A First Analysis of Kernels for Kriging-based Optimization in Hierarchical Search Spaces
This work addresses optimization problems with hierarchical variables for researchers in evolutionary algorithms and surrogate modeling, but it is incremental as it builds on existing kernels and uses an artificial test function.
The paper tackled the challenge of optimizing expensive objective functions with hierarchical variables by integrating hierarchical structure into model-based optimization frameworks, comparing existing and alternative kernels on an artificial test function to assess model quality and search performance.
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.