Cluster analysis using genetic algorithms
Tianzi Jiang, Song De Ma · 2002
In this paper, we propose a novel approach to solve the clustering problem. We consider the problem of clustering m objects into c clusters. The objects are represented by points in an n-dimensional Euclidean space, and the objective is classify these m points into c clusters such that the distance between points within a cluster and its center is minimized. We propose and implement a genetic algorithm-based cost minimization approach to this problem. We compare the performance of our algorithm, with that of the k-means and simulated annealing algorithms. Our algorithm obtained results that are better than the well-known k-means and simulated annealing algorithms.