Bandingkan kaedah

	Evolutionary Strategy (CMA-ES)	Pencarian Seni Bina Neural
Bidang	Pengoptimuman	Pembelajaran Mendalam
Keluarga	Process / pipeline	Machine learning
Tahun asal	2001	2017
Pengasas	Nikolaus Hansen & Andreas Ostermeier	Zoph, B. & Le, Q.V.
Jenis	Derivative-free continuous black-box optimizer	Automated architecture optimization (deep learning)
Sumber perintis	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
Berkaitan	5	5
Ringkasan	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.