Michael Margaliot Tel Aviv University, Israel 1556-603X/21©2021IEEE AUGUST 2021 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 67 Abstract—Recently, convolutional neural networks (CNNs) have achieved great success in the field of artificial intelligence, including speech recognition, image recognition, and natural language processing. CNN architecture plays a key role in CNNs’ performance. Most previous CNN architectures are hand-crafted, which requires designers to have rich expert domain knowledge. The trial-and-error process consumes a lot of time and computing resources. To solve this problem, researchers proposed the neural architecture search, which searches CNN architecture automatically, to satisfy dif- ferent requirements. However, the blindness of the search strategy causes a ‘loss of experience’ in the early stage of the search process, and ultimately affects the results of the later stage. In this paper, we propose a self-adaptive mutation neural architecture search algorithm based on ResNet blocks and DenseNet blocks. The self-adaptive mutation strategy makes the algorithm adaptively adjust the mutation strategies during the evolution process to achieve better exploration. In addition, the whole search pro- cess is fully automatic, and users do not need expert knowl- edge about CNNs architecture design. In this paper, the proposed algorithm is compared with 17 state-of-the-art algorithms, including manually designed CNN and automatic search algorithms on CIFAR10 and CIFAR100. The results indicate that the proposed algorithm outperforms the com- petitors in terms of classification performance and consumes fewer computing resources. Digital Object Identifier 10.1109/MCI.2021.3084435 Date of current version: 15 July 2021 Corresponding Author: Yu Xue (e-mail: xueyu@nuist.edu.cn). Yu Xue and Yankang Wang Nanjing University of Information Science and Technology, CHINA Jiayu Liang Tiangong University, CHINA Adam Slowik Koszalin University of Technology, POLAND A Self-Adaptive Mutation Neural Architecture Search Algorithm Based on Blocks ©SHUTTERSTOCK.COM/BETTERVECTOR Authorized licensed use limited to: Nanjing University of Information Science and Technology. Downloaded on July 22,2021 at 05:28:02 UTC from IEEE Xplore. Restrictions apply.