New Genetic Operators for the Evolutionary Algorithm for Clustering

Author

Ferrari, Daniel G. ; de Castro, Leandro N.

Author_Institution

Natural Comput. Lab. (LCoN), Mackenzie Univ., Sao Paulo, Brazil

fYear

2013

fDate

8-11 Sept. 2013

Firstpage

55

Lastpage

59

Abstract

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.

Keywords

genetic algorithms; pattern clustering; EAC-II; clustering solution; complex problems; computational efficiency; evolutionary algorithms; fast evolutionary algorithm for clustering; fast-EAC; genetic operators; high quality solutions; Algorithm design and analysis; Clustering algorithms; Computational efficiency; Evolutionary computation; Genetics; Sociology; Statistics; Clustering Problems; Computational Efficiency; Evolutionary Algorithm; Genetic Operators;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and 11th Brazilian Congress on Computational Intelligence (BRICS-CCI & CBIC), 2013 BRICS Congress on

Conference_Location

Ipojuca

Type

conf

DOI

10.1109/BRICS-CCI-CBIC.2013.20

Filename

6855829