Fabricating neural models for a wide range of mobile devices demands for a
specific design of networks due to highly constrained resources. Both evolution
algorithms (EA) and reinforced learning methods (RL) have been dedicated to
solve neural architecture search problems. However, these combinations usually
concentrate on a single objective such as the error rate of image
classification. They also fail to harness the very benefits from both sides. In
this paper, we present a new multi-objective oriented algorithm called MoreMNAS
(Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search) by
leveraging good virtues from both EA and RL. In particular, we incorporate a
variant of multi-objective genetic algorithm NSGA-II, in which the search space
is composed of various cells so that crossovers and mutations can be performed
at the cell level. Moreover, reinforced control is mixed with a natural
mutating process to regulate arbitrary mutation, maintaining a delicate balance
between exploration and exploitation. Therefore, not only does our method
prevent the searched models from degrading during the evolution process, but it
also makes better use of learned knowledge. Our experiments conducted in
Super-resolution domain (SR) deliver rivalling models compared to some
state-of-the-art methods with fewer FLOPS.

本文提出了一种新的多目标导向算法 MoreMNAS（Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search），通过结合 EA 和 RL 的优点，将 NSGA-II 遗传算法与自然突变过程相混合，使得神经模型搜索过程中不仅能够避免模型退化，还能更好地利用已学习的知识，并在超分辨领域 SR 中进行实验，获得比某些现有技术更少的 FLOPS 令人瞩目的模型。