Ensemble of surrogates

By Tushar Goel, Raphael T. Haftka, Wei Shyy, Néstor V. Queipo. 2007

729 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/be70238611b2cb29cbeed0a8a649ea7b94cbd542

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Citations in the corpus

Surrogate-assisted evolutionary computation: Recent advances and future challenges (2011, background): “The benefit of using multiple surrogates has also been shown empirically in many papers [54,55,52].”
Recent advances and applications of surrogate models for finite element method computations: a review (2022, background): “2005), a global cross-validationmetric (Goel et al. 2007), and error metrics (Müller and Piché 2011) are among the most prevalent.”
Using Cross Validation to Design Conservative Surrogates (2010, methodology): “For unbiased surrogates, there has been research pointing to the utility of cross validation [24–29] for selecting the most accurate surrogate from several fitted surrogates.”
Recent advances in surrogate-based optimization (2009, methodology): “[28] have tried using a weighted average of an ensemble of surrogates.”
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”
Shape optimization of a fan-shaped hole to enhance film-cooling effectiveness (2010, methodology): “[18] developed weighted-average surrogate models that uses the response surface approximation (RSA) [19], Kriging (KRG) [20], and radial basis neural network (RBNN) [21] models.”
Data-Driven Evolutionary Optimization: An Overview and Case Studies (2019, methodology): “CAL-SAPSO uses two surrogate ensembles composed of a polynomial regression model, an RBFN, and a Kriging model [71] to approximate the expensive objective.”
Incremental modeling of a new high-order polynomial surrogate model (2016, background): “[11, 12], or the combination of these surrogate models [13-15].”
On Using Surrogates with Genetic Programming (2015, background): “Recently, also approaches were proposed that combine different models (possibly using different techniques) or select the most promising model(s) depending on the current situation (see, e.g., Goel et al. (2007); Lim et al. (2007)).”
Optimization of a Cylindrical Film Cooling Hole using Surrogate Modeling (2009, methodology): “[ 12 ] developed a weighted average surrogate model and concluded that this model is a more reliable prediction method than individual surrogates.”
Generalizing Surrogate-Assisted Evolutionary Computation (2010, background): “Others in [37], [38], [40] and [41] resolve this issue by introducing various ensemble model building techniques.”
Multiple surrogates: how cross-validation errors can help us to obtain the best predictor (2009, methodology): “Goel et al. (2007) suggested α = 0.05 and β = −1.”
Metamodeling in Multidisciplinary Design Optimization: How Far Have We Really Come? (2014, methodology): “Recent works such as [102] and [105] extend the idea to selection of models”
Comparing error estimation measures for polynomial and kriging approximation of noise-free functions (2009, methodology): “Finally, we explore the concept of an ensemble of error measures that is inspired by our previous work on the ensemble of surrogates ( Goel et al. 2007 ).”