Сравнение методов

	Генетический алгоритм	Оптимизатор "Серый волк"
Область	Оптимизация	Оптимизация
Семейство	Process / pipeline	Process / pipeline
Год появления	1975	2014
Автор метода	John Henry Holland	Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis
Тип	Population-based metaheuristic	Swarm-intelligence metaheuristic
Основополагающий источник	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.	Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61.
Другие названия	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO)
Связанные	5	5
Сводка	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.	The Grey Wolf Optimizer (GWO) is a swarm-intelligence metaheuristic introduced by Mirjalili, Mirjalili, and Lewis in 2014 that models the social hierarchy and cooperative hunting behaviour of grey wolves. A population of candidate solutions is divided into four leadership ranks — alpha, beta, delta, and omega — and the three best solutions at each iteration guide the entire swarm toward increasingly better regions of the search space.