Process / pipeline
进化策略（CMA-ES）——协方差矩阵自适应
CMA-ES，即协方差矩阵自适应进化策略，是由Hansen和Ostermeier于2001年提出的一种现代的、不依赖梯度（derivative-free）的连续黑盒函数优化器。它维护一个从多元正态分布中抽取的候选解种群，并迭代地更新分布的均值、步长和完整的协方差矩阵，以引导搜索趋向参数空间中的更优区域。它已成为连续黑盒优化的事实标准，并广泛应用于神经架构搜索和强化学习策略优化。
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来源
- Hansen, N. & Ostermeier, A. (2001). Completely Derandomized Self-Adaptation in Evolutionary Strategies. Evolutionary Computation, 9(2), 159-195. DOI: 10.1162/106365601750190398 ↗
- Hansen, N. (2016). The CMA Evolution Strategy: A Tutorial. arXiv:1604.00772. link ↗
如何引用本页
ScholarGate. (2026, June 1). Covariance Matrix Adaptation Evolution Strategy (CMA-ES). ScholarGate. https://scholargate.app/zh/optimization/evolutionary-strategy
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