A Gentle Introduction to Sequential Parameter Optimization

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 package for R [5] is a toolbox 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 di erent meta-modeling approaches. This article exemplifies how an existing optimization algorithm, namely simulated annealing, can be tuned using the SPOT frame-