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Feature Selection for Surrogate Model-Based Optimization

  • We propose a hybridization approach called Regularized-Surrogate- Optimization (RSO) aimed at overcoming difficulties related to high- dimensionality. It combines standard Kriging-based SMBO with regularization techniques. The employed regularization methods use the least absolute shrinkage and selection operator (LASSO). An extensive study is performed on a set of artificial test functions and two real-world applications: the electrostatic precipitator problem and a multilayered composite design problem. Experiments reveal that RSO requires significantly less time than Kriging to obtain comparable results. The pros and cons of the RSO approach are discussed and recommendations for practitioners are presented.

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Metadaten
Author:Frederik Rehbach, Lorenzo Gentile, Thomas Bartz-Beielstein
URN:urn:nbn:de:hbz:832-cos4-9044
Series (Serial Number):CIplus (3/2020)
Document Type:Article
Language:English
Release Date:2020/07/22
Tag:Feature selection; Optimization; Surrogate model
Descirption of the primary publication:GECCO '19: Genetic and Evolutionary Computation Conference, Prague Czech Republic, July, 201920
Institutes and Central Facilities:Fakultät für Informatik und Ingenieurwissenschaften (F10)
CCS-Classification:F. Theory of Computation
Dewey Decimal Classification:000 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik
Open Access:Open Access
Licence (German):License LogoCreative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International