Methoden vergleichen

	Stochastic NSGA-II	Multi-Objective Genetic Algorithm (MOGA)
Fachgebiet	Simulation	Simulation
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	2001–2002	1984
Urheber	Deb, K. et al. (NSGA-II base); Hughes, E. J. and subsequent researchers for stochastic extensions	Schaffer, J. D. (early MOGA); Goldberg, D. E. (GA foundations)
Typ	Evolutionary multi-objective optimization under uncertainty	Population-based evolutionary optimizer
Wegweisende Quelle	Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.	Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley. ISBN: 9780201157673
Aliasnamen	S-NSGA-II, NSGA-II under Uncertainty, Stochastic Multi-Objective NSGA-II, Robust NSGA-II	MOGA, Multi-objective GA, Evolutionary multi-objective optimization, EMO
Verwandt	5	4
Zusammenfassung	Stochastic NSGA-II extends the NSGA-II evolutionary algorithm to handle objective functions that are noisy, uncertain, or probabilistic. By averaging or sampling stochastic objectives across multiple evaluations, it identifies Pareto-optimal solutions that are robust to uncertainty, making it suitable for engineering design, supply chain, and policy optimization problems where real-world variability matters.	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.