Reinforced Evolutionary Neural Architecture Search.

Neural architecture search (NAS) is an important yet challenging task in network design due to its high computational consumption and low stability. To address these two issues, we propose the Reinforced Evolutionary Neural Architecture Search (RENAS), which is an evolutionary method with reinforced mutation for NAS. Our method integrates reinforced mutation into an evolution algorithm for neural architecture exploration, in which a mutation controller to learn the effects of slight modifications and make mutation actions. The reinforced mutation controller instructs the model population to evolve efficiently in a suitable direction. Furthermore, as child models can inherit parameters from their parents during evolution, our method requires very limited computational resources. We conduct the proposed search method on CIFAR-10 with 4 GPUs (Titan Xp) across 1.5 days and discover a powerful network architecture. This architecture achieves a competitive result on CIFAR-10. We further apply the explored network architecture to the mobile setting ImageNet. The network achieves a new state-of-the-art accuracy, i.e., 75.7% top-1 accuracy with 5.36M parameters.