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	Optymalizacja Bayesowska z wykorzystaniem kolonii mrówek	Bayesowski algorytm genetyczny
Dziedzina	Symulacja	Symulacja
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	1996 (ACO); Bayesian variant: 2000s	1999
Twórca	Dorigo, M. et al. (ACO); Bayesian extensions by multiple researchers in the 2000s–2010s	Pelikan, M., Goldberg, D. E., & Cantu-Paz, E.
Typ	Metaheuristic with Bayesian probabilistic learning	Evolutionary metaheuristic with Bayesian probabilistic model
Źródło pierwotne	Dorigo, M., Maniezzo, V., Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 26(1), 29–41.	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.
Inne nazwy	BACO, Bayesian ACO, Bayesian-guided ACO, Probabilistic ACO	BGA, Bayesian-guided GA, Probabilistic GA, EDA-GA
Pokrewne	5	5
Podsumowanie	Bayesian Ant Colony Optimization (BACO) is a hybrid metaheuristic that embeds Bayesian inference into the Ant Colony Optimization framework. By treating pheromone intensities or algorithm parameters as probability distributions updated with collected evidence, BACO improves convergence reliability and robustness compared to classical ACO on noisy or uncertain combinatorial optimization problems.	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.