Ensemble of metamodels with Recursive arithmetic average

Ensemble of metamodels is an effective way to overcome the deficiency of stand-alone metamodel. A new ensemble technique with Recursive arithmetic average is proposed in this paper. The presented technique has been evaluated using four benchmark problems, and several commonly used criteria for evaluation of prediction error are adopted to examine the ensemble technique. The results showed that the proposed ensemble of metamodels with recursive arithmetic average provides more accurate predictions than the standalone metamodels and for most problems even surpassing the previously reported ensemble techniques.