Porównaj metody
Przeglądaj wybrane metody obok siebie; wiersze, które się różnią, są wyróżnione.
| Bayesowski algorytm genetyczny× | Algorytm genetyczny stochastyczny× | |
|---|---|---|
| Dziedzina | Symulacja | Symulacja |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1999 | 1975 |
| Twórca≠ | Pelikan, M., Goldberg, D. E., & Cantu-Paz, E. | Holland, J. H. |
| Typ≠ | Evolutionary metaheuristic with Bayesian probabilistic model | Stochastic evolutionary metaheuristic |
| Źródło pierwotne≠ | 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 ↗ | Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor. ISBN: 978-0262581110 |
| Inne nazwy | BGA, Bayesian-guided GA, Probabilistic GA, EDA-GA | SGA, Canonical Genetic Algorithm, Simple Genetic Algorithm, Evolutionary Algorithm |
| Pokrewne | 5 | 5 |
| Podsumowanie≠ | 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. | The Stochastic Genetic Algorithm (SGA) is a population-based metaheuristic that mimics biological evolution — selection, crossover, and mutation — to search for near-optimal solutions in complex, nonlinear, or combinatorial spaces. Its randomized operators make it robust to local optima and broadly applicable across engineering, scheduling, machine learning, and operations research. |
| ScholarGateZbiór danych ↗ |
|
|