Porovnať metódy
Prezrite si vybrané metódy vedľa seba; riadky, ktoré sa líšia, sú zvýraznené.
| Bayesian NSGA-II× | Multi-Objective Genetic Algorithm (MOGA)× | |
|---|---|---|
| Odbor | Simulácia | Simulácia |
| Rodina | Process / pipeline | Process / pipeline |
| Rok vzniku≠ | 2002–2006 | 1984 |
| Tvorca≠ | Emmerich, M. T. M. et al. (surrogate-assisted MO-EA); Deb et al. (NSGA-II base) | Schaffer, J. D. (early MOGA); Goldberg, D. E. (GA foundations) |
| Typ≠ | Surrogate-assisted multi-objective evolutionary algorithm | Population-based evolutionary optimizer |
| Pôvodný zdroj≠ | 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 |
| Ďalšie názvy | B-NSGA-II, Surrogate-Assisted NSGA-II, Gaussian Process NSGA-II, Bayesian Multi-Objective EA | MOGA, Multi-objective GA, Evolutionary multi-objective optimization, EMO |
| Príbuzné≠ | 3 | 4 |
| Zhrnutie≠ | Bayesian NSGA-II integrates Gaussian process surrogate models (Bayesian metamodels) into the NSGA-II evolutionary loop to solve expensive multi-objective optimization problems. By replacing costly true function evaluations with fast probabilistic predictions, it discovers high-quality Pareto-front approximations with far fewer real evaluations than standard NSGA-II. | 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. |
| ScholarGateDátová sada ↗ |
|
|