Abstract
A wavelet-based neural network with evolutionary programming is proposed. Unlike conventional backpropagation training algorithms, the evolutionary programming does not require gradient information and can provide a stochastic optimal search. Evolutionary programming is applied to optimise the wavelet neural network for function approximation. Experimental results are presented to show the potential of the evolutionary wavelet neural networks.
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