A Survey on Evolutionary Neural Architecture Search

Deep Neural Networks (DNNs) have achieved great success in many applications, such as image classification, natural language processing and speech recognition. The architectures of DNNs have been proved to play a crucial role in its performance. However, mamually designing architectures for different tasks is a difficult and time-consuming process of trial and error. Neural Architecture Search (NAS), which received great attention in recent years, can design the architecture automatically. Among different kinds of NAS methods, Evolutionary Computation (EC) based NAS methods have recently gained much attention and success. Unfortunately, there has not yet been a comprehensive summary of the EC-based methods. This paper reviews 100+ papers of EC-based NAS methods in light of the common process. Four steps of the process have been covered in this paper including encoding space determination, population initialization, population updating and evaluation. Furthermore, current challenges and issues are also discussed to identify future research in this field.