NPENAS: Neural Predictor Guided Evolution for Neural Architecture Search

Neural architecture search (NAS) is a promising method for automatically finding excellent architectures.Commonly used search strategies such as evolutionary algorithm, Bayesian optimization, and Predictor method employs a predictor to rank sampled architectures. In this paper, we propose two predictor based algorithms NPUBO and NPENAS for neural architecture search. Firstly we propose NPUBO which takes a neural predictor with uncertainty estimation as surrogate model for Bayesian optimization. Secondly we propose a simple and effective predictor guided evolution algorithm(NPENAS), which uses neural predictor to guide evolutionary algorithm to perform selection and mutation. Finally we analyse the architecture sampling pipeline and find that mostly used random sampling pipeline tends to generate architectures in a subspace of the real underlying search space. Our proposed methods can find architecture achieves high test accuracy which is comparable with recently proposed methods on NAS-Bench-101 and NAS-Bench-201 dataset using less training and evaluated samples. Code will be publicly available after finish all the experiments.