Abstract
Neural networks are parallel processing structures that provide the capability to perform various pattern recognition tasks. A network is typically trained over a set of exemplars by adjusting the weights of the interconnections using a back propagation algorithm. This gradient search converges to locally optimal solutions which may be far removed from the global optimum. In this paper, evolutionary programming is analyzed as a technique for training a general neural network. This approach can yield faster, more efficient yet robust training procedures that accommodate arbitrary interconnections and neurons possessing additional processing capabilities.
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Fogel, D.B., Fogel, L.J. & Porto, V.W. Evolving neural networks. Biol. Cybern. 63, 487–493 (1990). https://doi.org/10.1007/BF00199581
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DOI: https://doi.org/10.1007/BF00199581