TY - RPRT U1 - Forschungsbericht A1 - Flasch, Oliver A1 - Friese, Martina A1 - Zaefferer, Martin A1 - Bartz-Beielstein, Thomas A1 - Branke, Jürgen T1 - Learning Model-Ensemble Policies with Genetic Programming N2 - We propose to apply typed Genetic Programming (GP) to the problem of finding surrogate-model ensembles for global optimization on compute-intensive target functions. In a model ensemble, base-models such as linear models, random forest models, or Kriging models, as well as pre- and post-processing methods, are combined. In theory, an optimal ensemble will join the strengths of its comprising base-models while avoiding their weaknesses, offering higher prediction accuracy and robustness. This study defines a grammar of model ensemble expressions and searches the set for optimal ensembles via GP. We performed an extensive experimental study based on 10 different objective functions and 2 sets of base-models. We arrive at promising results, as on unseen test data, our ensembles perform not significantly worse than the best base-model. T3 - CIplus - 3/2015 KW - Modellierung KW - Optimierung KW - Ensemble Methods KW - Genetic Programming KW - Surrogate-Model-Based Optimization Y2 - 2015 U6 - https://nbn-resolving.org/urn:nbn:de:hbz:832-cos-787 UN - https://nbn-resolving.org/urn:nbn:de:hbz:832-cos-787 ER -