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| Bayesian Simulated Annealing× | Algoritmo Genetico Bayesiano× | |
|---|---|---|
| Campo | Simulazione | Simulazione |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1984 | 1999 |
| Ideatore≠ | Geman, S. & Geman, D. (Bayesian framing); Kirkpatrick, S. et al. (SA foundation) | Pelikan, M., Goldberg, D. E., & Cantu-Paz, E. |
| Tipo≠ | Probabilistic metaheuristic with Bayesian inference | Evolutionary metaheuristic with Bayesian probabilistic model |
| Fonte seminale≠ | Kirkpatrick, 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 ↗ |
| Alias | BSA, Bayesian SA, Bayesian Stochastic Annealing, Bayesian Thermodynamic Optimization | BGA, Bayesian-guided GA, Probabilistic GA, EDA-GA |
| Correlati | 5 | 5 |
| Sintesi≠ | Bayesian 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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