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Neural network architecture search provides a solution to the automaticdesign of network structures. However, it is difficult to search the wholenetwork architecture directly. Although using stacked cells to search neuralnetwork architectures is an effective way to reduce the complexity ofsearching, these methods do not able find the global optimal neural networkstructure since the number of layers, cells and connection methods is fixed. Inthis paper, we propose a Two-Stage Evolution for cell-based NetworkArchitecture Search(TS-ENAS), including one-stage searching based on stackedcells and second-stage adjusting these cells. In our algorithm, a newcell-based search space and an effective two-stage encoding method are designedto represent cells and neural network structures. In addition, a cell-basedweight inheritance strategy is designed to initialize the weight of thenetwork, which significantly reduces the running time of the algorithm. Theproposed methods are extensively tested and compared on four imageclassification dataset, Fashion-MNIST, CIFAR10, CIFAR100 and ImageNet andcompared with 22 state-of-the-art algorithms including hand-designed networksand NAS networks. The experimental results show that TS-ENAS can moreeffectively find the neural network architecture with comparative performance.

TS-ENAS:Two-Stage Evolution for Cell-based Network Architecture Search