Multiple Population Alternate Evolution Neural Architecture Search
Juan Zou Han Chu Yizhang Xia Junwen Xu Yuan Liu Zhanglu Hou
The effectiveness of Evolutionary Neural Architecture Search (ENAS) is
influenced by the design of the search space. Nevertheless, common methods
including the global search space, scalable search space and hierarchical
search space have certain limitations. Specifically, the global search space
requires a significant amount of computational resources and time, the scalable
search space sacrifices the diversity of network structures and the
hierarchical search space increases the search cost in exchange for network
diversity. To address above limitation, we propose a novel paradigm of
searching neural network architectures and design the Multiple Population
Alternate Evolution Neural Architecture Search (MPAE), which can achieve module
diversity with a smaller search cost. MPAE converts the search space into L
interconnected units and sequentially searches the units, then the above search
of the entire network be cycled several times to reduce the impact of previous
units on subsequent units. To accelerate the population evolution process, we
also propose the the population migration mechanism establishes an excellent
migration archive and transfers the excellent knowledge and experience in the
migration archive to new populations. The proposed method requires only 0.3 GPU
days to search a neural network on the CIFAR dataset and achieves the
state-of-the-art results.