Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Agent-based genetic algorithm× | Genetisk algoritm× | |
|---|---|---|
| Ämnesområde≠ | Simulering | Optimering |
| Familj | Process / pipeline | Process / pipeline |
| Ursprungsår≠ | 1990s | 1975 |
| Upphovsperson≠ | Adamidis, P. & Petridis, V. (early formal treatment); broader community development in 1990s | John Henry Holland |
| Typ≠ | Hybrid evolutionary-agent simulation | Population-based metaheuristic |
| Ursprungskälla≠ | 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 ↗ |
| Alias≠ | ABGA, Agent-Based GA, Multi-Agent Genetic Algorithm, Distributed Agent GA | GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon |
| Närliggande | 5 | 5 |
| Sammanfattning≠ | 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. |
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