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dc.date.accessioned 2004-05-07T18:56:40Z
dc.date.available 2004-05-07T03:00:00Z
dc.date.issued 2000
dc.identifier.uri http://hdl.handle.net/10915/9389
dc.description.abstract Three alternatives within the method of assignment by classes are presented for the calculation of individuals lifetime in genetic algorithms with varying population size. (GAVaPS). In the proposed strategy (assignment by classes) individuals are grouped according to their fitness. The purpose is to use the allowed range of lifetime values in a way which is more suitable to search the optimum than proportional, linear and bilinear strategies. A comparative study of three possibilities of assignment by classes as related to the traditional methods is carried out, and results are shown over five functions. Finally, some conclusions are presented, along with possible future lines of work. en
dc.format.extent 13 p. es
dc.language en es
dc.title Comparative analysis of the method of assignment by classes in GAVaPS en
dc.type Articulo es
sedici.identifier.uri http://journal.info.unlp.edu.ar/journal/journal2/papers/Comparative.pdf es
sedici.identifier.issn 1666-6038 es
sedici.creator.person Lanzarini, Laura Cristina es
sedici.creator.person Sanz, Cecilia Verónica es
sedici.creator.person Naiouf, Marcelo es
sedici.creator.person Romero, Fernando es
sedici.subject.materias Ciencias Informáticas es
sedici.subject.keyword evolutive computation; genetic algorithms; genetic algorithms with varying population size en
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-0000000234 es
sedici.relation.journalTitle Journal of Computer Science & Technology es
sedici.relation.journalVolumeAndIssue vol. 1, no. 2 es
sedici.subject.acmcss98 Algorithms es
sedici.subject.acmcss98 Heuristic methods es

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