Deep learning has made substantial breakthroughs in many fields due to its powerful automatic representation capabilities. It has been proven that neural architecture design is crucial to the feature representation of data and the final performance. However, the design of the neural architecture heavily relies on the researchers’ prior knowledge and experience. And due to the limitations of humans’ inherent knowledge, it is difficult for people to jump out of their original thinking paradigm and design an optimal model. Therefore, an intuitive idea would be to reduce human intervention as much as possible and let the algorithm automatically design the neural architecture. Neural Architecture Search ( NAS ) is just such a revolutionary algorithm, and the related research work is complicated and rich. Therefore, a comprehensive and systematic survey on the NAS is essential. Previously related surveys have begun to classify existing work mainly based on the key components of NAS: search space, search strategy, and evaluation strategy. While this classification method is more intuitive, it is difficult for readers to grasp the challenges and the landmark work involved. Therefore, in this survey, we provide a new perspective: beginning with an overview of the characteristics of the earliest NAS algorithms, summarizing the problems in these early NAS algorithms, and then providing solutions for subsequent related research work. In addition, we conduct a detailed and comprehensive analysis, comparison, and summary of these works. Finally, we provide some possible future research directions.

中文翻译：

---

神经架构搜索的综合调查

深度学习由于其强大的自动表示能力，在很多领域都取得了实质性的突破。已经证明，神经架构设计对于数据的特征表示和最终性能至关重要。然而，神经架构的设计在很大程度上依赖于研究人员的先验知识和经验。而由于人类固有知识的局限，人们很难跳出原有的思维范式，设计出最优的模型。因此，一个直观的想法是尽可能减少人为干预，让算法自动设计神经架构。 神经架构搜索 (NAS)就是这样一个革命性的算法，相关的研究工作复杂而丰富。因此，对NAS进行全面而系统的调查至关重要。之前的相关调查已经开始主要根据 NAS 的关键组成部分对现有工作进行分类：搜索空间、搜索策略和评估策略。虽然这种分类方法更直观，但读者很难把握其中的挑战和具有里程碑意义的工作。因此，在本次调查中，我们提供了一个新的视角：首先概述最早的 NAS 算法的特点，总结这些早期 NAS 算法存在的问题，然后为后续的相关研究工作提供解决方案。此外，我们对这些作品进行了详细而全面的分析、比较和总结。最后，