Clustering using a coarse-grained parallel genetic algorithm: a preliminary study

Abstract

Genetic algorithms (GA) are useful in solving complex optimization problems. By posing pattern clustering as an optimization problem, GAs can be used to obtain optimal minimum squared error partitions. In order to improve the total execution time, a distributed algorithm has been developed using the divide and conquer approach. Using a standard communication library called PVM, the distributed algorithm has been implemented on a workstation cluster: the GA approach gives better quality clusters for many data sets compared to a standard K-means clustering algorithm. We have achieved a near linear speedup for the distributed implementation.