Comparer des méthodes

	Algorithme Génétique Bayésien	Algorithme Génétique Stochastique
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1999	1975
Auteur d'origine	Pelikan, M., Goldberg, D. E., & Cantu-Paz, E.	Holland, J. H.
Type	Evolutionary metaheuristic with Bayesian probabilistic model	Stochastic evolutionary metaheuristic
Source fondatrice	Pelikan, M., Goldberg, D. E., & Cantu-Paz, E. (1999). BOA: The Bayesian optimization algorithm. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-1999), pp. 525–532. Morgan Kaufmann.	Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor. ISBN: 978-0262581110
Alias	BGA, Bayesian-guided GA, Probabilistic GA, EDA-GA	SGA, Canonical Genetic Algorithm, Simple Genetic Algorithm, Evolutionary Algorithm
Apparentées	5	5
Résumé	A Bayesian Genetic Algorithm (BGA) replaces traditional crossover and mutation operators with a probabilistic Bayesian network learned from selected high-fitness individuals. At each generation the algorithm builds a graphical model of promising solution structure, then samples new offspring from that model, enabling the search to capture and exploit variable dependencies that standard GAs miss.	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.