Comparer des méthodes

	Algorithme Génétique Robuste	Algorithme Génétique Stochastique
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	2005 (systematic survey); earlier applications from late 1990s	1975
Auteur d'origine	Jin, Y. and Branke, J. (systematic formalization); roots in Holland (1975)	Holland, J. H.
Type	Metaheuristic evolutionary optimizer with robustness mechanism	Stochastic evolutionary metaheuristic
Source fondatrice	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
Alias	RGA, Robust GA, Uncertainty-Aware Genetic Algorithm, Noise-Tolerant Genetic Algorithm	SGA, Canonical Genetic Algorithm, Simple Genetic Algorithm, Evolutionary Algorithm
Apparentées	6	5
Résumé	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.