Módszerek összehasonlítása

	Evolúciós Stratégia (CMA-ES)	Genetikus algoritmus	Neuronális Architektúra Keresés
Tudományterület	Optimalizálás	Optimalizálás	Mélytanulás
Módszercsalád	Process / pipeline	Process / pipeline	Machine learning
Keletkezés éve	2001	1975	2017
Megalkotó	Nikolaus Hansen & Andreas Ostermeier	John Henry Holland	Zoph, B. & Le, Q.V.
Típus	Derivative-free continuous black-box optimizer	Population-based metaheuristic	Automated architecture optimization (deep learning)
Alapmű	Hansen, N. & Ostermeier, A. (2001). Completely Derandomized Self-Adaptation in Evolutionary Strategies. Evolutionary Computation, 9(2), 159-195.	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.	Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR.
Alternatív nevek	CMA-ES, Evolution Strategy, Evrimsel Strateji (CMA-ES), self-adapting evolution strategy	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search
Kapcsolódó	5	5	5
Összefoglaló	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.	A genetic algorithm (GA) is a population-based metaheuristic optimization method introduced by John Henry Holland (1975) that mimics the principles of natural selection. It maintains a population of candidate solutions and iteratively improves them through selection, crossover, and mutation operators, making it especially powerful on discontinuous, non-convex, and multi-modal search spaces where classical gradient-based methods fail.	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.