  Neural architecture search (NAS) is a promising method for automatically
design neural architectures. NAS adopts a search strategy to explore the
predefined search space to find outstanding performance architecture with the
minimum searching costs. Bayesian optimization and evolutionary algorithms are
two commonly used search strategies, but they suffer from computationally
expensive, challenge to implement or inefficient exploration ability. In this
paper, we propose a neural predictor guided evolutionary algorithm to enhance
the exploration ability of EA for NAS (NPENAS) and design two kinds of neural
predictors. The first predictor is defined from Bayesian optimization and we
propose a graph-based uncertainty estimation network as a surrogate model that
is easy to implement and computationally efficient. The second predictor is a
graph-based neural network that directly outputs the performance prediction of
the input neural architecture. The NPENAS using the two neural predictors are
denoted as NPENAS-BO and NPENAS-NP respectively. In addition, we introduce a
new random architecture sampling method to overcome the drawbacks of the
existing sampling method. Extensive experiments demonstrate the superiority of
NPENAS. Quantitative results on three NAS search spaces indicate that both
NPENAS-BO and NPENAS-NP outperform most existing NAS algorithms, with NPENAS-BO
achieving state-of-the-art performance on NASBench-201 and NPENAS-NP on
NASBench-101 and DARTS, respectively.
