Comparar métodos

	Bayesian Tabu Search	Algoritmo Genético Bayesiano
Área	Simulação	Simulação
Família	Process / pipeline	Process / pipeline
Ano de origem	1989 (tabu search); hybrid formulations ~2005–2015	1999
Autor original	Glover, F. (tabu search); Bayesian integration developed by multiple researchers in the 2000s–2010s	Pelikan, M., Goldberg, D. E., & Cantu-Paz, E.
Tipo	Hybrid metaheuristic — memory-based local search with Bayesian probabilistic guidance	Evolutionary metaheuristic with Bayesian probabilistic model
Fonte seminal	Glover, F. (1989). Tabu search — Part I. ORSA Journal on Computing, 1(3), 190–206.	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.
Outros nomes	BTS, Bayesian-guided tabu search, probabilistic tabu search, Bayes-TS	BGA, Bayesian-guided GA, Probabilistic GA, EDA-GA
Relacionados	6	5
Resumo	Bayesian Tabu Search (BTS) is a hybrid metaheuristic that couples the memory-based forbidden-move mechanism of classic Tabu Search with a Bayesian probabilistic model. The Bayesian component learns from past evaluations to score candidate moves, focusing the search on promising regions while the tabu list prevents cycling. This combination reduces wasted function evaluations in expensive combinatorial and continuous 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.