The performance of a deep neural network is heavily dependent on its architecture and various neural architecture search strategies have been developed for automated network architecture design. Recently, evolutionary neural architecture search (ENAS) has received increasing attention due to the attractive global optimization capability of evolutionary algorithms. However, ENAS suffers from extremely high computation costs because a large number of performance evaluations is usually required in evolutionary optimization and training deep neural networks is itself computationally very intensive. To address this issue, this paper proposes a new evolutionary framework for fast ENAS based on directed acyclic graph, in which parents are randomly sampled and trained on each mini-batch of training data. In addition, a node inheritance strategy is adopted to generate offspring individuals and their fitness is directly evaluated without training. To enhance the feature processing capability of the evolved neural networks, we also encode a channel attention mechanism in the search space. We evaluate the proposed algorithm on the widely used datasets, in comparison with 26 state-of-the-art peer algorithms. Our experimental results show the proposed algorithm is not only computationally much more efficiently, but also highly competitive in learning performance.

中文翻译：

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用于快速进化神经架构搜索的采样训练和节点继承

深度神经网络的性能在很大程度上取决于其架构，并且已经为自动化网络架构设计开发了各种神经架构搜索策略。最近，由于进化算法具有吸引力的全局优化能力，进化神经架构搜索（ENAS）受到越来越多的关注。然而，ENAS 的计算成本非常高，因为在进化优化中通常需要大量的性能评估，并且训练深度神经网络本身的计算量非常大。为了解决这个问题，本文提出了一种新的基于有向无环图的快速 ENAS 进化框架，其中父母在每个小批量训练数据上随机采样和训练。此外，采用节点继承策略生成后代个体，无需训练直接评估其适应度。为了增强进化神经网络的特征处理能力，我们还在搜索空间中编码了一个通道注意机制。与 26 种最先进的对等算法相比，我们在广泛使用的数据集上评估了所提出的算法。我们的实验结果表明，所提出的算法不仅在计算上更有效，而且在学习性能方面也具有很强的竞争力。与 26 种最先进的对等算法进行比较。我们的实验结果表明，所提出的算法不仅在计算上更有效，而且在学习性能方面也具有很强的竞争力。与 26 种最先进的对等算法进行比较。我们的实验结果表明，所提出的算法不仅在计算上更有效，而且在学习性能方面也具有很强的竞争力。