Comparer des méthodes

	Recuit simulé bayésien	Algorithme génétique
Domaine	Simulation	Optimisation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1984	1975
Auteur d'origine	Geman, S. & Geman, D. (Bayesian framing); Kirkpatrick, S. et al. (SA foundation)	John Henry Holland
Type	Probabilistic metaheuristic with Bayesian inference	Population-based metaheuristic
Source fondatrice	Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680.	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.
Alias	BSA, Bayesian SA, Bayesian Stochastic Annealing, Bayesian Thermodynamic Optimization	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon
Apparentées	5	5
Résumé	Bayesian Simulated Annealing (BSA) integrates Bayesian prior knowledge about the objective landscape into the simulated annealing search process. By encoding beliefs about promising regions as prior distributions and updating them as the search progresses, BSA focuses computational effort on high-probability areas of the solution space, accelerating convergence and improving solution quality compared to uninformed SA.	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.