Designing Optimal Convolutional Neural Network Architecture Using Differential Evolution Algorithm

Convolutional Neural Networks (CNNs) are widely used deep learning models for solving various tasks such as computer vision, speech recognition, among others. However, CNNs are developed manually based on problem-specific domain knowledge and tricky settings, which are laborious, time-consuming and challenging. To address these issues, this study proposes an Improved Differential Evolution of Convolutional Neural Network algorithm, namely IDECNN, to design CNN layer architectures for image classification task.