Genetic Algorithm-based Text Clustering Technique: Automatic Evolution of Clusters with High Efficiency

In this paper, we propose a modified variable string length genetic algorithm (MVGA) for text clustering. Our algorithm has been exploited for automatically evolving the optimal number of clusters as well as providing proper data set clustering. The chromosome is encoded by a string of real numbers with special indices to indicate the location of each gene. More effective versions of operators for selection, crossover, and mutation are introduced in MVGA which can also automatically adjust the influence between the diversity of the population and selective pressure during generations. The superiority of the MVGA over conventional variable string length genetic algorithm (VGA) is demonstrated by providing proper Reuter text collection clusters in terms of number of clusters and clustering data sets.