Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Deterministický genetický algoritmus× | Stochastický genetický algoritmus× | |
|---|---|---|
| Obor | Simulace | Simulace |
| Rodina | Process / pipeline | Process / pipeline |
| Rok vzniku≠ | 1975–1989 | 1975 |
| Tvůrce≠ | Goldberg, D. E.; Holland, J. H. | Holland, J. H. |
| Typ≠ | Deterministic evolutionary optimization | Stochastic evolutionary metaheuristic |
| Původní zdroj≠ | Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA. ISBN: 9780201157673 | Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor. ISBN: 978-0262581110 |
| Další názvy | DGA, Deterministic EA, Deterministic Evolutionary Algorithm, Deterministic Selection GA | SGA, Canonical Genetic Algorithm, Simple Genetic Algorithm, Evolutionary Algorithm |
| Příbuzné | 5 | 5 |
| Shrnutí≠ | A Deterministic Genetic Algorithm (DGA) applies the structural framework of evolutionary computation — population, selection, crossover, and replacement — using entirely deterministic operators and fixed decision rules instead of stochastic sampling. By eliminating randomness, the algorithm becomes fully reproducible: running it twice on the same problem yields identical solutions, making it tractable for rigorous benchmarking, reproducibility studies, and systems where stochasticity is undesirable. | 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. |
| ScholarGateDatová sada ↗ |
|
|