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Experimental Matching of Instances to Heuristics for Constraint Satisfaction Problems

dc.creatorJosé Carlos Ortiz Bayliss
dc.creatorSantiago Enrique Conant Pablos
dc.creatorHugo Terashima Marín
dc.date2016
dc.date.accessioned2018-10-18T22:08:18Z
dc.date.available2018-10-18T22:08:18Z
dc.identifier.issn16875265
dc.identifier.doi10.1155/2016/7349070
dc.identifier.urihttp://hdl.handle.net/11285/630545
dc.descriptionConstraint satisfaction problems are of special interest for the artificial intelligence and operations research community due to their many applications. Although heuristics involved in solving these problems have largely been studied in the past, little is known about the relation between instances and the respective performance of the heuristics used to solve them. This paper focuses on both the exploration of the instance space to identify relations between instances and good performing heuristics and how to use such relations to improve the search. Firstly, the document describes a methodology to explore the instance space of constraint satisfaction problems and evaluate the corresponding performance of six variable ordering heuristics for such instances in order to find regions on the instance space where some heuristics outperform the others. Analyzing such regions favors the understanding of how these heuristics work and contribute to their improvement. Secondly, we use the information gathered from the first stage to predict the most suitable heuristic to use according to the features of the instance currently being solved. This approach proved to be competitive when compared against the heuristics applied in isolation on both randomly generated and structured instances of constraint satisfaction problems. © 2016 Jorge Humberto Moreno-Scott et al.
dc.languageeng
dc.publisherHindawi Limited
dc.relationhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84959450311&doi=10.1155%2f2016%2f7349070&partnerID=40&md5=cc52a40f1f7f6adb7a9f95ad454c0c11
dc.relationInvestigadores
dc.relationEstudiantes
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.sourceComputational Intelligence and Neuroscience
dc.subjectArtificial intelligence
dc.subjectHeuristic methods
dc.subjectOperations research
dc.subjectProblem solving
dc.subjectVariable ordering heuristics
dc.subjectConstraint satisfaction problems
dc.subjectalgorithm
dc.subjectartificial intelligence
dc.subjectcomputer simulation
dc.subjectheuristics
dc.subjecthuman
dc.subjectnonlinear system
dc.subjectphysiology
dc.subjectsatisfaction
dc.subjecttheoretical model
dc.subjectAlgorithms
dc.subjectArtificial Intelligence
dc.subjectComputer Simulation
dc.subjectHeuristics
dc.subjectHumans
dc.subjectModels, Theoretical
dc.subjectNonlinear Dynamics
dc.subjectPersonal Satisfaction
dc.subject.classification7 INGENIERÍA Y TECNOLOGÍA
dc.titleExperimental Matching of Instances to Heuristics for Constraint Satisfaction Problems
dc.typeArtículo
dc.identifier.volume2016
refterms.dateFOA2018-10-18T22:08:18Z


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