The COVID-19 pandemic has caused a global alarm. With the advances in artificial intelligence, the COVID-19 testing capabilities have been greatly expanded, and hospital resources are significantly alleviated. Over the past years, computer vision researches have focused on convolutional neural networks (CNNs), which can significantly improve image analysis ability. However, CNN architectures are usually manually designed with rich expertise that is scarce in practice. Evolutionary algorithms (EAs) can automatically search for the proper CNN architectures and voluntarily optimize the related hyperparameters. The networks searched by EAs can be used to effectively process COVID-19 computed tomography images without expert knowledge and manual setup. In this paper, we propose a novel EA-based algorithm with a dynamic searching space to design the optimal CNN architectures for diagnosing COVID-19 before the pathogenic test. The experiments are performed on the COVID-CT data set against a series of state-of-the-art CNN models. The experiments demonstrate that the architecture searched by the proposed EA-based algorithm achieves the best performance yet without any preprocessing operations. Furthermore, we found through experimentation that the intensive use of batch normalization may deteriorate the performance. This contrasts with the common sense approach of manually designing CNN architectures and will help the related experts in handcrafting CNN models to achieve the best performance without any preprocessing operations

COVID-19大流行引起了全球警报。随着人工智能的进步，COVID-19检测能力得到极大扩展，医院资源得到显着缓解。过去几年，计算机视觉研究主要集中在卷积神经网络（CNN）上，它可以显着提高图像分析能力。然而，CNN 架构通常是手动设计的，具有丰富的专业知识，但在实践中却很稀缺。进化算法（EA）可以自动搜索合适的 CNN 架构并自动优化相关的超参数。 EA 搜索的网络可用于有效处理 COVID-19 计算机断层扫描图像，无需专业知识和手动设置。在本文中，我们提出了一种基于 EA 的新型算法，具有动态搜索空间，以设计用于在致病性测试之前诊断 COVID-19 的最佳 CNN 架构。这些实验是在 COVID-CT 数据集上针对一系列最先进的 CNN 模型进行的。实验表明，所提出的基于 EA 的算法搜索的架构无需任何预处理操作即可实现最佳性能。此外，我们通过实验发现，大量使用批量归一化可能会降低性能。这与手动设计 CNN 架构的常识方法形成鲜明对比，将帮助相关专家手动设计 CNN 模型，无需任何预处理操作即可实现最佳性能