Salīdzināt metodes

	Bajesiskais NSGA-II	Optimizācija ar Bajesas metodi
Nozare	Simulācija	Optimizācija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2002–2006	1975 (foundational); 2012 (ML standard)
Autors	Emmerich, M. T. M. et al. (surrogate-assisted MO-EA); Deb et al. (NSGA-II base)	Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012)
Tips	Surrogate-assisted multi-objective evolutionary algorithm	Sequential model-based black-box optimization
Pirmavots	Deb, K., Pratap, A., Agarwal, S., Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.	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	B-NSGA-II, Surrogate-Assisted NSGA-II, Gaussian Process NSGA-II, Bayesian Multi-Objective EA	Bayesçi Optimizasyon (Hyperparameter Tuning), surrogate-based optimization, sequential model-based optimization, SMBO
Saistītās	3	2
Kopsavilkums	Bayesian NSGA-II integrates Gaussian process surrogate models (Bayesian metamodels) into the NSGA-II evolutionary loop to solve expensive multi-objective optimization problems. By replacing costly true function evaluations with fast probabilistic predictions, it discovers high-quality Pareto-front approximations with far fewer real evaluations than standard NSGA-II.	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.