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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.
Author: | Frederik Rehbach, Lorenzo Gentile, Thomas Bartz-Beielstein |
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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): | Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International |