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。
| 算術最適化アルゴリズム× | 遺伝的アルゴリズム× | |
|---|---|---|
| 分野 | 最適化 | 最適化 |
| 系統≠ | Machine learning | Process / pipeline |
| 提唱年≠ | 2020 | 1975 |
| 提唱者≠ | Laith Abualigah | John Henry Holland |
| 種類≠ | Mathematical metaheuristic algorithm | Population-based metaheuristic |
| 原典≠ | Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness, M. A., & Gandomi, A. H. (2021). Arithmetic optimization algorithm: A new metaheuristic algorithm for solving optimization problems. Applied Mathematics and Computation, 392, 125450. link ↗ | Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. link ↗ |
| 別名≠ | AOA | GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon |
| 関連 | 5 | 5 |
| 概要≠ | The Arithmetic Optimization Algorithm (AOA) is a metaheuristic optimization approach introduced by Abualigah et al. in 2020 that leverages mathematical operators (multiplication, division, addition, subtraction) as the inspiration for search strategies. Unlike nature-inspired algorithms, AOA uses the inherent properties of arithmetic operations to balance exploration and exploitation, making it particularly effective for mathematical optimization problems. | 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. |
| ScholarGateデータセット ↗ |
|
|