dc.creatorde Andrade, Carlos Eduardo
dc.creatorToso, Rodrigo Franco
dc.creatorResende, Mauricio G C
dc.creatorMiyazawa, Flávio Keidi
dc.date2014-Oct
dc.date2015-11-27T13:43:33Z
dc.date2015-11-27T13:43:33Z
dc.date.accessioned2018-03-29T01:22:15Z
dc.date.available2018-03-29T01:22:15Z
dc.identifierEvolutionary Computation. , 2014-Oct.
dc.identifier1530-9304
dc.identifier10.1162/EVCO_a_00138
dc.identifierhttp://www.ncbi.nlm.nih.gov/pubmed/25299242
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/201787
dc.identifier25299242
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1302020
dc.descriptionAbstract In this paper, we address the problem of picking a subset of bids in a general combinatorial auction so as to maximize the overall profit using the first-price model. This winner determination problem assumes that a single bidding round is held to determine both the winners and prices to be paid. We introduce six variants of biased random-key genetic algorithms for this problem. Three of them use a novel initialization technique that makes use of solutions of intermediate linear programming relaxations of an exact mixed integer-linear programming model as initial chromosomes of the population. An experimental evaluation compares the effectiveness of the proposed algorithms with the standard mixed linear integer programming formulation, a specialized exact algorithm, and the best-performing heuristics proposed for this problem. The proposed algorithms are competitive and offer strong results, mainly for large-scale auctions.
dc.description
dc.description
dc.languageeng
dc.relationEvolutionary Computation
dc.relationEvol Comput
dc.rightsfechado
dc.rights
dc.sourcePubMed
dc.subjectCombinatorial Auctions
dc.subjectBiased Random-key Genetic Algorithms
dc.subjectGenetic Algorithms
dc.subjectWinner Determination Problem
dc.titleBiased Random-key Genetic Algorithms For The Winner Determination Problem In Combinatorial Auctions.
dc.typeArtículos de revistas


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