Hyperparameter optimization of deep neural network using univariate dynamic encoding algorithm for searches

作者：

Highlights：

• An optimization method for hyper-parameters for a deep neural network.

• Performing optimization of the network using a univariate dynamic encoding algorithm for searches.

• Validation of the proposed method with two neural network model with MNIST data set.

• Fast convergence speed and a small computational amount to optimize hyper-parameter of the network.

摘要

•An optimization method for hyper-parameters for a deep neural network.•Performing optimization of the network using a univariate dynamic encoding algorithm for searches.•Validation of the proposed method with two neural network model with MNIST data set.•Fast convergence speed and a small computational amount to optimize hyper-parameter of the network.

论文关键词：Hyperparameter optimization,Gradient-free optimization,Deep neural network,Convolution neural network,Autoencoder

论文评审过程：Received 25 December 2018, Revised 21 April 2019, Accepted 24 April 2019, Available online 3 May 2019, Version of Record 4 June 2019.

论文官网地址：https://doi.org/10.1016/j.knosys.2019.04.019