Genetic algorithms for large-scale clustering problems

We consider the clustering problem in the case where the distances between elements are metric and both the number of attributes and the number of clusters are large. In this environment the genetic algorithm approach gives high quality clusterings, but at the expense of long running time. Three new and efficient crossover techniques are introduced here. The hybridization of the genetic algorithm and k-means algorithm is discussed.