Akgun, Ozgur, Attieh, Saad Wasim A, Gent, Ian Philip et al. (6 more authors) (2018) A framework for constraint based local search using ESSENCE. In: Lang, Jérôme, (ed.) Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence , pp. 1242-1248.
Abstract
Structured Neighbourhood Search (SNS) is a framework for constraint-based local search for problems expressed in the Essence abstract constraint specification language. The local search explores a structured neighbourhood, where each state in the neighbourhood preserves a high level structural feature of the problem. SNS derives highly structured problem-specific neighbourhoods automatically and directly from the features of the ESSENCE specification of the problem. Hence, neighbourhoods can represent important structural features of the problem, such as partitions of sets, even if that structure is obscured in the low-level input format required by a constraint solver. SNS expresses each neighbourhood as a constrained optimisation problem, which is solved with a constraint solver. We have implemented SNS, together with automatic generation of neighbourhoods for high level structures, and report high quality results for several optimisation problems.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | Funding: UK Engineering & Physical Sciences Research Council (EPSRC) grants EP/P015638/1 and EP/P026842/1. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 23 Jan 2019 09:40 |
Last Modified: | 30 Mar 2025 00:15 |
Published Version: | https://doi.org/10.24963/ijcai.2018/173 |
Status: | Published |
Publisher: | International Joint Conferences on Artificial Intelligence |
Identification Number: | 10.24963/ijcai.2018/173 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:141508 |