A Gradient-Guided Evolutionary Approach to Training Deep Neural Networks

Yang，Shangshang1; Tian，Ye2; He，Cheng3; Zhang，Xingyi1; Tan，Kay Chen4; Jin，Yaochu5

2022-09

10.1109/TNNLS.2021.3061630

IEEE Transactions on Neural Networks and Learning Systems   影响因子和分区

2162-2388

Deep neural networks (DNNs) evolutionary algorithm (EA) genetic operator gradient descent sparsity

SCI ; EI

其他

20211110073108

Genetic algorithms ; Recurrent neural networks

IEEE

1.Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei 230039, China.
2.Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China (e-mail: field910921@gmail.com)
3.Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
4.Department of Computing, The Hong Kong Polytechnic University, Hong Kong SAR.
5.Department of Computer Science, University of Surrey, Guildford GU2 7XH, U.K..

Yang，Shangshang,Tian，Ye,He，Cheng,et al. A Gradient-Guided Evolutionary Approach to Training Deep Neural Networks[J]. IEEE Transactions on Neural Networks and Learning Systems,2022,33(9):4861-4875.

Yang，Shangshang,Tian，Ye,He，Cheng,Zhang，Xingyi,Tan，Kay Chen,&Jin，Yaochu.(2022).A Gradient-Guided Evolutionary Approach to Training Deep Neural Networks.IEEE Transactions on Neural Networks and Learning Systems,33(9),4861-4875.

Yang，Shangshang,et al."A Gradient-Guided Evolutionary Approach to Training Deep Neural Networks".IEEE Transactions on Neural Networks and Learning Systems 33.9(2022):4861-4875.