Comparar métodos

	Otimização Estocástica	Estratégia Evolutiva (CMA-ES)
Área	Otimização	Otimização
Família	Process / pipeline	Process / pipeline
Ano de origem	1951 (SGD); 2014 (Adam)	2001
Autor original	—	Nikolaus Hansen & Andreas Ostermeier
Tipo	Gradient-based iterative optimization	Derivative-free continuous black-box optimizer
Fonte seminal	Robbins, H. & Monro, S. (1951). A Stochastic Approximation Method. Annals of Mathematical Statistics, 22(3), 400-407.	Hansen, N. & Ostermeier, A. (2001). Completely Derandomized Self-Adaptation in Evolutionary Strategies. Evolutionary Computation, 9(2), 159-195.
Outros nomes	Stokastik Optimizasyon (SGD & Varyantları), stochastic gradient descent, SGD, Adam	CMA-ES, Evolution Strategy, Evrimsel Strateji (CMA-ES), self-adapting evolution strategy
Relacionados	3	5
Resumo	Stochastic optimization is a family of iterative methods that minimize an objective function by computing gradients on randomly sampled subsets of data — mini-batches — rather than on the entire dataset at once. Pioneered by Robbins and Monro in 1951 as stochastic approximation, the approach became the standard engine for training large-scale machine-learning models through variants such as SGD with momentum, AdaGrad, RMSProp, and Adam.	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.