Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Детерминированный генетический алгоритм× | Стохастический генетический алгоритм× | |
|---|---|---|
| Область | Имитационное моделирование | Имитационное моделирование |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1975–1989 | 1975 |
| Автор метода≠ | Goldberg, D. E.; Holland, J. H. | Holland, J. H. |
| Тип≠ | Deterministic evolutionary optimization | Stochastic evolutionary metaheuristic |
| Основополагающий источник≠ | 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 |
| Другие названия | DGA, Deterministic EA, Deterministic Evolutionary Algorithm, Deterministic Selection GA | SGA, Canonical Genetic Algorithm, Simple Genetic Algorithm, Evolutionary Algorithm |
| Связанные | 5 | 5 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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