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dc.date.accessioned 2012-11-05T12:26:24Z
dc.date.available 2012-11-05T12:26:24Z
dc.date.issued 2012-10
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/23593
dc.description.abstract Traditionally in Genetic Algorithms, the mutation probability parameter maintains a constant value during the search. However, an important difficulty is to determine a priori which probability value is the best suited for a given problem. Besides, there is a growing demand for up-to-date optimization software, applicable by a non-specialist within an industrial development environment. These issues encourage us to propose an adaptive evolutionary algorithm that includes a mechanism to modify the mutation probability without external control. This process of dynamic adaptation happens while the algorithm is searching for the problem solution. This eliminates a very expensive computational phase related to the pre-tuning of the algorithmic parameters. We compare the performance of our adaptive proposal against traditional genetic algorithms with fixed parameter values in a numerical way. The empirical comparisons, over a range of NK-Landscapes instances, show that a genetic algorithm incorporating a strategy for adapting the mutation probability outperforms the same algorithm using fixed mutation rates. en
dc.language en es
dc.subject Adapting the Mutation Probability en
dc.subject Algorithms es
dc.subject Intelligent agents es
dc.subject Genetic Algorithms en
dc.title A new strategy for adapting the mutation probability in genetic algorithms en
dc.type Objeto de conferencia es
sedici.creator.person Stark, Natalia es
sedici.creator.person Minetti, Gabriela F. es
sedici.creator.person Salto, Carolina es
sedici.description.note Eje: Workshop Agentes y sistemas inteligentes (WASI) 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 2012-10
sedici.relation.event XVIII Congreso Argentino de Ciencias de la Computación es
sedici.description.peerReview peer-review es


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