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Hong, Libin (2018) Hyper-heuristic approaches to automatically designing heuristics as mutation operators for evolutionary programming on function classes. PhD thesis, University of Nottingham.

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Abstract

A hyper-heuristic is a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Researchers classify hyper-heuristics according to the source of feedback during learning: Online learning hyper-heuristics learn while solving a given instance of a problem; Offline learning hyper-heuristics learn from a set of training instances, a method that can generalise to unseen instances.

Genetic programming (GP) can be considered a specialization of the more widely known genetic algorithms (GAs) where each individual is a computer program. GP automatically generates computer programs to solve specified tasks. It is a method of searching a space of computer programs. GP can be used as a kind of hyper-heuristic to be a learning algorithm when it uses some feedback from the search process. Our research mainly uses genetic programming as offline hyper-heuristic approach to automatically design various heuristics for evolutionary programming.

Item Type:	Thesis (University of Nottingham only) (PhD)
Supervisors:	Cartlidge, John Özcan, Ender Bai, Ruibin
Keywords:	hyper-heuristic; evolutionary programming;
Subjects:	Q Science > QA Mathematics > QA 75 Electronic computers. Computer science
Faculties/Schools:	UNNC Ningbo, China Campus > Faculty of Science and Engineering > School of Computer Science
Item ID:	52348
Depositing User:	HONG, Libin
Date Deposited:	13 Aug 2018 08:45
Last Modified:	07 May 2020 17:47
URI:	https://eprints.nottingham.ac.uk/id/eprint/52348

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