Adaptive Multiobjective Evolutionary Neural Architecture Search for GANs based on Two-Factor Cooperative Mutation Mechanism

The automated design of generative adversarial networks (GAN) is currently being solved well by neural architecture search (NAS), although there are still some issues. One problem is the vast majority of NAS for GANs methods are only based on a single evaluation metric or a linear superposition of multiple evaluation metrics. Another problem is that the conventional evolutionary neural architecture search (ENAS) is unable to adjust its mutation probabilities in accordance with the NAS process, making it simple to settle into a local optimum. To address these issues, we firstly design a two-factor cooperative mutation mechanism that can control the mutation probability based on the current iteration rounds of the population, population fitness and other information. Secondly, we divide the evolutionary process into three stages based on the properties of NAS, so that the different stages can adaptively adjust the mutation probability according to the population state and the expected development goals. Finally, we incorporate multiple optimization objectives from GANs based on image generation tasks into ENAS. And we construct an adaptive multiobjective ENAS based on a two-factor cooperative mutation mechanism. We test and ablate our algorithm on the STL-10 and CIFAR-10 datasets, and the experimental results show that our method outperforms the majority of traditional NAS-GANs.