Evolving the Architecture and Weights of Neural Networks Using a Weight Mapping Approach

The application of genetic programming to the evolution of n eural networks has been hindered by the inadequacy of parse trees to represent oriented graph s, nd by the lack of a good mechanism for encoding the weights. In this work, a hybrid method is int roduced, where genetic programming evolves a mapping function to adapt the weights, whereas a ge netic algorithm-based approach evolves the architecture. Results on the application of the new method to the evolution of feedforward and recurrent neural networks are reported.

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