Ensemble of metamodels with optimized weight factors

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Multiple surrogates: how cross-validation errors can help us to obtain the best predictor (2009, methodology): “Acar and Rais-Rohani (2008) discussed variations in the choice weights using crossvalidation errors, and studied weight selection via optimization.”
Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life (2011, background): “Combining different approximate algorithms into an ensemble has found its applications in a wide variety of research fields, such as the development of committees of neural networks [13,14], the metamodeling for the design of modern engineered systems [15,16], the discovery of regulatory motifs in b”
A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design (2017, methodology): “Besides, the cross-validation approach is capable of estimating the optimal model parameters (Rippa 1999; Sundararajan and Keerthi 2001), selecting the model type (Meckesheimer et al. 2002; Acar and Rais-Rohani 2009), simultaneously estimating the model parameters and selecting the model type (Acar ”
Metamodeling in Multidisciplinary Design Optimization: How Far Have We Really Come? (2014, background): “Acar and Rais-Rohani [103] studied weight selection via optimization.”
Generalizing Surrogate-Assisted Evolutionary Computation (2010, background): “Others in [37], [38], [40] and [41] resolve this issue by introducing various ensemble model building techniques.”