In this paper, an evolutionary programming-based clustering algorithm
is proposed. The algorithm effectively groups a given set of data into
an optimum number of clusters. The proposed method is applicable for
clustering tasks where clusters are crisp and spherical. This algorith
m determines the number of clusters and the cluster centers in such a
way that locally optimal solutions are avoided. The result of the algo
rithm does not depend critically on the choice of the initial cluster
centers. (C) 1997 Published by Elsevier Science B.V.