  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.
