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Optimization of Unsupervised Classification by Evolutionary Strategies


A.EL Allaoui, M Merzougui, M. Nasri, M. EL Hitmy, H. Ouariachi


Vol. 10  No. 6  pp. 325-332


The kmeans algorithm is an unsupervised classification algorithm. This algorithm however, suffers from two difficulties which are the initialization phase and the local optimums. We present in this paper some improvements to this algorithm based on the evolutionary strategies in order to get around these two difficulties. We have designed a new evolutionist kmeans algorithm. We have proposed a new mutation operator in order for the algorithm to avoid local solutions and to converge to the global solution for a low computational time. This approach is validated on some simulation examples. The experimental results obtained confirm the rapidity of convergence and the good performances of the proposed algorithm.


Classification, evolutionary strategies, evolutionist kmeans algorithm, mutation operator