@techreport{BartzBeielstein2016, author = {Thomas Bartz-Beielstein}, title = {Stacked Generalization of Surrogate Models - A Practical Approach}, url = {https://nbn-resolving.org/urn:nbn:de:hbz:832-cos4-3759}, pages = {20}, year = {2016}, abstract = {This report presents a practical approach to stacked generalization in surrogate model based optimization. It exemplifies the integration of stacking methods into the surrogate model building process. First, a brief overview of the current state in surrogate model based opti- mization is presented. Stacked generalization is introduced as a promising ensemble surrogate modeling approach. Then two examples (the first is based on a real world application and the second on a set of artificial test functions) are presented. These examples clearly illustrate two properties of stacked generalization: (i) combining information from two poor performing models can result in a good performing model and (ii) even if the ensemble contains a good performing model, combining its information with information from poor performing models results in a relatively small performance decrease only.}, language = {en} }