Vertaile menetelmiä

	Robust Optimization	Kovarianssimatriisin adaptaatio (CMA-ES)
Tieteenala	Optimointi	Optimointi
Menetelmäperhe	Process / pipeline	Process / pipeline
Syntyvuosi	1970s theoretical roots; modern tractable form from late 1990s–2004	2001
Kehittäjä	Ben-Tal, El Ghaoui & Nemirovski (seminal book, 2009); Bertsimas & Sim (tractable polyhedral formulation, 2004)	Nikolaus Hansen & Andreas Ostermeier
Tyyppi	Mathematical programming framework	Derivative-free continuous black-box optimizer
Alkuperäislähde	Ben-Tal, A., El Ghaoui, L. & Nemirovski, A. (2009). Robust Optimization. Princeton University Press. ISBN: 9780691143682	Hansen, N. & Ostermeier, A. (2001). Completely Derandomized Self-Adaptation in Evolutionary Strategies. Evolutionary Computation, 9(2), 159-195.
Rinnakkaisnimet	minimax optimization, worst-case optimization, Gürbüz Optimizasyon (Robust Optimization)	CMA-ES, Evolution Strategy, Evrimsel Strateji (CMA-ES), self-adapting evolution strategy
Liittyvät	5	5
Tiivistelmä	Robust optimization is a mathematical programming framework, formalised by Ben-Tal and Nemirovski in the late 1990s and made broadly tractable by Bertsimas and Sim (2004), that finds decisions guaranteed to perform acceptably under every scenario within a predefined uncertainty set — rather than assuming parameter values are known exactly. Instead of optimising for a single expected outcome, it minimises the worst-case objective across all plausible realisations of uncertain data.	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.