Free Paper: In a Nutshell – Sequential Parameter #Optimization @utopia_network #TH_Koeln #rstats

We proudly present the first result of our cooperation in the UTOPIAE network:
Lorenzo Gentile, Martin Zaefferer, and Thomas Bartz-Beielstein published a paper about sequential parameter optimization. The paper is based on the talk given by Thomas Bartz-Beielstein during the UTOPIAE Network Training School at University of Strathclyde, Glasgow, earlier this year.

The paper is entitled “In a Nutshell: Sequential Parameter Optimization”. It gives a short introduction to surrogate model based optimization, which will be applied in the UTOPIAE project. Many examples illustrate the usefulness of the SPOT approach.

The paper can be downloaded here.

The abstract reads as follows:
The performance of optimization algorithms relies crucially on their parameterizations. Finding good parameter settings is called algorithm tuning. Using a simple simulated annealing algorithm, we will demonstrate how optimization algorithms can be tuned using the sequential parameter opti- mization toolbox (SPOT). SPOT provides several tools for automated and interactive tuning. The underling concepts of the SPOT approach are explained. This includes key techniques such as exploratory fitness landscape analysis and response surface methodology. Many examples illustrate how SPOT can be used for understanding the performance of algorithms and gaining insight into algorithm’s behavior. Furthermore, we demonstrate how SPOT can be used as an optimizer and how a sophisticated ensemble approach is able to combine several meta models via stacking.