Process / pipelineSimulation / optimization
Bayesian NSGA-II — Surrogate-Assisted Multi-Objective Evolutionary Optimization
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.
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Sources
- 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: 10.1109/4235.996017 ↗
- Emmerich, M. T. M., Giannakoglou, K. C., Naujoks, B. (2006). Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels. IEEE Transactions on Evolutionary Computation, 10(4), 421–439. DOI: 10.1109/TEVC.2005.859463 ↗