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dc.contributor.authorMachuca, Enrique
dc.contributor.authorMandow-Andaluz, Lorenzo 
dc.contributor.authorGaland, Lucie
dc.date.accessioned2013-09-27T06:29:31Z
dc.date.available2013-09-27T06:29:31Z
dc.date.issued2013-09
dc.identifier.citationhttp://link.springer.com/chapter/10.1007/978-3-642-40643-0_1es_ES
dc.identifier.urihttp://hdl.handle.net/10630/5913
dc.description.abstractThis work evaluates two different approaches for multicriteria graph search problems using compromise preferences. This approach focuses search on a single solution that represents a balanced tradeoff between objectives, rather than on the whole set of Pareto optimal solutions. We review the main concepts underlying compromise preferences, and two main approaches proposed for their solution in heuristic graph problems: naive Pareto search (NAMOA ), and a k-shortest-path approach (kA ). The performance of both approaches is evaluated on sets of standard bicriterion road map problems. The experiments reveal that the k-shortest-path approach looses effectiveness in favor of naive Pareto search as graph size increases. The reasons for this behavior are analyzed and discussedes_ES
dc.description.sponsorshipPartially funded by P07-TIC-03018, Cons. Innovación, Ciencia y Empresa (Junta Andalucía), and Univ. Málaga, Campus Excel. Int. Andalucía Teches_ES
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.relation.ispartofseriesLecture Notes in Artificial Intelligence (LNAI);8109
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectInformáticaes_ES
dc.subject.otherMultiobjective A*es_ES
dc.subject.otherk-shortest pathses_ES
dc.subject.otherroad networkses_ES
dc.titleAn evaluation of best compromise search in graphses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroE.T.S.I. Informáticaes_ES


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