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Simulated Annealing Bayesiana×Algorisme Genètic Bayesà×
CampSimulacióSimulació
FamíliaProcess / pipelineProcess / pipeline
Any d'origen19841999
Autor originalGeman, S. & Geman, D. (Bayesian framing); Kirkpatrick, S. et al. (SA foundation)Pelikan, M., Goldberg, D. E., & Cantu-Paz, E.
TipusProbabilistic metaheuristic with Bayesian inferenceEvolutionary metaheuristic with Bayesian probabilistic model
Font seminalKirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680. DOI ↗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. link ↗
ÀliesBSA, Bayesian SA, Bayesian Stochastic Annealing, Bayesian Thermodynamic OptimizationBGA, Bayesian-guided GA, Probabilistic GA, EDA-GA
Relacionats55
ResumBayesian Simulated Annealing (BSA) integrates Bayesian prior knowledge about the objective landscape into the simulated annealing search process. By encoding beliefs about promising regions as prior distributions and updating them as the search progresses, BSA focuses computational effort on high-probability areas of the solution space, accelerating convergence and improving solution quality compared to uninformed SA.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.
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ScholarGateCompara mètodes: Bayesian Simulated Annealing · Bayesian Genetic Algorithm. Recuperat el 2026-06-15 de https://scholargate.app/ca/compare