Abstract
A modified variable string length genetic algorithm, called MVGA, is proposed for text clustering in this paper. 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 special indices to indicate the location of each gene. More effective version of evolutional steps can 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 text clustering.
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Song, W., Park, S.C. (2006). Genetic Algorithm-Based Text Clustering Technique. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_103
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DOI: https://doi.org/10.1007/11881070_103
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45901-9
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