Genetic Algorithm Augmented Convolutional Neural Network for Image Recognition Applications

In this paper a Genetic Algorithm (GA) augmented Convolutional Neural Network (CNN) procedure has been developed. The proposed approach was implemented in Python and applied on a simple three layer convolutional neural network to explore its effects on training the process. The global searching capability of the GA is exploited to initiate the training process of the conventional Back Propagation (BP) based CNN. The weights of the network are optimally initiated using the GA genetic algorithm rather than using random initializers before training. The proposed method of GA-BP based CNN shows better performance in terms of the training time and accuracy with respect to the conventional BP based CNN.