Book Chapter

A Re-characterization of Hyper-Heuristics

Details

Citation

Swan J, De Causmaecker P, Martin S & Ozcan E (2018) A Re-characterization of Hyper-Heuristics. In: Amodeo L, Talbi EG & Yalaoui F (eds.) Recent Developments in Metaheuristics. Operations Research/Computer Science Interfaces, 62. Cham, Switzerland: Springer Publishing Company, pp. 75-89. https://doi.org/10.1007/978-3-319-58253-5_5

Abstract
Hyper-heuristics are an optimization methodology which 'search the space of heuristics' rather than directly searching the space of the underlying candidate-solution representation. Hyper-heuristic search has traditionally been divided into two layers: a lower problem-domain layer (where domain-specific heuristics are applied) and an upper hyper-heuristic layer, where heuristics are selected or generated. The interface between the two layers is commonly termed the "domain barrier". Historically this interface has been defined to be highly restrictive, in the belief that this is required for generality. We argue that this prevailing conception of domain barrier is so limiting as to defeat the original motivation for hyper-heuristics. We show how it is possible to make use of domain knowledge without loss of generality and describe generalized hyper-heuristics which can incorporate arbitrary domain knowledge.

Keywords
Hyper-heuristics; Metaheuristics; Optimization; Machine learning; Constraint programming

StatusPublished
Title of seriesOperations Research/Computer Science Interfaces
Number in series62
Publication date31/12/2018
Publication date online19/09/2017
PublisherSpringer Publishing Company
Place of publicationCham, Switzerland
ISSN of series1387-666X
ISBN978-3-319-58253-5; 978-3-319-58252-8