Learning Model-Ensemble Policies with Genetic Programming
- 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.
Author: | Oliver Flasch, Martina Friese, Martin Zaefferer, Thomas Bartz-BeielsteinGND, Jürgen Branke |
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URN: | urn:nbn:de:hbz:832-cos-787 |
Series (Serial Number): | CIplus (3/2015) |
Document Type: | Report |
Language: | English |
Year of Completion: | 2015 |
Release Date: | 2015/02/24 |
Tag: | Ensemble Methods; Genetic Programming; Surrogate-Model-Based Optimization |
GND Keyword: | Modellierung; Optimierung |
Institutes and Central Facilities: | Fakultät für Informatik und Ingenieurwissenschaften (F10) / Fakultät 10 / Institut für Informatik |
Dewey Decimal Classification: | 000 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik |
Open Access: | Open Access |
Licence (German): | Creative Commons - Namensnennung, Nicht kommerziell, Keine Bearbeitung |