Abstract
A genetic algorithm (GA) method that evolves both the topology and training parameters of backpropagation-trained, fully-connected, feed-forward neural networks is presented. The GA uses a weak encoding scheme with real-valued alleles. One contribution of the proposed approach is to replace the needed but potentially slow evolution of final weights by the more efficient evolution of a single weight spread parameter used to set the initial weights only. In addition, the co-evolution of an input mask effects a form of automatic feature selection. Preliminary experiments suggest that the resulting system is able to produce networks that perform well under backpropagation.
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© 1999 Springer-Verlag Wien
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Lock, D., Giraud-Carrier, C. (1999). Evolutionary Programming of Near-Optimal Neural Networks. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6384-9_50
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DOI: https://doi.org/10.1007/978-3-7091-6384-9_50
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83364-3
Online ISBN: 978-3-7091-6384-9
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