Problem Understanding through Landscape Theory
| dc.centro | E.T.S.I. Informática | es_ES |
| dc.contributor.author | Chicano-García, José-Francisco | |
| dc.contributor.author | Luque-Polo, Gabriel Jesús | |
| dc.contributor.author | Alba-Torres, Enrique | |
| dc.date.accessioned | 2013-07-15T11:12:45Z | |
| dc.date.available | 2013-07-15T11:12:45Z | |
| dc.date.issued | 2013 | |
| dc.departamento | Lenguajes y Ciencias de la Computación | |
| dc.description.abstract | In order to understand the structure of a problem we need to measure some features of the problem. Some examples of measures suggested in the past are autocorrelation and fitness-distance correlation. Landscape theory, developed in the last years in the field of combinatorial optimization, provides mathematical expressions to efficiently compute statistics on optimization problems. In this paper we discuss how can we use optimización combinatoria in the context of problem understanding and present two software tools that can be used to efficiently compute the mentioned measures. | es_ES |
| dc.description.sponsorship | Ministerio de Economía y Competitividad (TIN2011-28194) | es_ES |
| dc.identifier.citation | Workshop of Problem Understandin and Real-World Optimization, in GECCO companion: 1055-1062 | es_ES |
| dc.identifier.uri | http://hdl.handle.net/10630/5618 | |
| dc.language.iso | eng | es_ES |
| dc.publisher | ACM | es_ES |
| dc.rights.accessRights | open access | |
| dc.subject | Optimización combinatoria | es_ES |
| dc.subject.other | fitness landscapes | es_ES |
| dc.subject.other | elementary landscapes | es_ES |
| dc.subject.other | problem understanding | es_ES |
| dc.subject.other | quadratic assignment problem | es_ES |
| dc.subject.other | unconstrained quadratic optimization | es_ES |
| dc.title | Problem Understanding through Landscape Theory | es_ES |
| dc.type | journal article | es_ES |
| dspace.entity.type | Publication | |
| relation.isAuthorOfPublication | 6f65e289-6502-4756-871c-dbe0ca9be545 | |
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| relation.isAuthorOfPublication | e8596ab5-92f0-420d-a394-17d128c965da | |
| relation.isAuthorOfPublication.latestForDiscovery | 6f65e289-6502-4756-871c-dbe0ca9be545 |
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