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| 에이전트 기반 유전 알고리즘× | 행위자 기반 모델링 (ABM)× | |
|---|---|---|
| 분야 | 시뮬레이션 | 시뮬레이션 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1990s | 1970s–1990s (formalized as a field) |
| 창시자≠ | Adamidis, P. & Petridis, V. (early formal treatment); broader community development in 1990s | Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s) |
| 유형≠ | Hybrid evolutionary-agent simulation | Computational simulation method |
| 원전≠ | 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 ↗ | Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press. DOI ↗ |
| 별칭 | ABGA, Agent-Based GA, Multi-Agent Genetic Algorithm, Distributed Agent GA | ABM, Ajan Tabanlı Modelleme (ABM), multi-agent simulation, individual-based modeling |
| 관련 | 5 | 5 |
| 요약≠ | 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. | Agent-based modeling (ABM) is a computational simulation method, formalized through the work of Thomas Schelling and Robert Axelrod in the 1970s–1990s, that simulates the behavior of complex systems by specifying and running autonomous agents — individuals, firms, cells, or any bounded entity — whose local interactions with each other and with their environment collectively produce global, system-level patterns that could not be predicted from any single agent's rules alone. |
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