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| Thuật toán di truyền mạnh mẽ× | Thuật toán Di truyền Ngẫu nhiên× | |
|---|---|---|
| Lĩnh vực | Mô phỏng | Mô phỏng |
| Họ | Process / pipeline | Process / pipeline |
| Năm ra đời≠ | 2005 (systematic survey); earlier applications from late 1990s | 1975 |
| Người khởi xướng≠ | Jin, Y. and Branke, J. (systematic formalization); roots in Holland (1975) | Holland, J. H. |
| Loại≠ | Metaheuristic evolutionary optimizer with robustness mechanism | Stochastic evolutionary metaheuristic |
| Công trình gốc≠ | Jin, Y., Branke, J. (2005). Evolutionary optimization in uncertain environments — a survey. IEEE Transactions on Evolutionary Computation, 9(3), 303–317. DOI ↗ | Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor. ISBN: 978-0262581110 |
| Tên gọi khác | RGA, Robust GA, Uncertainty-Aware Genetic Algorithm, Noise-Tolerant Genetic Algorithm | SGA, Canonical Genetic Algorithm, Simple Genetic Algorithm, Evolutionary Algorithm |
| Liên quan≠ | 6 | 5 |
| Tóm tắt≠ | The Robust Genetic Algorithm (RGA) extends standard genetic algorithms to find solutions that perform well not only at the nominal design point but also when subjected to uncertainty in decision variables, parameters, or fitness evaluations. By incorporating explicit robustness measures into selection pressure, RGA balances optimality against sensitivity to perturbation, making it suitable for engineering design, scheduling, and policy optimization under real-world variability. | 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. |
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