Comparer des méthodes

	Algorithme Génétique Robuste	Algorithme génétique
Domaine	Simulation	Optimisation
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)	John Henry Holland
Type	Metaheuristic evolutionary optimizer with robustness mechanism	Population-based 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.
Alias	RGA, Robust GA, Uncertainty-Aware Genetic Algorithm, Noise-Tolerant Genetic Algorithm	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon
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.	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.