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Adaptive hybridization strategies

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Autor dc.contributor.author Monfroy E.
Autor dc.contributor.author Castro C.
Autor dc.contributor.author Crawford B.
Autor dc.contributor.author Figueroa C.
Fecha Ingreso dc.date.accessioned 2014-04-05T00:14:14Z
Fecha Disponible dc.date.available 2014-04-05T00:14:14Z
Fecha en Repositorio dc.date.issued 2014-04-04
dc.identifier 10.1145/1982185.1982387
dc.description.abstract During the last decades, significant improvements have been achieved for solving complex combinatorial optimization problems issued from real world applications. To tackle large scale instances and intricate problem structures, sophisticated solving techniques have been developed, combined, and hybridized to provide efficient solvers. Combinatorial problems are often modeled as Constraint Satisfaction Problems or constraint optimization problems, which consist of a set of variables, a set of possible values for these variables and a set of constraints to be satisfied. However, solvers or hybridization of solvers become more and more complex: the user must select various solving and hybridization strategies and tune numerous parameters. Moreover, it is well-known that an a priori decision concerning strategies and parameters is very difficult since strategies and parameters effects are rather unpredictable and may change during solving. © 2011 Authors. en_US
dc.source Proceedings of the ACM Symposium on Applied Computing
Link Descarga dc.source.uri http://www.scopus.com/inward/record.url?eid=2-s2.0-79959301229&partnerID=40&md5=d6d9bd1508425d7da95774cc8a516f5b
Title dc.title Adaptive hybridization strategies en_US
Tipo dc.type Conference Paper
dc.description.keywords Combinatorial optimization problems; Combinatorial problem; Constraint optimization problems; Constraint Satisfaction Problems; Problem structure; Real-world application; Optimization; Combinatorial optimization en_US


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