Discrete optimisation based on the combined use of reinforcement and constraint satisfaction schemes

Likas, A; Kontoravdis, D; Stafylopatis, A

dc.contributor.author	Likas, A	en
dc.contributor.author	Kontoravdis, D	en
dc.contributor.author	Stafylopatis, A	en
dc.date.accessioned	2014-03-01T01:10:56Z
dc.date.available	2014-03-01T01:10:56Z
dc.date.issued	1995	en
dc.identifier.issn	0941-0643	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/11501
dc.subject	Constraint satisfaction	en
dc.subject	Discrete optimisation	en
dc.subject	Graph partitioning	en
dc.subject	Higher-order Hopfield	en
dc.subject	Reinforcement learning	en
dc.subject	Set partitioning	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.other	NETWORKS	en
dc.title	Discrete optimisation based on the combined use of reinforcement and constraint satisfaction schemes	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/BF01421961	en
heal.identifier.secondary	http://dx.doi.org/10.1007/BF01421961	en
heal.language	English	en
heal.publicationDate	1995	en
heal.abstract	A new approach is presented for finding near-optimal solutions to discrete optimisation problems that is based on the cooperation of two modules: an optimisation module and a constraint satisfaction module. The optimisation module must be able to search the problem state space through an iterative process of sampling and evaluating the generated samples. To evaluate a generated point, first a constraint satisfaction module is employed to map that point to another one satisfying the problem constraints, and then the cost of the new point is used as the evaluation of the original one. The scheme that we have adopted for testing the effectiveness of the method uses a reinforcement learning algorithm in the optimisation module and a general deterministic constraint satisfaction algorithm in the constraint satisfaction module. Experiments using this scheme for the solution of two optimisation problems indicate that the proposed approach is very effective in providing feasible solutions of acceptable quality. © 1995 Springer-Verlag London Limited.	en
heal.publisher	Springer-Verlag	en
heal.journalName	Neural Computing & Applications	en
dc.identifier.doi	10.1007/BF01421961	en
dc.identifier.isi	ISI:A1995RL72300005	en
dc.identifier.volume	3	en
dc.identifier.issue	2	en
dc.identifier.spage	101	en
dc.identifier.epage	112	en