Genetic learning of neural networks and its applications

The paper presents a constructive method, which combines the architectural feature of the cascade correlation algorithm (CCA) and genetic algorithms for building the neural network and training the corresponding connection weights. Comparisons between the proposed method and the cascade correlation algorithm are made by applying it to SAR image classification. Experimental results showed that the proposed genetic learning method has higher classification rate and can create more compact networks in terms of number of hidden nodes, than that of the standard cascade correlation algorithm.