Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/14940
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Peer Review Status: Refereed
Authors: Walker, James
Ochoa, Gabriela
Gendreau, Michel
Burke, Edmund
Contact Email: gabriela.ochoa@stir.ac.uk
Title: Vehicle routing and adaptive iterated local search within the HyFlex hyper-heuristic framework
Editors: Hamadi, Y
Schoenauer, M
Citation: Walker J, Ochoa G, Gendreau M & Burke E (2012) Vehicle routing and adaptive iterated local search within the HyFlex hyper-heuristic framework In: Hamadi Y, Schoenauer M (ed.) Learning and Intelligent Optimization 6th International Conference, LION 6, Paris, France, January 16-20, 2012, Revised Selected Papers, Amsterdam: Springer. 6th International Conference, LION 6,, 16.1.2012 - 20.1.2012, Paris, France, pp. 265-276.
Issue Date: 2012
Series/Report no.: Lecture Notes in Computer Science, Vol. 7219
Conference Name: 6th International Conference, LION 6,
Conference Dates: 2012-01-16T00:00:00Z
Conference Location: Paris, France
Abstract: HyFlex (Hyper-heuristic Flexible framework) [15] is a soft- ware framework enabling the development of domain independent search heuristics (hyper-heuristics), and testing across multiple problem do- mains. This framework was used as a base for the first Cross-domain Heuristic Search Challenge, a research competition that attracted signif- icant international attention. In this paper, we present one of the prob- lems that was used as a hidden domain in the competition, namely, the capacitated vehicle routing problem with time windows. The do- main implements a data structure and objective function for the vehicle routing problem, as well as many state-of- the-art low-level heuristics (search operators) of several types. The domain is tested using two adap- tive variants of a multiple-neighborhood iterated local search algorithm that operate in a domain independent fashion, and therefore can be con- sidered as hyper-heuristics. Our results con¯rm that adding adaptation mechanisms improve the performance of hyper-heuristics. It is our hope that this new and challenging problem domain can be used to promote research within hyper-heuristics, adaptive operator selection, adaptive multi-meme algorithms and autonomous control for search algorithms
Type: Conference Paper
Status: Book Chapter: publisher version
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
URI: http://hdl.handle.net/1893/14940
URL: http://link.springer.com/chapter/10.1007/978-3-642-34413-8_19#
Affiliation: University of Nottingham
Computing Science - CSM Dept
University of Montreal
Computing Science and Mathematics

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