方法证据记录
Bayesian Multi-Objective Optimization
Bayesian Multi-Objective Optimization (BMOO/MOBO) uses Gaussian process surrogate models to approximate multiple expensive objective functions and guides the search toward the Pareto frontier with minimal real evaluations. By quantifying prediction uncertainty at each candidate point, it balances exploration of unknown regions against exploitation of promising solutions, making it especially powerful when each function evaluation is computationally or experimentally costly.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Bayesian Multi-Objective Optimization (BMOO) — Surrogate-assisted Pareto frontier exploration under uncertainty
分类方法记录 · process-pipeline / simulation
- Svenson, J., Santner, T. (2016). Multiobjective optimization of expensive-to-evaluate deterministic computer simulator models. Computational Statistics & Data Analysis, 94, 250-264. · DOI 10.1016/j.csda.2015.08.011
- Emmerich, M., Giannakoglou, K., 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
精选声明
声明已持久化到证据分类账中，每个声明都有自己的评估。
尚无精选声明
当分类账中没有声明时，此视图不会自行创建声明评估。
相关方法
从方法图中生成，显示为机器建议的关系 — 不推断任何证据声明。