Сравнение методов

	NEAT: Нейроэволюция дополняющих топологий	Генетический алгоритм	Нейросетевой поиск архитектур
Область	Глубокое обучение	Оптимизация	Глубокое обучение
Семейство	Machine learning	Process / pipeline	Machine learning
Год появления	2002	1975	2017
Автор метода	Kenneth Stanley & Risto Miikkulainen	John Henry Holland	Zoph, B. & Le, Q.V.
Тип	Neuroevolutionary algorithm	Population-based metaheuristic	Automated architecture optimization (deep learning)
Основополагающий источник	Stanley, K. O., & Miikkulainen, R. (2002). Evolving neural networks through augmenting topologies. Evolutionary Computation, 10(2), 99–127.	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.
Другие названия	Neuroevolution of Augmenting Topologies, Topology and Weight Evolving Artificial Neural Networks (variant), Evolving Neural Networks, Topoloji Artırımlı Nöroevrim	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search
Связанные	3	5	5
Сводка	NEAT is a genetic algorithm for evolving artificial neural networks introduced by Kenneth Stanley and Risto Miikkulainen in 2002. Unlike methods that evolve weights alone, NEAT simultaneously evolves both the topology (structure) and the connection weights of neural networks. It achieves this through a direct genome encoding with historical markings that enable meaningful crossover between networks of different structures, making it applicable to reinforcement learning, game playing, and control tasks without requiring a predefined architecture.	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.