Abstract—In this paper, an algorithm for the clustering problem using a combination of the genetic algorithm with the popular K-Means greedy algorithm is proposed. The main idea of this algorithm is to use the genetic search approach to generate new clusters using the famous two-point crossover and then apply the K-Means technique to further improve the quality of the formed clusters in order to speed up the search process.
Experimental results demonstrate that the proposed genetic algorithm combined with K-Means converges faster while producing the same quality of the clustering compared to the standard genetic algorithm.
Index Terms—Clustering problem, genetic algorithm, K-means.
The authors are with Buskerud and Vestfold University College, Faculty of Technology, Norway (e-mail: noureddine.bouhmala@hbv.no).
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Cite: N. Bouhmala, A. Viken, and J. B. Lønnum, "Enhanced Genetic Algorithm with K-Means for the Clustering Problem," International Journal of Modeling and Optimization vol. 5, no. 2, pp. 150-154, 2015.
