Subir material

Suba sus trabajos a SEDICI, para mejorar notoriamente su visibilidad e impacto

 

Mostrar registro sencillo

dc.date.accessioned 2012-07-11T13:47:29Z
dc.date.available 2012-07-11T13:47:29Z
dc.date.issued 2011
dc.identifier.uri http://hdl.handle.net/10915/18571
dc.description.abstract There are many different forms of recombination operators available in literature. However, it is difficult to determine a priori which one is the best suited for a given problem. This issue encourages us to propose an adaptive evolutionary algorithm to solve the NK landscape problem, which dynamically selects the recombination operator from an operator pool during the evolution; this removes the need of specifying a single recombinator operator ad-hoc. We compare the performance of our adaptive proposal against traditional evolutionary algorithms in a numerical way. Our experiments show that the simple adaptive mechanism has a good performance among all the evaluated ones on high dimensional landscapes with an additional reduction in pretuning time. en
dc.format.extent p. 1-10 es
dc.language en es
dc.title A self-adaptive recombination method in evolutionary algorithms for solving epistatic problems es
dc.type Objeto de conferencia es
sedici.creator.person Stark, Natalia es
sedici.creator.person Salto, Carolina es
sedici.description.note Presentado en XII Workshop Agentes y Sistemas Inteligentes (WASI) es
sedici.subject.materias Ciencias Informáticas es
sedici.subject.keyword recombination operator; evolutionary algorithm; epistatic problems 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 2011-10
sedici.relation.event XVII Congreso Argentino de Ciencias de la Computación es
sedici.description.peerReview peer-review es
sedici.subject.acmcss98 Algorithms es


Descargar archivos

Este ítem aparece en las siguientes colecciones:

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)