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| Algorithme Génétique Basé sur Agents× | Algorithme Génétique Multi-Objectif (MOGA)× | |
|---|---|---|
| Domaine | Simulation | Simulation |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1990s | 1984 |
| Auteur d'origine≠ | Adamidis, P. & Petridis, V. (early formal treatment); broader community development in 1990s | Schaffer, J. D. (early MOGA); Goldberg, D. E. (GA foundations) |
| Type≠ | Hybrid evolutionary-agent simulation | Population-based evolutionary optimizer |
| Source fondatrice≠ | 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 ↗ | Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley. ISBN: 9780201157673 |
| Alias | ABGA, Agent-Based GA, Multi-Agent Genetic Algorithm, Distributed Agent GA | MOGA, Multi-objective GA, Evolutionary multi-objective optimization, EMO |
| Apparentées≠ | 5 | 4 |
| Résumé≠ | 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 Multi-Objective Genetic Algorithm (MOGA) is an evolutionary computation method that evolves a population of candidate solutions toward a Pareto-optimal front, simultaneously optimizing two or more conflicting objective functions. It avoids collapsing trade-offs into a single score, instead producing a set of non-dominated solutions for the decision-maker to choose among. |
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