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dc.date.accessioned 2012-10-24T18:42:32Z
dc.date.available 2012-10-24T18:42:32Z
dc.date.issued 2005-10
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/22904
dc.description.abstract Interest of dynamic multimodal functions risen over the last year since many real problems have this feature. On these problems, the goal is no longer to find the global optimal, but to track their progression through the space as closely as possible. This paper presents three evolutionary algorithms for dynamic fitness landscapes. In order to mantain diversity in the population they use two clustering techniques and a macromutation operator. Besides, this paper compares two crossover operators: arithmetic and multiparents two points, respectively. Effectiveness and limitations of each algorithm are discuss and analyzed en
dc.language en es
dc.subject Algorithms es
dc.subject dynamic multimodal functions en
dc.subject Clustering es
dc.subject macromutation en
dc.title Evolutionaty algorithms with clustering for dynamic fitness landscapes es
dc.type Objeto de conferencia es
sedici.creator.person Esquivel, Susana Cecilia es
sedici.creator.person Aragón, Victoria S. es
sedici.description.note Eje: VI Workshop de 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 2005-10
sedici.relation.event XI 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) Except where otherwise noted, this item's license is described as Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)