A Fast Evolutionary Learning to Optimize CNN

Chinese Journal of ElectronicsVolume 29, Issue 6 p. 1061-1073 ArticleFree Access A Fast Evolutionary Learning to Optimize CNN Jinyin Chen, College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023 ChinaSearch for more papers by this authorXiang Lin, Corresponding Author lynnzlnx@163.com College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023 ChinaSearch for more papers by this authorShangtengda Gao, NESA Lab, Zhejiang University, Hangzhou, 310027 ChinaSearch for more papers by this authorHui Xiong, College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023 ChinaSearch for more papers by this authorLongyuan Zhang, Institute of Process Equipment and Control Engineering, Hangzhou, 310023 ChinaSearch for more papers by this authorYi Liu, Institute of Process Equipment and Control Engineering, Hangzhou, 310023 ChinaSearch for more papers by this authorQi Xuan, College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023 ChinaSearch for more papers by this author Jinyin Chen, College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023 ChinaSearch for more papers by this authorXiang Lin, Corresponding Author lynnzlnx@163.com College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023 ChinaSearch for more papers by this authorShangtengda Gao, NESA Lab, Zhejiang University, Hangzhou, 310027 ChinaSearch for more papers by this authorHui Xiong, College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023 ChinaSearch for more papers by this authorLongyuan Zhang, Institute of Process Equipment and Control Engineering, Hangzhou, 310023 ChinaSearch for more papers by this authorYi Liu, Institute of Process Equipment and Control Engineering, Hangzhou, 310023 ChinaSearch for more papers by this authorQi Xuan, College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023 ChinaSearch for more papers by this author First published: 01 November 2020 https://doi.org/10.1049/cje.2020.09.007AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Abstract Deep neural networks (DNNs) show great performance in lots of applications. Convolutional neural network (CNN) is one of the classic DNNs, and various modified CNNs have been brought up, such as DenseNet, GoogleNet, ResNet, etc. For diverse tasks, a unique structure of CNN may show its advantage. However, how to design an effective CNN model for a practical task is a puzzle. In this paper, we model the architecture optimization of CNN as an optimization problem and design a Genetic network programming based Fast evolutionary learning (GNP-FEL) to optimize CNN. GNP-FEL contains three main ideas: First GNP is adopted to optimize CNN architecture and hyperparameters, which can build diverse network structures and make network parameters selfevolve; Second multi-objective optimization is designed by balancing both CNN model efficiency and structure compactness; Last a novel incremental training method is proposed to train offspring CNN models in GNP, which is capable of reducing time complexity sharply. Experiments have validated that GNP-FEL can quickly evolve a CNN classifier with a sufficiently compact architecture. And the classifier has a comparable classification effect to state-oftheart CNN model. Volume29, Issue6November 2020Pages 1061-1073 RelatedInformation