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Feature selection for surrogate model-based optimization
- Source :
- GECCO (Companion)
- Publication Year :
- 2019
- Publisher :
- ACM, 2019.
-
Abstract
- 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.
- Subjects :
- Mathematical optimization
Computer science
Dimensionality reduction
Feature selection
0102 computer and information sciences
02 engineering and technology
01 natural sciences
Regularization (mathematics)
Surrogate model
Lasso (statistics)
010201 computation theory & mathematics
Kriging
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
ddc:004
Fakultät für Informatik und Ingenieurwissenschaften (F10)
Shrinkage
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Proceedings of the Genetic and Evolutionary Computation Conference Companion
- Accession number :
- edsair.doi.dedup.....bc9fc93a2d2d15be875356ef6cdab5bc