Many meta-models have been developed to approximate true responses. These meta-models are often used for optimization instead of computer simulations which require high computational cost. However, designers do not know which meta-model is the best one in advance because the accuracy of each meta-model becomes different from problem to problem. To address this difficulty, research on the ensemble of meta-models that combines stand-alone meta-models has recently been pursued with the expectation of improving the prediction accuracy. In this study, we propose a selection method of weight factors for the ensemble of meta-models based on v-nearest neighbors’ cross-validation error (CV). The four stand-alone meta-models we employed in this study are polynomial regression, Kriging, radial basis function, and support vector regression. Each method is applied to five 1-D mathematical examples and ten 2-D mathematical examples. The prediction accuracy of each stand-alone meta-model and the existing ensemble of meta-models is compared. Ensemble of meta-models shows higher accuracy than the worst stand-alone model among the four stand-alone meta-models at all test examples (30 cases). In addition, the ensemble of meta-models shows the highest accuracy for the 5 test cases. Although it has lower accuracy than the best stand-alone meta-model, it has almost same RMSE values (less than 1.1) as the best standalone model in 16 out of 30 test cases. From the results, we can conclude that proposed method is effective and robust.

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