Comparer des méthodes

	Stratégie d'évolution (CMA-ES)	Recherche d'architecture neuronale
Domaine	Optimisation	Apprentissage profond
Famille	Process / pipeline	Machine learning
Année d'origine	2001	2017
Auteur d'origine	Nikolaus Hansen & Andreas Ostermeier	Zoph, B. & Le, Q.V.
Type	Derivative-free continuous black-box optimizer	Automated architecture optimization (deep learning)
Source fondatrice	Hansen, N. & Ostermeier, A. (2001). Completely Derandomized Self-Adaptation in Evolutionary Strategies. Evolutionary Computation, 9(2), 159-195.	Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR.
Alias	CMA-ES, Evolution Strategy, Evrimsel Strateji (CMA-ES), self-adapting evolution strategy	Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search
Apparentées	5	5
Résumé	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.	Neural Architecture Search (NAS), introduced by Zoph and Le in 2017, automatically optimizes architectural decisions such as a network's depth, width, and connection structure instead of hand-designing them. Leading methods in the field include DARTS, ENAS, and Once-for-All.