  The evolutionary paradigm has been successfully applied to neural network
search(NAS) in recent years. Due to the vast search complexity of the global
space, current research mainly seeks to repeatedly stack partial architectures
to build the entire model or to seek the entire model based on manually
designed benchmark modules. The above two methods are attempts to reduce the
search difficulty by narrowing the search space. To efficiently search network
architecture in the global space, this paper proposes another solution, namely
a computationally efficient neural architecture evolutionary search framework
based on network growth (G-EvoNAS). The complete network is obtained by
gradually deepening different Blocks. The process begins from a shallow
network, grows and evolves, and gradually deepens into a complete network,
reducing the search complexity in the global space. Then, to improve the
ranking accuracy of the network, we reduce the weight coupling of each network
in the SuperNet by pruning the SuperNet according to elite groups at different
growth stages. The G-EvoNAS is tested on three commonly used image
classification datasets, CIFAR10, CIFAR100, and ImageNet, and compared with
various state-of-the-art algorithms, including hand-designed networks and NAS
networks. Experimental results demonstrate that G-EvoNAS can find a neural
network architecture comparable to state-of-the-art designs in 0.2 GPU days.
