edmund K.; Hyde, Matthew R.; Kendall, Graham; Ochoa, Gabriela; Özcan, Ender; Woodward, John R.


Edmund K. Burke

Matthew R. Hyde

Graham Kendall

Gabriela Ochoa

Ender Özcan

John R. Woodward


Hyper-heuristics comprise a set of approaches that aim to automate the development of computational search methodologies, initially to address operational research problems but more recently venturing into new domains such as bioinformatics, strategies for games, and software engineering. This chapter overviews previous categorisations of hyper-heuristics and provides a unified classification and definition. We distinguish between two main hyper-heuristic categories: heuristic selection and heuristic generation. Some representative examples of each category are discussed in detail, and recent research trends are highlighted. Our goals are to clarify the main features of existing techniques and to suggest new directions for hyper-heuristic research.

Publication Date Sep 21, 2018
Electronic ISSN 2214-7934
Publisher Springer Publishing Company
Pages 453-477
Series Title International Series in Operations Research & Management Science
Series Number 272
Series ISSN 2214-7934
Book Title Handbook of metaheuristics
Chapter Number 14
ISBN 9783319910857; 9783319910864
APA6 Citation Burke, E. K., Hyde, M. R., Kendall, G., Ochoa, G., Özcan, E., & Woodward, J. R. (2018). A classification of hyper-heuristic approaches: revisited. In Handbook of metaheuristics (453-477). Cham: Springer Publishing Company.
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Additional Information Burke E.K., Hyde M.R., Kendall G., Ochoa G., Özcan E., Woodward J.R. (2019) A Classification of Hyper-Heuristic Approaches: Revisited. In: Gendreau M., Potvin JY. (eds) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol 272. Springer, Cham


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