手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| NEAT: ニューロエボリューション・オブ・オーグメンティング・トポロジーズ× | ニューラルアーキテクチャ探索× | |
|---|---|---|
| 分野 | 深層学習 | 深層学習 |
| 系統 | Machine learning | Machine learning |
| 提唱年≠ | 2002 | 2017 |
| 提唱者≠ | Kenneth Stanley & Risto Miikkulainen | Zoph, B. & Le, Q.V. |
| 種類≠ | Neuroevolutionary algorithm | Automated architecture optimization (deep learning) |
| 原典≠ | 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 ↗ |
| 別名 | 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 |
| 関連≠ | 3 | 5 |
| 概要≠ | 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. |
| ScholarGateデータセット ↗ |
|
|