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dc.date.accessioned 2008-05-23T19:20:00Z
dc.date.available 2008-05-23T03:00:00Z
dc.date.issued 2005-12
dc.identifier.uri http://hdl.handle.net/10915/9593
dc.description.abstract Interest on dynamic multimodal functions risen over the last years 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 maintain 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 anda analyzed. en
dc.format.extent p. 196-203 es
dc.language en es
dc.title Evolutionary algorithms with clustering for dynamic fitness landscapes en
dc.type Articulo es
sedici.identifier.uri http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Dec05-6.pdf es
sedici.identifier.issn 1666-6038 es
sedici.creator.person Aragón, Victoria S. es
sedici.creator.person Esquivel, Susana Cecilia es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Facultad de Informática es
sedici.subtype Articulo es
sedici.rights.license Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
sedici.rights.uri http://creativecommons.org/licenses/by-nc/3.0/
sedici.description.peerReview peer-review es
sedici2003.identifier ARG-UNLP-ART-0000000624 es
sedici.relation.journalTitle Journal of Computer Science & Technology es
sedici.relation.journalVolumeAndIssue vol. 5, no. 4 es
sedici.subject.acmcss98 Algorithms es
sedici.subject.acmcss98 Information Systems es

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Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0) Except where otherwise noted, this item's license is described as Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)