Improving the performance of convolutional neural networks using evolutionary computing
In this paper, we have proposed an algorithm to design Convolutional Neural Networks (CNN) structures using Genetic Alghorithms (GAs) that are able to learn the best CNN architecture in a completely automatic manner based on limited computing resources. A coding strategy based on sophisticated, hand-designed, modern network blocks has been proposed. So that, the proposed algorithm does not require users with prior knowledge of CNNs, the problem being addressed or even GAs. The performance of the proposed algorithm was evaluated by conducting a series of experiments with widely used reference datasets for image classification tasks and comparing results with modern algorithms that have shown promising performance in this field. The experimental results showed that the proposed algorithm can be used to automatically find a competitive CNN structure compared to modern models, as this algorithm achieved the best classification accuracy among manually and automatically designed CNNs as well as competitive classification accuracy for semi-automatic algorithms.