TY - JOUR U1 - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed) A1 - Rehbach, Frederik A1 - Gentile, Lorenzo A1 - Bartz-Beielstein, Thomas T1 - Feature Selection for Surrogate Model-Based Optimization N2 - 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. T3 - CIplus - 3/2020 KW - Optimization KW - Feature selection KW - Surrogate model U6 - https://nbn-resolving.org/urn:nbn:de:hbz:832-cos4-9044 UN - https://nbn-resolving.org/urn:nbn:de:hbz:832-cos4-9044 ER -