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Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Peer Review Status: Refereed
Author(s): Ochoa, Gabriela
Walker, James
Hyde, Matthew
Curtois, Tim
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Title: Adaptive evolutionary algorithms and extensions to the HyFlex hyper-heuristic framework
Editor(s): Coello, Coello CA
Cutello, V
Deb K, K
Forrest, S
Nicosia, G
Pavone, M
Citation: Ochoa G, Walker J, Hyde M & Curtois T (2012) Adaptive evolutionary algorithms and extensions to the HyFlex hyper-heuristic framework. In: Coello CC, Cutello V, Deb K K, Forrest S, Nicosia G & Pavone M (eds.) Parallel Problem Solving from Nature - PPSN XII: 12th International Conference, Taormina, Italy, September 1-5, 2012, Proceedings, Part II. Lecture Notes in Computer Science, Vol. 7492. 12th International Conference, Taormina, Italy, 01.09.2012-05.09.2012. Amsterdam: Springer, pp. 418-427.
Issue Date: 2012
Date Deposited: 6-Jun-2013
Series/Report no.: Lecture Notes in Computer Science, Vol. 7492
Conference Name: 12th International Conference
Conference Dates: 2012-09-01 - 2012-09-05
Conference Location: Taormina, Italy
Abstract: HyFlex is a recently proposed software framework for implementing hyper-heuristics and domain-independent heuristic optimisation algorithms [13]. Although it was originally designed to implement hyper-heuristics, it provides a population and a set of move operators of different types. This enable the implementation of adaptive versions of other heuristics such as evolutionary algorithms and iterated local search. The contributions of this article are twofold. First, a number of extensions to the HyFlex framework are proposed and implemented that enable the design of more effective adaptive heuristics. Second, it is demonstrated that adaptive evolutionary algorithms can be implemented within the framework, and that the use of crossover and a diversity metric produced improved results, including a new best-known solution, on the studied vehicle routing problem.
Status: VoR - Version of Record
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