方法对比
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| 随机遗传算法× | 模拟退火× | |
|---|---|---|
| 领域≠ | 仿真 | 优化 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1975 | 1983 |
| 提出者≠ | Holland, J. H. | — |
| 类型≠ | Stochastic evolutionary metaheuristic | Probabilistic metaheuristic / local search |
| 开创性文献≠ | Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor. ISBN: 978-0262581110 | Kirkpatrick, S., Gelatt, C.D. & Vecchi, M.P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. DOI ↗ |
| 别名≠ | SGA, Canonical Genetic Algorithm, Simple Genetic Algorithm, Evolutionary Algorithm | Benzetimli Tavlama (Simulated Annealing), SA, probabilistic local search |
| 相关 | 5 | 5 |
| 摘要≠ | 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. | Simulated annealing is a probabilistic local-search metaheuristic introduced by Kirkpatrick, Gelatt, and Vecchi in 1983. It models the physical annealing process in metallurgy — where a material is heated and then slowly cooled to reach a low-energy crystalline state — and uses this analogy to escape local optima in combinatorial and continuous optimization problems. |
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