Methoden vergleichen

	Agent-Based Genetic Algorithm	Agentenbasiertes Modellieren (ABM)
Fachgebiet	Simulation	Simulation
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1990s	1970s–1990s (formalized as a field)
Urheber	Adamidis, P. & Petridis, V. (early formal treatment); broader community development in 1990s	Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s)
Typ	Hybrid evolutionary-agent simulation	Computational simulation method
Wegweisende Quelle	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.	Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press.
Aliasnamen	ABGA, Agent-Based GA, Multi-Agent Genetic Algorithm, Distributed Agent GA	ABM, Ajan Tabanlı Modelleme (ABM), multi-agent simulation, individual-based modeling
Verwandt	5	5
Zusammenfassung	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.