Linganisha mbinu

	Algorithimu ya Kijeni ya Kibayesiani	Uboreshaji wa Malengo Mengi wa Kibayesia
Nyanja	Uigaji	Uigaji
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1999	2006-2016
Mwanzilishi	Pelikan, M., Goldberg, D. E., & Cantu-Paz, E.	Emmerich, M.; Svenson, J.; and related Gaussian process optimization community
Aina	Evolutionary metaheuristic with Bayesian probabilistic model	Surrogate-model-assisted multi-objective optimizer
Chanzo asilia	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.	Svenson, J., Santner, T. (2016). Multiobjective optimization of expensive-to-evaluate deterministic computer simulator models. Computational Statistics & Data Analysis, 94, 250-264.
Majina mbadala	BGA, Bayesian-guided GA, Probabilistic GA, EDA-GA	BMOO, Bayesian MOO, Multi-objective Bayesian optimization, MOBO
Zinazohusiana	5	3
Muhtasari	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 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.