Enhanced Genetic Algorithm with K-Means for the Clustering Problem
Noureddine Bouhmala, Anders Viken, Jonas Blasas Lonnum · International Journal of Modeling and Optimization · 2015
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.