  COVID-19 pandemic has spread globally for months. Due to its long incubation
period and high testing cost, there is no clue showing its spread speed is
slowing down, and hence a faster testing method is in dire need. This paper
proposes an efficient Evolutionary Multi-objective neural ARchitecture Search
(EMARS) framework, which can automatically search for 3D neural architectures
based on a well-designed search space for COVID-19 chest CT scan
classification. Within the framework, we use weight sharing strategy to
significantly improve the search efficiency and finish the search process in 8
hours. We also propose a new objective, namely potential, which is of benefit
to improve the search process's robustness. With the objectives of accuracy,
potential, and model size, we find a lightweight model (3.39 MB), which
outperforms three baseline human-designed models, i.e., ResNet3D101 (325.21
MB), DenseNet3D121 (43.06 MB), and MC3\_18 (43.84 MB). Besides, our
well-designed search space enables the class activation mapping algorithm to be
easily embedded into all searched models, which can provide the
interpretability for medical diagnosis by visualizing the judgment based on the
models to locate the lesion areas.
