Abstract
This paper presents a novel genetic clustering algorithm combining a genetic algorithm (GA) with the classical hard c-means clustering algorithm (HCMCA). It processes partition matrices rather than sets of center points and thus provides a new implementation scheme for the genetic operator - recombination. For comparison of performance with other existing clustering algorithms, a gray-level image quantization problem is considered. Experimental results show that the proposed algorithm converges more quickly to the global optimum and thus provides a better way out of the dilemma in which the traditional clustering algorithms are easily trapped in local optima and the genetic approach is time consuming.
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Meng, L., Wu, Q.H., Yong, Z.Z. (2000). A Faster Genetic Clustering Algorithm. In: Cagnoni, S. (eds) Real-World Applications of Evolutionary Computing. EvoWorkshops 2000. Lecture Notes in Computer Science, vol 1803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45561-2_3
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DOI: https://doi.org/10.1007/3-540-45561-2_3
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