Deep Neural Networks (DNNs) have achieved great success in many applications. The architectures of DNNs play a crucial role in their performance, which is usually manually designed with rich expertise. However, such a design process is labour intensive because of the trial-and-error process, and also not easy to realize due to the rare expertise in practice. Neural Architecture Search (NAS) is a type of technology that can design the architectures automatically. Among different methods to realize NAS, Evolutionary Computation (EC) methods have recently gained much attention and success. Unfortunately, there has not yet been a comprehensive summary of the EC-based NAS algorithms. This paper reviews over 200 papers of most recent EC-based NAS methods in light of the core components, to systematically discuss their design principles as well as justifications on the design. Furthermore, current challenges and issues are also discussed to identify future research in this emerging field.
翻译:深神经网络(DNN)在许多应用中取得了巨大成功,DNN的架构在它们的工作表现中发挥着关键作用,而这种工作通常都是以丰富的专门知识手工设计的,然而,这种设计过程由于试验和操作过程而耗费大量人力,而且由于实践方面的专门知识很少,也不容易实现。神经建筑搜索是一种可以自动设计建筑的技术类型。在实现NAS的不同方法中,最近对进化计算(EC)方法的关注和成功很多。不幸的是,还没有一份基于EC的NAS算法的综合摘要。本文根据核心组成部分审查200多份最近基于EC的NAS方法的文件,以便系统地讨论其设计原则和设计的理由。此外,还讨论了目前的挑战和问题,以确定这个新兴领域的今后研究。