Least square method based evolutionary neural learning algorithm

We present a new idea of evolving weights for the artificial neural networks (ANNs), and propose a novel hybrid learning approach for the training of a feedforward ANN. The approach combines evolutionary algorithms with matrix solution methods such as Gram-Schmidt, etc., to achieve optimum weights for hidden and output layers. Our hybrid method is to apply the evolutionary algorithm in the first layer and the least square method in the second layer of the ANN. A two-layer network is considered. The hidden layer weights are evolved using the evolutionary algorithm. When a certain number of generation or error goal in terms of RMS/class error is reached, the training stops. We start with a small number of hidden neurons, and then the number is increased gradually. We applied our algorithm for XOR, 10-bit odd parity and handwritten segmented characters recognition problems. The implementation of the algorithm was done in MATLAB and C. Experiments show some promising results when compared with other evolutionary based algorithm only in terms of results in classification rate and time complexity