Comparer des méthodes

	Algorithme Génétique Déterministe	Algorithme Génétique Multi-Objectif (MOGA)
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1975–1989	1984
Auteur d'origine	Goldberg, D. E.; Holland, J. H.	Schaffer, J. D. (early MOGA); Goldberg, D. E. (GA foundations)
Type	Deterministic evolutionary optimization	Population-based evolutionary optimizer
Source fondatrice	Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA. ISBN: 9780201157673	Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley. ISBN: 9780201157673
Alias	DGA, Deterministic EA, Deterministic Evolutionary Algorithm, Deterministic Selection GA	MOGA, Multi-objective GA, Evolutionary multi-objective optimization, EMO
Apparentées	5	4
Résumé	A Deterministic Genetic Algorithm (DGA) applies the structural framework of evolutionary computation — population, selection, crossover, and replacement — using entirely deterministic operators and fixed decision rules instead of stochastic sampling. By eliminating randomness, the algorithm becomes fully reproducible: running it twice on the same problem yields identical solutions, making it tractable for rigorous benchmarking, reproducibility studies, and systems where stochasticity is undesirable.	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.