Version 2.0.2 of SPOT, the Sequential Parameter Optimization Toolbox, is available on CRAN:
SPOT provides a set of tools for model based optimization and tuning of algorithms. It includes surrogate models, optimizers and design of experiment approaches. The main interface is spot, which uses sequentially updated surrogate models for the purpose of efficient optimization. The main goal is to ease the burden of objective function evaluations, when a single evaluation requires a significant amount of resources.
Version 2 of the SPOT package is a complete redesign and rewrite of the original R package. Most function interfaces were redesigned to give a more streamlined usage experience. At the same time, modular and transparent code structures allow for increased extensibility. In addition, some new developments were added to the SPOT package. A Kriging model implementation, based on earlier Matlab code by Forrester et al. (Forrester, Sobester, and Keane 2008), has been extended to allow for the usage of categorical inputs. Additionally, it is now possible to use stacking for the construction of ensemble learners (Bartz-Beielstein and Zaefferer 2017). This allows for the creation of models with a far higher predictive performance, by combining the strengths of different modeling approaches.
- Bartz-Beielstein, Thomas, and Martin Zaefferer. 2017. “Model-Based Methods for Continuous and Discrete Global Optimization.” Applied Soft Computing 55: 154–67. doi:10.1016/j.asoc.2017.01.039.
A pre-print is available here: T. Bartz-Beielstein and M. Zaefferer, “Model-based methods for continuous and discrete global optimization,” Fakultät für Informatik und Ingenieurwissenschaften (F10), TH Köln, Schriftenreihe CIplus 8/2016, 2016.
- Bartz-Beielstein, Thomas, Christian Lasarczyk, and Mike Preuss. 2005. “Sequential Parameter Optimization.” In Proceedings Congress on Evolutionary Computation 2005 (Cec’05), 1553. Edinburgh, Scotland. http://www.spotseven.de/wp-content/papercite-data/pdf/blp05.pdf.
- Forrester, Alexander, Andras Sobester, and Andy Keane. 2008. Engineering Design via Surrogate Modelling. Wiley.