Abstract
In this paper we present the use of a genetic algorithm (GA) for the optimization, in clustering tasks, of a new kind of fast-learning neural network. The network uses a combination of supervised and un-supervised learning that makes it suitable for automatic tuning -by means of the GA-of the learning parameters and initial weights in order to obtain the highest recognition score. Simulation results are presented showing as, for relatively simple clustering tasks, the GA finds in a few generations the parameters of the network that lead to a classification accuracy close to 100%.
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© 1993 Springer-Verlag Berlin Heidelberg
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Merelo, J.J., Patón, M., Cañas, A., Prieto, A., Morán, F. (1993). Optimization of a competitive learning neural network by genetic algorithms. In: Mira, J., Cabestany, J., Prieto, A. (eds) New Trends in Neural Computation. IWANN 1993. Lecture Notes in Computer Science, vol 686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56798-4_145
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DOI: https://doi.org/10.1007/3-540-56798-4_145
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