Abstract
This article deals with the use of genetic algorithms to optimize the architecture of a neural network. After a brief recall of our original neural network (named Yprel network), we show that a simulated-annealing-like technique has been advantageously replaced by genetic operators. Indeed, tests on character recognition (NIST handwritten database) have shown that the generalization rate has been improved, the mean network size has been reduced by a factor 3 and the learning speed has been significantly increased. Moreover, a portable adaptive mutation probability has been introduced which enables a parameter-free learning.
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© 1997 Springer-Verlag Berlin Heidelberg
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Ribert, A., Stocker, E., Lecourtier, Y., Ennaji, A. (1997). Optimizing a neural network architecture with an adaptive parameter genetic algorithm. In: Mira, J., Moreno-Díaz, R., Cabestany, J. (eds) Biological and Artificial Computation: From Neuroscience to Technology. IWANN 1997. Lecture Notes in Computer Science, vol 1240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0032512
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DOI: https://doi.org/10.1007/BFb0032512
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