Neuroevolutionary optimization
This paper presents an application of evolutionary search
procedures to artificial neural networks. Here, we can distinguish
among three kinds of evolution in artificial neural networks, i.e.
the evolution of connection weights, of architectures, and of
learning rules. We review each kind of evolution in detail and
analyse critical issues related to different evolutions. This article
concentrates on finding the suitable way of using evolutionary
algorithms for optimizing the artificial neural network
parameters.
Keywords: evolutionary algorithms, artificial neural networks
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