TY  - RPRT
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
Y1  - 2016
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-3480
ER  - 
TY  - RPRT
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
Y1  - 2015
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos-787
ER  -