Structure tuning method on deep convolutional generative adversarial network with nondominated sorting genetic algorithm II
Lei Du, Zhihua Cui, Lifang Wang, Junming Ma · Concurrency and Computation Practice and Experience · 2020
Abstract Currently the generative adversarial networks (GANs) have rapidly become a popular research hotspot that people concerned and have been applied to various fields. Lots of meaningful work have been proposed and various variants of GANs sprung up in last few years. The scholars usually design GAN structure like the layers and hyperparameters setting according to the experience and constantly attempts. For the propose of finding the appropriate structure more conveniently and efficiently. A method with multiobjective algorithm is proposed to obtain the optimal structure for the GANs. In the proposed method, the nondominated sorting genetic algorithm II (NSGA‐II) is utilized to optimize the hyperparameters and structure of deep convolutional generative adversarial network (DCGAN). The experiments are conducted on MNIST and Malware datasets demonstrate the efficiency and high performance of proposed method.