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| Multi-Objective Dynamic Programming× | Multi-Objective Genetic Algorithm (MOGA)× | |
|---|---|---|
| Fachgebiet | Simulation | Simulation |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1957-1975 | 1984 |
| Urheber≠ | Extension of Bellman (1957); formalized by multiple authors from 1970s onward | Schaffer, J. D. (early MOGA); Goldberg, D. E. (GA foundations) |
| Typ≠ | Exact optimization — recursive multi-objective decomposition | Population-based evolutionary optimizer |
| Wegweisende Quelle≠ | Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780691079516 | Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley. ISBN: 9780201157673 |
| Aliasnamen | MODP, Multi-criteria dynamic programming, Vector dynamic programming, Pareto dynamic programming | MOGA, Multi-objective GA, Evolutionary multi-objective optimization, EMO |
| Verwandt≠ | 5 | 4 |
| Zusammenfassung≠ | Multi-Objective Dynamic Programming (MODP) extends Bellman's classical dynamic programming to settings where a decision-maker must optimize several competing objectives simultaneously across a sequence of stages. Rather than a single optimal policy, it produces a Pareto-optimal set of policies — each representing a distinct trade-off profile — by propagating vector-valued value functions backward through the state space. | 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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