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| Robust NSGA-II× | 다목적 유전 알고리즘 (MOGA)× | |
|---|---|---|
| 분야 | 시뮬레이션 | 시뮬레이션 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 2006 | 1984 |
| 창시자≠ | Kalyanmoy Deb and Himanshu Gupta | Schaffer, J. D. (early MOGA); Goldberg, D. E. (GA foundations) |
| 유형≠ | Robust evolutionary multi-objective optimization algorithm | Population-based evolutionary optimizer |
| 원전≠ | 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. DOI ↗ | Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley. ISBN: 9780201157673 |
| 별칭 | Robust NSGA2, NSGA-II under uncertainty, Uncertainty-aware NSGA-II, RNSGA-II | MOGA, Multi-objective GA, Evolutionary multi-objective optimization, EMO |
| 관련≠ | 5 | 4 |
| 요약≠ | Robust NSGA-II extends the classic NSGA-II evolutionary algorithm to account for parametric uncertainty, finding Pareto-optimal trade-off solutions that remain high-performing even when input parameters deviate from their nominal values. Instead of optimizing objective values at a single point, it evaluates each candidate solution across a range or distribution of uncertainty realizations and selects for robustness alongside Pareto dominance. | 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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