Compară metode

	Simulated Annealing Bayesian	Optimizare Bayesiană
Domeniu	Simulare	Optimizare
Familie	Process / pipeline	Process / pipeline
Anul apariției	1984	1975 (foundational); 2012 (ML standard)
Autorul original	Geman, S. & Geman, D. (Bayesian framing); Kirkpatrick, S. et al. (SA foundation)	Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012)
Tip	Probabilistic metaheuristic with Bayesian inference	Sequential model-based black-box optimization
Sursa seminală	Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680.	Snoek, J., Larochelle, H., & Adams, R.P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems (NeurIPS), 25.
Denumiri alternative	BSA, Bayesian SA, Bayesian Stochastic Annealing, Bayesian Thermodynamic Optimization	Bayesçi Optimizasyon (Hyperparameter Tuning), surrogate-based optimization, sequential model-based optimization, SMBO
Înrudite	5	2
Rezumat	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.	Bayesian Optimization is a sequential, model-based strategy for finding the optimum of expensive black-box functions with as few evaluations as possible. Rooted in the work of Mockus (1975) and brought to mainstream machine-learning practice by Snoek, Larochelle, and Adams (2012), it fits a probabilistic surrogate model — typically a Gaussian Process — to past observations and uses an acquisition function to decide where to probe next, balancing exploration of unknown regions with exploitation of promising ones.