Fast automatic optimisation of CNN architectures for image classification using genetic algorithm

Convolutional Neural Networks (CNNs) are currently the most prominent deep neural network models and have been used with great success for image classification and other applications. The performance of CNNs depends on their architecture and hyperparameter settings. Early CNN models like LeNet and AlexNet were manually designed by experienced researchers. The empirical design and optimisation of a new CNN architecture require a lot of expertise and can be very time-consuming. In this paper, we propose a genetic algorithm that can, for a given image processing task, efficiently explore a defined space of potentially suitable CNN architectures and simultaneously optimise their hyperparameters. We named this fast automatic optimisation model fast-CNN and employed it to find competitive CNN architectures for image classification on CIFAR10. In a series of comparative simulation experiments we could demonstrate that the network designed by fast-CNN achieved nearly as good accuracy as some of the other best network models available but fast-CNN took significantly less time to evolve. The trained fast-CNN network model also generalised well to CIFAR100.