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| Multi-Objective Genetic Algorithm (MOGA)× | Mehrzieloptimierung – Gleichzeitige Optimierung widerstreitender Ziele× | |
|---|---|---|
| Fachgebiet | Simulation | Simulation |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1984 | 1896 (concept); 1989–2002 (evolutionary algorithms era) |
| Urheber≠ | Schaffer, J. D. (early MOGA); Goldberg, D. E. (GA foundations) | Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al. |
| Typ≠ | Population-based evolutionary optimizer | Optimization framework |
| Wegweisende Quelle≠ | Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley. ISBN: 9780201157673 | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 |
| Aliasnamen | MOGA, Multi-objective GA, Evolutionary multi-objective optimization, EMO | MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization |
| Verwandt≠ | 4 | 3 |
| Zusammenfassung≠ | 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. | Multi-Objective Optimization (MOO) is a mathematical and computational framework for finding solutions that simultaneously optimize two or more conflicting objective functions. Rather than collapsing all goals into a single scalar, MOO produces a set of trade-off solutions — the Pareto front — from which a decision-maker selects according to preference. It is widely used in engineering design, operations research, logistics, economics, and policy analysis. |
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