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Building Ensembles of Surrogate Models by Optimal Convex Combination

  • 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.

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Author:Martina Friese, Thomas Bartz-BeielsteinGND, Michael Emmerich
Series (Serial Number):CIplus (4/2016)
Document Type:Report
Year of Completion:2016
Release Date:2016/04/01
Tag:Automated Learning; Ensemble Methods; Function Approximation; Model Selection; Surrogate Models
GND Keyword:Globale Optimierung; Maschinelles Lernen
Page Number:19
Institutes and Central Facilities:Fakultät für Informatik und Ingenieurwissenschaften (F10)
CCS-Classification:J. Computer Applications / J.0 GENERAL
Dewey Decimal Classification:000 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik
JEL-Classification:C Mathematical and Quantitative Methods / C6 Mathematical Methods and Programming / C61 Optimization Techniques; Programming Models; Dynamic Analysis
Open Access:Open Access
Licence (German):License LogoCreative Commons - Namensnennung, Nicht kommerziell, Keine Bearbeitung