An Improved Genetic Algorithm for Automated Convolutional Neural Network Design

Extracting the features from an image is a cumbersome task. Initially, this task was performed by domain experts through a process known as handcrafted feature design. A deep embedding technique known as convolutional neural networks (CNNs) later solved this problem by introducing the feature learning concept, through which the CNN is directly provided with images. This CNN then learns the features of the image, which are subsequently given as input to the further layers for an intended task like classification. CNNs have demonstrated astonishing performance in several practicable applications in the last few years. Nevertheless, the pursuance of CNNs primarily depends upon their architecture, which is handcrafted by domain expertise and type of investigated problem. On the other hand, for researchers who do not have proficiency in using CNNs, it has been very difficult to explore this topic in their problem statements. In this paper, we have come up with a rank and gradient descent-based optimized genetic algorithm to automatically find the architecture design of CNNs that is vigorously competent in exploring the best CNN architecture for maneuvering the tasks of image classification. In the proposed algorithm, there is no requirement for handcrafted pre- and post-processing, which implies that the algorithm is fully mechanized. The validation of the proposed algorithm on conventional benchmarked datasets has been done by comparing the run time of a graphics processing unit (GPU) throughout the training process and assessing the accuracy of various measures. The experimental results show that the proposed algorithm accomplishes better and more persistent 'classification accuracy' than the original genetic algorithm on the CIFAR datasets by using fifty percent less intensive computing resources for training the individual CNN and the entire population.