  The performance of optimization algorithms relies crucially on their
parameterizations. Finding good parameter settings is called algorithm tuning.
The sequential parameter optimization (SPOT) package for R is a toolbox for
tuning and understanding simulation and optimization algorithms. Model-based
investigations are common approaches in simulation and optimization. Sequential
parameter optimization has been developed, because there is a strong need for
sound statistical analysis of simulation and optimization algorithms. SPOT
includes methods for tuning based on classical regression and analysis of
variance techniques; tree-based models such as CART and random forest; Gaussian
process models (Kriging), and combinations of different meta-modeling
approaches. Using a simple simulated annealing algorithm, we will demonstrate
how optimization algorithms can be tuned using SPOT. The underling concepts of
the SPOT approach are explained. This includes key techniques such as
exploratory fitness landscape analysis and sensititvity analysis. 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. This article exemplifies how
SPOT can be used for automatic and interactive tuning.
