TY - RPRT U1 - Forschungsbericht A1 - Friese, Martina A1 - Bartz-Beielstein, Thomas A1 - Emmerich, Michael T1 - Building Ensembles of Surrogate Models by Optimal Convex Combination N2 - When using machine learning techniques for learning a function approximation from given data it is often a difficult task to select the right modeling technique. In many real-world settings is no preliminary knowledge about the objective function available. Then it might be beneficial if the algorithm could learn all models by itself and select the model that suits best to the problem. This approach is known as automated model selection. In this work we propose a generalization of this approach. It combines the predictions of several into one more accurate ensemble surrogate model. This approach is studied in a fundamental way, by first evaluating minimalistic ensembles of only two surrogate models in detail and then proceeding to ensembles with three and more surrogate models. The results show to what extent combinations of models can perform better than single surrogate models and provides insights into the scalability and robustness of the approach. The study focuses on multi-modal functions topologies, which are important in surrogate-assisted global optimization. T3 - CIplus - 4/2016 KW - Globale Optimierung KW - Maschinelles Lernen KW - Function Approximation KW - Surrogate Models KW - Model Selection KW - Ensemble Methods KW - Automated Learning Y2 - 2016 U6 - https://nbn-resolving.org/urn:nbn:de:hbz:832-cos4-3480 UN - https://nbn-resolving.org/urn:nbn:de:hbz:832-cos4-3480 SP - 19 S1 - 19 ER -