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dc.date.accessioned 2012-11-16T12:29:28Z
dc.date.available 2012-11-16T12:29:28Z
dc.date.issued 1995-10
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/24282
dc.description.abstract Genetic algorithms (GAs) are stochastic adaptive algorithms whose search method is based on simulation of natural genetic inheritance and Darwinian strive for survival. They can be used to find approximate solutions to numerical optimization problems in cases where finding the exact optimum is prohibitively expensive, or where . no algorithm is known. The main operator, which is the driving force of genetic algorithms, IS crossover. It combines the features of two parents and produces two offspring. This paper propases a Multiple Crossover Per Couple (MCPC) approach as an altemate method for crossover operators. en
dc.format.extent 141-150 es
dc.language en es
dc.subject Algorithms es
dc.subject genetic algorithms en
dc.subject genetic operators en
dc.subject crossover en
dc.title MCPC: another approach to crossover in genetic algorithms en
dc.type Objeto de conferencia es
sedici.creator.person Esquivel, Susana Cecilia es
sedici.creator.person Gallard, Raúl Hector es
sedici.creator.person Michalewicz, Zbigniew es
sedici.description.note Eje: Diseño de algoritmos es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place 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 http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
sedici.date.exposure 1995-10
sedici.relation.event I Congreso Argentino de Ciencias de la Computación es
sedici.description.peerReview peer-review 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)