A comparison between the Pittsburgh and Michigan approaches for the binary PSO algorithm

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dc.contributor.author Cervantes, Alejandro
dc.contributor.author Galván, Inés M.
dc.contributor.author Isasi, Pedro
dc.date.accessioned 2009-04-20T07:40:00Z
dc.date.available 2009-04-20T07:40:00Z
dc.date.issued 2005-09
dc.identifier.bibliographicCitation The 2005 IEEE Congress on Evolutionary Computation. Vol 1, p. 290-297
dc.identifier.isbn 0-7803-9363-5
dc.identifier.uri http://hdl.handle.net/10016/4020
dc.description IEEE Congress on Evolutionary Computation. Edimburgo, 5 september 2005
dc.description.abstract This paper shows the performance of the binary PSO algorithm as a classification system. These systems are classified in two different perspectives: the Pittsburgh and the Michigan approaches. In order to implement the Michigan approach binary PSO algorithm, the standard PSO dynamic equations are modified, introducing a repulsive force to favor particle competition. A dynamic neighborhood, adapted to classification problems, is also defined. Both classifiers are tested using a reference set of problems, where both classifiers achieve better performance than many classification techniques. The Michigan PSO classifier shows clear advantages over the Pittsburgh one both in terms of success rate and speed. The Michigan PSO can also be generalized to the continuous version of the PSO.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher IEEE
dc.rights © IEEE
dc.title A comparison between the Pittsburgh and Michigan approaches for the binary PSO algorithm
dc.type conferenceObject
dc.type bookPart
dc.relation.publisherversion http://dx.doi.org/10.1109/CEC.2005.1554697
dc.subject.eciencia Informática
dc.identifier.doi 10.1109/CEC.2005.1554697
dc.rights.accessRights openAccess
dc.relation.eventdate 2-5 Sept. 2005
dc.relation.eventplace Edinburgh (United Kingdom)
dc.relation.eventtitle 2005 IEEE Congress on Evolutionary Computation (CEC'05)
dc.relation.eventtype proceeding
dc.identifier.publicationfirstpage 290
dc.identifier.publicationlastpage 297
dc.identifier.publicationvolume 1
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