  Neural network architecture search provides a solution to the automatic
design of network structures. However, it is difficult to search the whole
network architecture directly. Although using stacked cells to search neural
network architectures is an effective way to reduce the complexity of
searching, these methods do not able find the global optimal neural network
structure since the number of layers, cells and connection methods is fixed. In
this paper, we propose a Two-Stage Evolution for cell-based Network
Architecture Search(TS-ENAS), including one-stage searching based on stacked
cells and second-stage adjusting these cells. In our algorithm, a new
cell-based search space and an effective two-stage encoding method are designed
to represent cells and neural network structures. In addition, a cell-based
weight inheritance strategy is designed to initialize the weight of the
network, which significantly reduces the running time of the algorithm. The
proposed methods are extensively tested and compared on four image
classification dataset, Fashion-MNIST, CIFAR10, CIFAR100 and ImageNet and
compared with 22 state-of-the-art algorithms including hand-designed networks
and NAS networks. The experimental results show that TS-ENAS can more
effectively find the neural network architecture with comparative performance.
