BenchENAS: A Benchmarking Platform for Evolutionary Neural Architecture Search
为解决进化神经架构搜索（ENAS）中算法比较不公平和评估成本高的问题，开发了BenchENAS平台，通过统一设置和高效评估方法，实现了公平比较，并验证了不公平问题的存在。
Neural architecture search (NAS), which automatically designs the architectures of deep neural networks, has achieved breakthrough success over many applications in the past few years. Among different classes of NAS methods, evolutionary computation-based NAS (ENAS) methods have recently gained much attention. Unfortunately, the development of ENAS is hindered by unfair comparison between different ENAS algorithms due to different training conditions and high computational cost caused by expensive performance evaluation. This article develops a platform named BenchENAS, in short for benchmarking evolutionary NAS, to address these issues. BenchENAS makes it easy to achieve fair comparisons between different algorithms by keeping them under the same settings. To accelerate the performance evaluation in a common lab environment, BenchENAS designs a novel and generic efficient evaluation method for the population characteristics of evolutionary computation. This method has greatly improved the efficiency of the evaluation. Furthermore, BenchENAS is easy to install and highly configurable and modular, which brings benefits in good usability and easy extensibility. This article conducts efficient comparison experiments on eight ENAS algorithms with high GPU utilization on this platform. The experiments validate that the fair comparison issue does exist in the current ENAS algorithms, and BenchENAS can alleviate this issue. A Website has been built to promote BenchENAS at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://benchenas.com</uri> , where interested researchers can obtain the source code and document of BenchENAS for free.