方法对比
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| NEAT：拓扑增强神经进化× | 神经架构搜索× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2002 | 2017 |
| 提出者≠ | Kenneth Stanley & Risto Miikkulainen | Zoph, B. & Le, Q.V. |
| 类型≠ | Neuroevolutionary algorithm | Automated architecture optimization (deep learning) |
| 开创性文献≠ | Stanley, K. O., & Miikkulainen, R. (2002). Evolving neural networks through augmenting topologies. Evolutionary Computation, 10(2), 99–127. DOI ↗ | Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR. link ↗ |
| 别名 | Neuroevolution of Augmenting Topologies, Topology and Weight Evolving Artificial Neural Networks (variant), Evolving Neural Networks, Topoloji Artırımlı Nöroevrim | Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search |
| 相关≠ | 3 | 5 |
| 摘要≠ | NEAT is a genetic algorithm for evolving artificial neural networks introduced by Kenneth Stanley and Risto Miikkulainen in 2002. Unlike methods that evolve weights alone, NEAT simultaneously evolves both the topology (structure) and the connection weights of neural networks. It achieves this through a direct genome encoding with historical markings that enable meaningful crossover between networks of different structures, making it applicable to reinforcement learning, game playing, and control tasks without requiring a predefined architecture. | 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. |
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