A Review of Evolutionary Deep Neural Architecture Search with Performance Predictor

QIAOZenglin; QIAO Zenglin; ZHAOXinchao; ZHAO Xinchao; WULingyu; WU Lingyu

doi:10.48014/bcam.20230822002

A Review of Evolutionary Deep Neural Architecture Search with Performance Predictor

中国应用数学通报 2023年第1卷第1期页码[1-9] 下载全文[2MB]

Abstract

Automated design of deep neural networks using performance predictor has become a hot topic in current research. Neural architecture search (NAS) methods can be used to enable automatic design of neural network structures by defining different search spaces, search strategies, or optimization strategies. Evolutionary computation by many researchers as the search strategy for NAS, which is called evolutionary NAS (ENAS) . However, ENAS is time-consuming in evaluating the performance of network structures, which hinders the development of ENAS. Therefore, predicting network architecture performance using performance predictor can improve ENAS search speed and save computational resources. This paper summarizes several ENAS methods that utilize performance predictor, and discusses an outlook on search space, search strategies, and the future directions of ENAS assisted by performance predictor.

DOI	10.48014/bcam.20230822002
文章类型	研究性论文
收稿日期	2023-08-22
接收日期	2023-09-15
出版日期	2023-12-28
Keywords	Neural architecture search, evolutionary algorithm, deep learning, convolutional neural network
作者	QIAO Zenglin^1,2, ZHAO Xinchao^1,2,*, WU Lingyu^1,2
Author	QIAO Zenglin^1,2, ZHAO Xinchao^1,2,*, WU Lingyu^1,2
Company	1. School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China 2. Key Laboratory of Mathematics and Information Networks (Beijing University of Posts and Telecommunications) , Ministry of Education, Beijing 100876, China.
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Citation	QIAO Zenglin, ZHAO Xinchao, WU Lingyu. A review of evolutionary deep neural architecture search with performance predictor[J]. Bulletin of Chinese Applied Mathematics, 2023, 1(1): 1-9.