Comparer des méthodes

	Algorithme génétique	Recherche d'architecture neuronale
Domaine	Optimisation	Apprentissage profond
Famille	Process / pipeline	Machine learning
Année d'origine	1975	2017
Auteur d'origine	John Henry Holland	Zoph, B. & Le, Q.V.
Type	Population-based metaheuristic	Automated architecture optimization (deep learning)
Source fondatrice	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.
Alias	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search
Apparentées	5	5
Résumé	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.