Volume 23, Issue 1 (IJIEPM 2012)                   2012, 23(1): 121-128 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

A Hybrid Clustering Method Using Genetic Algorithm with New Variation Operators. Journal title 2012; 23 (1) :121-128
URL: http://ijiepm.iust.ac.ir/article-1-865-en.html
Abstract:   (11621 Views)

  The clustering problem under the criterion of minimum sum of squares is a non-convex and non-linear program, which possesses many locally optimal values, resulting that its solution often being stuck at locally optimal values and therefore cannot converge to global optima solution. In this paper, we introduce several new variation operators for the proposed hybrid genetic algorithm for the clustering problem. The novel mutation operator, called Clustering Regional Mutation, exchanges neighboring centers and a simple one-point crossover. The proposed algorithm identifies proper clustering. The experimental results are given to illustrate the effectiveness of the new genetic algorithm.

Full-Text [PDF 3575 kb]   (5154 Downloads)    

Add your comments about this article : Your username or Email:

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.