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.

通过将搜索空间转换为相互连接的单元，并顺序地搜索这些单元，然后对整个网络的上述搜索进行多次循环以减小先前单元对后续单元的影响，我们提出了 Multiple Population Alternate Evolution Neural Architecture Search（MPAE）这一新颖的神经网络架构搜索范式，并在迁移归档中建立出色的迁移档案，并将其中的优秀知识和经验传递给新的群体，从而加速种群进化过程，该方法仅需要 0.3 GPU 天即可在 CIFAR 数据集上搜索神经网络，并达到了最先进的结果。