  Neural architecture search (NAS) aims to automate architecture engineering in
neural networks. This often requires a high computational overhead to evaluate
a number of candidate networks from the set of all possible networks in the
search space during the search. Prediction of the networks' performance can
alleviate this high computational overhead by mitigating the need for
evaluating every candidate network. Developing such a predictor typically
requires a large number of evaluated architectures which may be difficult to
obtain. We address this challenge by proposing a novel evolutionary-based NAS
strategy, Predictor-assisted E-NAS (PRE-NAS), which can perform well even with
an extremely small number of evaluated architectures. PRE-NAS leverages new
evolutionary search strategies and integrates high-fidelity weight inheritance
over generations. Unlike one-shot strategies, which may suffer from bias in the
evaluation due to weight sharing, offspring candidates in PRE-NAS are
topologically homogeneous, which circumvents bias and leads to more accurate
predictions. Extensive experiments on NAS-Bench-201 and DARTS search spaces
show that PRE-NAS can outperform state-of-the-art NAS methods. With only a
single GPU searching for 0.6 days, competitive architecture can be found by
PRE-NAS which achieves 2.40% and 24% test error rates on CIFAR-10 and ImageNet
respectively.
