Abstract
Genetic clustering consists in performing the analysis genetic optimization results using a clustering technique to get approximations of central parts of attractor of a multimodal objective. This work presents how outputs of Hierarchical Genetic Strategy can be clustered with EM algorithm. The approach gives an opportunity of theoretical analysis aimed on evaluating of approximation accuracy. In considered case genetic clustering can be easily implemented in parallel.
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Adamska, K. (2004). Genetic Clustering as a Parallel Algorithm for Approximating Basins of Attraction. In: Wyrzykowski, R., Dongarra, J., Paprzycki, M., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2003. Lecture Notes in Computer Science, vol 3019. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24669-5_70
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DOI: https://doi.org/10.1007/978-3-540-24669-5_70
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