TY - RPRT U1 - Forschungsbericht A1 - Bartz-Beielstein, Thomas A1 - Gentile, Lorenzo A1 - Zaefferer, Martin T1 - In a Nutshell: Sequential Parameter Optimization N2 - 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 Optimization Toolbox (SPOT). SPOT provides several tools for automated and interactive tuning. The underlying 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 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. T3 - CIplus - 7/2017 KW - Algorithm Tuning KW - Optimization KW - Surrogate Models KW - SPOT KW - R Y2 - 2017 U6 - https://nbn-resolving.org/urn:nbn:de:hbz:832-cos4-5928 UN - https://nbn-resolving.org/urn:nbn:de:hbz:832-cos4-5928 SP - 46 S1 - 46 ER -