TY - RPRT U1 - Forschungsbericht A1 - Bartz-Beielstein, Thomas A1 - Zaefferer, Martin T1 - A Gentle Introduction to Sequential Parameter Optimization N2 - There is a strong need for sound statistical analysis of simulation and optimization algorithms. Based on this analysis, improved parameter settings can be determined. This will be referred to as tuning. Model-based investigations are common approaches in simulation and optimization. The sequential parameter optimization toolbox (SPOT), which is implemented as a package for the statistical programming language R, provides sophisticated means for tuning and understanding simulation and optimization algorithms. The toolbox includes methods for tuning based on classical regression and analysis of variance techniques; tree-based models such as classification and regressions trees (CART) and random forest; Gaussian process models (Kriging), and combinations of different meta-modeling approaches. This article exemplifies how an existing optimization algorithm, namely simulated annealing, can be tuned using the SPOT framework. T3 - CIplus - 1/2012 KW - Optimierung KW - Globale Optimierung KW - Simulation KW - Simulated annealing KW - Versuchsplanung KW - Modellierung KW - Soft Computing KW - Sequentielle Parameter Optimierung KW - Parametertuning KW - Computational Intelligence Y2 - 2012 U6 - https://nbn-resolving.org/urn:nbn:de:hbz:832-cos-191 UN - https://nbn-resolving.org/urn:nbn:de:hbz:832-cos-191 ER -