Comparer des méthodes

	Algorithme Génétique Bayésien	Algorithme génétique
Domaine	Simulation	Optimisation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1999	1975
Auteur d'origine	Pelikan, M., Goldberg, D. E., & Cantu-Paz, E.	John Henry Holland
Type	Evolutionary metaheuristic with Bayesian probabilistic model	Population-based 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.
Alias	BGA, Bayesian-guided GA, Probabilistic GA, EDA-GA	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon
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.	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.