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Mostrar registro sencillo 2012-11-01T13:31:23Z 2012-11-01T13:31:23Z 2001-10
dc.description.abstract Improvements in evolutionary algorithms (EAs) consider multirecombination, allowing multiple crossover operations on a pair of parents (MCPC, multiple crossovers per couple) or on a set of multiple parents (MCMP, multiple crossovers on multiple parents). Evolutionary algorithms have been successfully applied to solve scheduling problems. MCMP-STUD and MCMP-SRI are novel MCMP variants, which considers the inclusion of a stud-breeding individual in a pool of random immigrant parents In this paper the proposal is to generate the stud-breeding individual by means of a robust conventional heuristic, the CDS. In a multirecombined EA, setting of parameters n1 (number of crossovers) and n2 (number of parents) remained as an open question. In previous works; they were empirically determined, or a deterministic rule was applied. In this paper self adaptation of parameters n1 and n2 is implemented, the idea is to code the parameters within the chromosome and undergo genetic operations. Hence it is expected that better parameter values be more intensively propagated. The present paper discusses different multi-recombined methods and contrasts their performance when different parameter control methods are applied, to find the minimum makespan for selected instances of the FSSP. en
dc.format.extent 12 p. es
dc.language en es
dc.title Parameter control in multirecombinated evolutionary algorithms for the flow shop scheduling problem en
dc.type Objeto de conferencia es
sedici.creator.person Vilanova, Gabriela es
sedici.creator.person Villagra, Andrea es
sedici.creator.person Pandolfi, Daniel es
sedici.creator.person San Pedro, María Eugenia de es
sedici.creator.person Gallard, Raúl Hector es
sedici.description.note Eje: Sistemas inteligentes es
sedici.subject.materias Ciencias Informáticas es
sedici.subject.keyword Evolutionary algorithms en
sedici.subject.keyword Multiple Crossovers en
sedici.subject.keyword Multiple Parents en
sedici.subject.keyword Parameter Control. en
sedici.description.fulltext true es Red de Universidades con Carreras en Informática (RedUNCI) es
sedici.subtype Objeto de conferencia es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
sedici.rights.uri 2001-10
sedici.relation.event VII Congreso Argentino de Ciencias de la Computación es
sedici.description.peerReview peer-review es
sedici.subject.acmcss98 Algorithms es
sedici.subject.acmcss98 Scheduling es
sedici.subject.acmcss98 ARTIFICIAL INTELLIGENCE es

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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)