Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Агентно-ориентированный генетический алгоритм× | Генетический алгоритм× | |
|---|---|---|
| Область≠ | Имитационное моделирование | Оптимизация |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1990s | 1975 |
| Автор метода≠ | Adamidis, P. & Petridis, V. (early formal treatment); broader community development in 1990s | John Henry Holland |
| Тип≠ | Hybrid evolutionary-agent simulation | Population-based metaheuristic |
| Основополагающий источник≠ | Adamidis, P., & Petridis, V. (1996). Co-operating populations with different evolution behaviors. Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC 1996), 188-191. IEEE. link ↗ | Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. link ↗ |
| Другие названия≠ | ABGA, Agent-Based GA, Multi-Agent Genetic Algorithm, Distributed Agent GA | GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon |
| Связанные | 5 | 5 |
| Сводка≠ | An Agent-Based Genetic Algorithm (ABGA) partitions a genetic algorithm's population across a network of autonomous agents, each maintaining a local sub-population and evolving it independently. Agents periodically exchange individuals (migration) based on proximity or communication rules, enabling parallel exploration of the search space while preserving population diversity and avoiding premature convergence. | 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. |
| ScholarGateНабор данных ↗ |
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