New genetic operators for the evolutionary algorithm for clustering
Tipo
Artigo de evento
Data de publicação
2013
Periódico
Proceedings - 1st BRICS Countries Congress on Computational Intelligence, BRICS-CCI 2013
Citações (Scopus)
1
Autores
Ferrari D.G.
De Castro L.N.
De Castro L.N.
Orientador
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Membros da banca
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Resumo
Finding 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.
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Assuntos Scopus
Clustering problems , Clustering solutions , Evolutionary algorithm for clustering , Fast evolutionary algorithms , Genetic operators , High-quality solutions , Quality of solution , Solution quality