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Erschienen in:
Hidden Markov Model Classifier for the Adaptive Particle Swarm Optimization Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

5. A Re-characterization of Hyper-Heuristics

verfasst von : Jerry Swan, Patrick De Causmaecker, Simon Martin, Ender Özcan

Erschienen in: Recent Developments in Metaheuristics

Verlag: Springer International Publishing

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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.

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Metadaten
Titel
A Re-characterization of Hyper-Heuristics
verfasst von
Jerry Swan
Patrick De Causmaecker
Simon Martin
Ender Özcan
Copyright-Jahr
2018
Buch
Recent Developments in Metaheuristics

Print ISBN: 978-3-319-58252-8

Electronic ISBN: 978-3-319-58253-5

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-58253-5_5

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