论文标题
神经架构搜索的差分进化
Differential Evolution for Neural Architecture Search
论文作者
论文摘要
神经体系结构搜索（NAS）方法依靠搜索策略来确定哪些体系结构评估下一步以及评估其性能的绩效估算策略（例如，使用完整的评估，多保真评估或单发模型）。在本文中，我们专注于搜索策略。我们向NAS社区介绍了差异进化的简单而强大的进化算法。使用最简单的绩效评估策略，我们将此搜索策略与正则化进化和贝叶斯优化进行了全面比较，并证明它基于NAS-Bench-101，NAS-Bench-1Shot1，NAS-Bench-1Sshot1，NAS-Bench-201，NAS-BENCH-2010和NAS-HPO BENCH，它可以改善和更强大的结果。
Neural architecture search (NAS) methods rely on a search strategy for deciding which architectures to evaluate next and a performance estimation strategy for assessing their performance (e.g., using full evaluations, multi-fidelity evaluations, or the one-shot model). In this paper, we focus on the search strategy. We introduce the simple yet powerful evolutionary algorithm of differential evolution to the NAS community. Using the simplest performance evaluation strategy of full evaluations, we comprehensively compare this search strategy to regularized evolution and Bayesian optimization and demonstrate that it yields improved and more robust results for 13 tabular NAS benchmarks based on NAS-Bench-101, NAS-Bench-1Shot1, NAS-Bench-201 and NAS-HPO bench.
