Surrogates for hierarchical search spaces
| Autor: | Daniel Horn, Martin Zaefferer, Nils-Jannik Schüßler, Jörg Stork |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Process (computing) 0102 computer and information sciences 02 engineering and technology Function (mathematics) computer.software_genre 01 natural sciences Kernel (linear algebra) 010201 computation theory & mathematics Kriging Face (geometry) Kernel (statistics) 0202 electrical engineering electronic engineering information engineering Test functions for optimization Benchmark (computing) 020201 artificial intelligence & image processing Algorithm design Data mining computer |
| Zdroj: | GECCO |
| DOI: | 10.1145/3321707.3321765 |
| Popis: | Optimization in hierarchical search spaces deals with variables that only have an influence on the objective function if other variables fulfill certain conditions. These types of conditions complicate the optimization process. If the objective function is expensive to evaluate, these complications are further compounded. Especially, if surrogate models are learned to replace the objective function, they have to be able to respect the hierarchical dependencies in the data. In this work a new kernel is introduced, that allows Kriging models (Gaussian process regression) to handle hierarchical search spaces. This new kernel is compared to existing kernels based on an artificial benchmark function. Thereby, we face a typical algorithm design problem: The statistical analysis of benchmark results. Instead of just identifying the best algorithm, it is often desirable to compute algorithm rankings that depend on additional experimental parameters. We propose a method for the automated analysis of such algorithm comparisons. Instead of investigating all parameter constellations of our artificial test function at once, we apply a cluster algorithm and analyze rankings of the algorithms within each cluster. This new method is used to analyze the above mentioned benchmark of kernels for hierarchical search spaces. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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