Salīdzināt metodes

	Bayesiešuņetiskais algoritms	Optimizācija ar Bajesas metodi
Nozare	Simulācija	Optimizācija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1999	1975 (foundational); 2012 (ML standard)
Autors	Pelikan, M., Goldberg, D. E., & Cantu-Paz, E.	Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012)
Tips	Evolutionary metaheuristic with Bayesian probabilistic model	Sequential model-based black-box optimization
Pirmavots	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.	Snoek, J., Larochelle, H., & Adams, R.P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems (NeurIPS), 25.
Citi nosaukumi	BGA, Bayesian-guided GA, Probabilistic GA, EDA-GA	Bayesçi Optimizasyon (Hyperparameter Tuning), surrogate-based optimization, sequential model-based optimization, SMBO
Saistītās	5	2
Kopsavilkums	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.	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.