@article{DING2023, 
author = {Ding DING and Wenzhe LIU and Changchong SHENG and Jinping SUI and Li LIU},
title = {State of the art and prospects of neural architecture search},
year = {2023},
journal = {Journal of National University of Defense Technology},
volume = {45},
number = {6},
pages = {100-131},
keywords = {deep learning, neural architecture search, automation of machine learning, reinforcement learning, search space design, search strategy, evolutionary algorithms},
url = {https://www.sciopen.com/article/10.11887/j.cn.202306014},
doi = {10.11887/j.cn.202306014},
abstract = {Neural architecture search is a task that aims to automatically search for the optimal neural network structure for different tasks, which is of great importance and inevitability in the joint development of deep learning and computer vision to the current stage. A comprehensive review of the research on neural network search was provided. In specific, the definition and significance of neural architecture search were introduced, and the difficulties and challenges faced in relevant research were deeply analyzed. Based on this, the mainstream search strategies was elaborate and summarize; Finally, the potential problems and possible future research directions were summarized and discussed to promote further development in this field.}
}