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dc.contributor.authorde la Barra C.L.
dc.contributor.authorCrawford B.
dc.contributor.authorSoto R.
dc.contributor.authorMonfroy E.
dc.date.accessioned2020-09-02T22:31:02Z
dc.date.available2020-09-02T22:31:02Z
dc.date.issued2013
dc.identifier10.1007/978-3-642-39473-7_129
dc.identifier.citation373, PART I, 650-654
dc.identifier.issn18650929
dc.identifier.urihttps://hdl.handle.net/20.500.12728/6689
dc.descriptionFor many real world problems, modeled as Constraint Satisfaction Problems, there are no known efficient algorithms to solve them. The specialized literature offers a variety of solvers, which have shown satisfactory performance. Nevertheless, despite the efforts of the scientific community in developing new strategies, there is no algorithm that is the best for all possible situations. Then, several approaches have emerged to deal with the Algorithm Selection Problem. Here, we sketch the use a Choice Function for guiding a Constraint Programming solver exploiting search process features to dynamically adapt it in order to more efficiently solve Constraint Satisfaction Problems. To determine the best set of parameters of the choice function, an upper-level metaheuristic is used. The main novelty of our approach is that we reconfigure the search based solely on performance data gathered while solving the current problem. © Springer-Verlag Berlin Heidelberg 2013.
dc.language.isoen
dc.publisherSpringer Verlag
dc.subjectAlgorithm selection problem
dc.subjectAutonomous search
dc.subjectConstraint satisfacion problems
dc.subjectConstraint solving
dc.subjectAlgorithms
dc.subjectConstraint satisfaction problems
dc.subjectConstraint theory
dc.subjectHuman computer interaction
dc.subjectLogic programming
dc.subjectAlgorithm selection
dc.subjectAutonomous searches
dc.subjectConstraint programming
dc.subjectConstraint satisfacion problems
dc.subjectConstraint Solving
dc.subjectMultilevel approach
dc.subjectReal-world problem
dc.subjectScientific community
dc.subjectProblem solving
dc.titleAdaptive and multilevel approach for constraint solving
dc.typeConference Paper


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