Сравнение на методи

	NEAT: Невроеволюция на разширяващи се топологии	Генетичен алгоритъм
Област	Дълбоко обучение	Оптимизация
Семейство	Machine learning	Process / pipeline
Година на възникване	2002	1975
Създател	Kenneth Stanley & Risto Miikkulainen	John Henry Holland
Тип	Neuroevolutionary algorithm	Population-based metaheuristic
Основополагащ източник	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.
Други 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
Свързани	3	5
Резюме	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.