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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.

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Metadaten
Author:Oliver Flasch, Martina Friese, Martin Zaefferer, Thomas Bartz-BeielsteinGND, Jürgen Branke
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 / 000 Allgemeines, Wissenschaft / 004 Informatik
Open Access:Open Access
Licence (German):License LogoCreative Commons - Namensnennung, Nicht kommerziell, Keine Bearbeitung