Refine
Year of publication
- 2015 (1)
Document Type
- Report (1)
Language
- English (1)
Has Fulltext
- yes (1)
Keywords
- Ensemble Methods (1)
- Genetic Programming (1)
- Modellierung (1)
- Optimierung (1)
- Surrogate-Model-Based Optimization (1)
Institute
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.