方法对比
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| 遗传算法× | 神经架构搜索× | |
|---|---|---|
| 领域≠ | 优化 | 深度学习 |
| 方法族≠ | Process / pipeline | Machine learning |
| 起源年份≠ | 1975 | 2017 |
| 提出者≠ | John Henry Holland | Zoph, B. & Le, Q.V. |
| 类型≠ | Population-based metaheuristic | Automated architecture optimization (deep learning) |
| 开创性文献≠ | Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. link ↗ | Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR. link ↗ |
| 别名≠ | GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon | Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search |
| 相关 | 5 | 5 |
| 摘要≠ | A genetic algorithm (GA) is a population-based metaheuristic optimization method introduced by John Henry Holland (1975) that mimics the principles of natural selection. It maintains a population of candidate solutions and iteratively improves them through selection, crossover, and mutation operators, making it especially powerful on discontinuous, non-convex, and multi-modal search spaces where classical gradient-based methods fail. | Neural Architecture Search (NAS), introduced by Zoph and Le in 2017, automatically optimizes architectural decisions such as a network's depth, width, and connection structure instead of hand-designing them. Leading methods in the field include DARTS, ENAS, and Once-for-All. |
| ScholarGate数据集 ↗ |
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