方法对比

	进化策略（CMA-ES）	基于代理的模型优化
领域	优化	优化
方法族	Process / pipeline	Process / pipeline
起源年份	2001	1989 (computer experiments formulation)
提出者	Nikolaus Hansen & Andreas Ostermeier	Sacks, Welch, Mitchell & Wynn (computer experiments framework, 1989); Kriging popularised by Matheron (1963)
类型	Derivative-free continuous black-box optimizer	Metamodel-assisted black-box optimization
开创性文献	Hansen, N. & Ostermeier, A. (2001). Completely Derandomized Self-Adaptation in Evolutionary Strategies. Evolutionary Computation, 9(2), 159-195.	Forrester, A., Sobester, A., & Keane, A. (2008). Engineering Design via Surrogate Modelling: A Practical Guide. Wiley.
别名	CMA-ES, Evolution Strategy, Evrimsel Strateji (CMA-ES), self-adapting evolution strategy	Vekil Model Tabanlı Optimizasyon (Surrogate-Based), metamodel-assisted optimization, surrogate modelling, emulator-based optimization
相关	5	5
摘要	CMA-ES, short for Covariance Matrix Adaptation Evolution Strategy, is a modern derivative-free optimizer for continuous black-box functions introduced by Hansen and Ostermeier in 2001. It maintains a population of candidate solutions drawn from a multivariate normal distribution and iteratively updates the distribution's mean, step size, and full covariance matrix to steer the search toward better regions of the parameter space. It has become the de-facto standard for continuous black-box optimization and is widely used in neural architecture search and reinforcement-learning policy optimization.	Surrogate-based optimization, formalized in the computer-experiments framework of Sacks et al. (1989) and popularized for engineering by Forrester et al. (2008), replaces a prohibitively expensive simulation or physical experiment with a cheap approximate model — called a surrogate or metamodel — and then optimizes that surrogate instead. The surrogate is typically a Kriging (Gaussian Process), Radial Basis Function, or polynomial response surface fitted to a small set of carefully chosen design evaluations and periodically updated as the search progresses.