New genetic operators for the evolutionary algorithm for clustering

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dc.contributor.authorFerrari D.G.
dc.contributor.authorDe Castro L.N.
dc.date.accessioned2024-03-13T01:05:31Z
dc.date.available2024-03-13T01:05:31Z
dc.date.issued2013
dc.description.abstractFinding a good clustering solution for an unknown problem is a challenging task. Evolutionary algorithms have proved to be reliable methods to search for high quality solutions to complex problems. The present paper proposes a new set of genetic operators for the Fast Evolutionary Algorithm for Clustering (Fast-EAC) to improve the solution quality and computational efficiency. The new algorithm, called EAC-II, is compared with its original version in terms of quality of solutions and efficiency over several problems from the literature. © 2013 IEEE.
dc.description.firstpage55
dc.description.lastpage59
dc.identifier.doi10.1109/BRICS-CCI-CBIC.2013.20
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/36663
dc.relation.ispartofProceedings - 1st BRICS Countries Congress on Computational Intelligence, BRICS-CCI 2013
dc.rightsAcesso Restrito
dc.subject.otherlanguageClustering Problems
dc.subject.otherlanguageComputational Efficiency
dc.subject.otherlanguageEvolutionary Algorithm
dc.subject.otherlanguageGenetic Operators
dc.titleNew genetic operators for the evolutionary algorithm for clustering
dc.typeArtigo de evento
local.scopus.citations1
local.scopus.eid2-s2.0-84905406553
local.scopus.subjectClustering problems
local.scopus.subjectClustering solutions
local.scopus.subjectEvolutionary algorithm for clustering
local.scopus.subjectFast evolutionary algorithms
local.scopus.subjectGenetic operators
local.scopus.subjectHigh-quality solutions
local.scopus.subjectQuality of solution
local.scopus.subjectSolution quality
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84905406553&origin=inward
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