विधियों की तुलना करें

	बेयसियन बहु-उद्देश्यीय अनुकूलन	बेयसियन ऑप्टिमाइजेशन
क्षेत्र	अनुकरण	अनुकूलन
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	2006-2016	1975 (foundational); 2012 (ML standard)
प्रवर्तक	Emmerich, M.; Svenson, J.; and related Gaussian process optimization community	Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012)
प्रकार	Surrogate-model-assisted multi-objective optimizer	Sequential model-based black-box optimization
मौलिक स्रोत	Svenson, J., Santner, T. (2016). Multiobjective optimization of expensive-to-evaluate deterministic computer simulator models. Computational Statistics & Data Analysis, 94, 250-264.	Snoek, J., Larochelle, H., & Adams, R.P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems (NeurIPS), 25.
उपनाम	BMOO, Bayesian MOO, Multi-objective Bayesian optimization, MOBO	Bayesçi Optimizasyon (Hyperparameter Tuning), surrogate-based optimization, sequential model-based optimization, SMBO
संबंधित	3	2
सारांश	Bayesian Multi-Objective Optimization (BMOO/MOBO) uses Gaussian process surrogate models to approximate multiple expensive objective functions and guides the search toward the Pareto frontier with minimal real evaluations. By quantifying prediction uncertainty at each candidate point, it balances exploration of unknown regions against exploitation of promising solutions, making it especially powerful when each function evaluation is computationally or experimentally costly.	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.