Evolutionary approach for approximation of artificial neural network

Neural Network is an effective tool in the field of pattern recognition. The neural network classifies the pattern from the training data and recognizes if the testing data holds that pattern. The classical Back propagation (BP) algorithm is generally used to train the neural network for its simplicity. The basic drawback of this algorithm is its uncertainty and long training time and it searches the local optima and not the global optima. To overcome the drawback of Back propagation (BP) algorithm, here we use a hybrid evolutionary approach (GA-NN algorithm) to train neural networks. The aim of this algorithm is to find the optimized synaptic weight of neural network so as to escape from local minima and overcome the drawbacks of BP. The implementation is done taking images as input in ¿.png¿and ¿.tif¿ format.