  Neural architecture search (NAS) enables re-searchers to automatically
explore vast search spaces and find efficient neural networks. But NAS suffers
from a key bottleneck, i.e., numerous architectures need to be evaluated during
the search process, which requires a lot of computing resources and time. In
order to improve the efficiency of NAS, a series of methods have been proposed
to reduce the evaluation time of neural architectures. However, they are not
efficient enough and still only focus on the accuracy of architectures. In
addition to the classification accuracy, more efficient and smaller network
architectures are required in real-world applications. To address the above
problems, we propose the SMEM-NAS, a pairwise com-parison relation-assisted
multi-objective evolutionary algorithm based on a multi-population mechanism.
In the SMEM-NAS, a surrogate model is constructed based on pairwise compari-son
relations to predict the accuracy ranking of architectures, rather than the
absolute accuracy. Moreover, two populations cooperate with each other in the
search process, i.e., a main population guides the evolution, while a vice
population expands the diversity. Our method aims to provide high-performance
models that take into account multiple optimization objectives. We conduct a
series of experiments on the CIFAR-10, CIFAR-100 and ImageNet datasets to
verify its effectiveness. With only a single GPU searching for 0.17 days,
competitive architectures can be found by SMEM-NAS which achieves 78.91%
accuracy with the MAdds of 570M on the ImageNet. This work makes a significant
advance in the important field of NAS.
