In search of optimal clusters using genetic algorithms

Genetic Algorithms (GAs) are generally portrayed as search procedures which can optimize functions based on a limited sample of function values. In this paper, GAs have been used in an attempt to optimize a specified objective function related to a clustering problem. Several experiments on synthetic and real life data sets show the utility of the proposed method. K-Means is one of the most popular methods adopted to solve the clustering problem. Analysis of the experimental results shows that the proposed method may improve the final output of K-Means where an improvement is possible.