Clustering of Categoric Data in Medicine - Application of Evolutionary Algorithms
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Improved spiking neural networks for EEG classification and epilepsy and seizure detection
Integrated Computer-Aided Engineering
Neural Networks
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We introduce a multiple subpopulation approach for parallel evolutionary algorithms the migration scheme of which follows a neural network learning like dynamic. It is adapted from the approach of collective learning in self-organizing maps with a more and more separation during time. We succesfully apply this approach to clustering real world data in psychotherapy research and VLSI-design. The advantages of the approach are shown which consist in a reduced communication overhead between the subpopulations preserving a non-vanishing information flow and an improved convergence rate resulting in decreasing computational costs.