Сравнение методов

	Устойчивый генетический алгоритм	Стохастический генетический алгоритм
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	2005 (systematic survey); earlier applications from late 1990s	1975
Автор метода	Jin, Y. and Branke, J. (systematic formalization); roots in Holland (1975)	Holland, J. H.
Тип	Metaheuristic evolutionary optimizer with robustness mechanism	Stochastic evolutionary metaheuristic
Основополагающий источник	Jin, Y., Branke, J. (2005). Evolutionary optimization in uncertain environments — a survey. IEEE Transactions on Evolutionary Computation, 9(3), 303–317.	Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor. ISBN: 978-0262581110
Другие названия	RGA, Robust GA, Uncertainty-Aware Genetic Algorithm, Noise-Tolerant Genetic Algorithm	SGA, Canonical Genetic Algorithm, Simple Genetic Algorithm, Evolutionary Algorithm
Связанные	6	5
Сводка	The Robust Genetic Algorithm (RGA) extends standard genetic algorithms to find solutions that perform well not only at the nominal design point but also when subjected to uncertainty in decision variables, parameters, or fitness evaluations. By incorporating explicit robustness measures into selection pressure, RGA balances optimality against sensitivity to perturbation, making it suitable for engineering design, scheduling, and policy optimization under real-world variability.	The Stochastic Genetic Algorithm (SGA) is a population-based metaheuristic that mimics biological evolution — selection, crossover, and mutation — to search for near-optimal solutions in complex, nonlinear, or combinatorial spaces. Its randomized operators make it robust to local optima and broadly applicable across engineering, scheduling, machine learning, and operations research.