A First Analysis of Kernels for Kriging-based Optimization in Hierarchical Search Spaces
Abstract
Many real-world optimization problems require significant resources for objective function evaluations. This is a challenge to evolutionary algorithms, as it limits the number of available evaluations. One solution are surrogate models, which replace the expensive objective. A particular issue in this context are hierarchical variables. Hierarchical variables only influence the objective function if other variables satisfy some condition. We study how this kind of hierarchical structure can be integrated into the model based optimization framework. We discuss an existing kernel and propose alternatives. An artificial test function is used to investigate how different kernels and assumptions affect model quality and search performance.
Cite
@article{arxiv.1807.01011,
title = {A First Analysis of Kernels for Kriging-based Optimization in Hierarchical Search Spaces},
author = {Martin Zaefferer and Daniel Horn},
journal= {arXiv preprint arXiv:1807.01011},
year = {2018}
}
Comments
The final authenticated version of this publication will appear in the proceedings of the 15th International Conference on Parallel Problem Solving from Nature 2018 (PPSN XV), published in the LNCS by Springer