Discrete Optimisation Based on the Combined Use of Reinforcement and Constraint Satisfaction Schemes

Kontoravdis, D; Likas, A; Stafylopatis, A

dc.contributor.author	Kontoravdis, D	en
dc.contributor.author	Likas, A	en
dc.contributor.author	Stafylopatis, A	en
dc.date.accessioned	2014-03-01T01:43:07Z
dc.date.available	2014-03-01T01:43:07Z
dc.date.issued	1995	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/24044
dc.subject	Constraint Satisfaction	en
dc.subject	Generic Point	en
dc.subject	Graph Partitioning	en
dc.subject	Optimal Solution	en
dc.subject	Reinforcement Learning	en
dc.subject	Satisfiability	en
dc.subject	Set Partitions	en
dc.subject	State Space	en
dc.subject	Higher Order	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.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	en
heal.journalName	Neural Computing and Applications	en
dc.identifier.doi	10.1007/BF01421961	en