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Mostrar el registro sencillo del ítem 2012-11-01T12:33:05Z 2012-11-01T12:33:05Z 2001-10
dc.description.abstract In previous works the ability of CPS-MCPC (an evolutionary, co-operative, population search method with multiple crossovers per couple) to build well delineated Pareto fronts in diverse multiobjective optimization problems (MOOPs) was demonstrated. To test the potential of the novel method when dealing with the Job Shop Scheduling Problem (JSSP), regular and non-regular objectives functions were chosen. They were the makespan and the mean absolute deviation (of job completion times from a common due date, an earliness/tardiness related problem). Diverse representations such as priority list representation (PLR), job-based representation (JBR) and operation-based representation (OBR) among others were implemented and tested. The latter showed to be the best one. As a good parameter setting can enhance the behaviour of an evolutionary algorithm distinct parameters combinations were implemented and their influence studied. Multiple crossovers on multiple parents (MCMP), a powerful multirecombination method showed some enhancement in single objective optimization when compared with MCPC. This paper shows the influence of different recombination schemes when building the Pareto front under OBR and using the best parameter settings determined in previous works on a set of demonstrative Lawrence´s instances. Details of implementation and results are discussed. en
dc.format.extent 12 p. es
dc.language en es
dc.title Upgrading evolutionary algorithms through multiplicity for multiobjective optimization in job shop scheduling problems en
dc.type Objeto de conferencia es
sedici.creator.person Esquivel, Susana Cecilia es
sedici.creator.person Ferrero, Sergio W. 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 Computation en
sedici.subject.keyword Job shop scheduling en
sedici.subject.keyword multiobjective optimization en
sedici.subject.keyword multirecombination 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 Scheduling es
sedici.subject.acmcss98 Optimization 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)