Guest Editorial Evolutionary Neural Architecture Search

Deep neural networks (DNNs) have shown remarkable performance in solving a wide variety of real-world problems, ranging from image recognition to natural language processing and self-driving vehicles. In principle, the achievements of DNNs are mainly contributed by their deep architectures, which can learn meaningful representations at different levels. This can greatly enhance the performance of the subsequent machine-learning algorithms. However, manually designing an optimal deep architecture for a particular problem requires a rich knowledge of both the investigated problem domain and the DNNs, which is not necessarily held by every end user interested in this area.