@techreport{FlaschFrieseZaeffereretal.2015, author = {Oliver Flasch and Martina Friese and Martin Zaefferer and Thomas Bartz-Beielstein and J{\"u}rgen Branke}, title = {Learning Model-Ensemble Policies with Genetic Programming}, url = {https://nbn-resolving.org/urn:nbn:de:hbz:832-cos-787}, year = {2015}, abstract = {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.}, language = {en} }