A framework for constraint based local search using ESSENCE
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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.
Akgun , O , Attieh , S W A , Gent , I P , Jefferson , C A , Miguel , I J , Nightingale , P W , Salamon , A Z , Spracklen , P & Wetter , J P 2018 , A framework for constraint based local search using ESSENCE . in J Lang (ed.) , Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence . International Joint Conferences on Artificial Intelligence , pp. 1242-1248 , 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence , Stockholm , Sweden , 13/07/18 . https://doi.org/10.24963/ijcai.2018/173conference
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
© 2018, IJCAI. This work has been made available online in accordance with the publisher’s policies. This is the final published version of the work, which was originally published at https://doi.org/10.24963/ijcai.2018/173
DescriptionFunding: UK Engineering & Physical Sciences Research Council (EPSRC) grants EP/P015638/1and EP/P026842/1.
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