During useR!, the international R user conference, Sebastian Krey talked about the “Revised Sequential Parameter Optimization Toolbox”.
Keywords: optimization, tuning, surrogate model, computer experiments
Real-world optimization problems often have very high complexity, due to multi-modality, constraints, noise or other crucial problem features. For solving these optimization problems a large collection of methods are available. Most of these methods require to set a number of parameters, which have a significant impact on the optimization performance. Hence, a lot of experience and knowledge about the problem is necessary to give the best possible results. This situation grows worse if the optimization algorithm faces the additional difficulty of strong restrictions on resources, especially time, money or number of experiments.
Sequential parameter optimization (Bartz-Beielstein, Lasarczyk, and Preuss 2005) is a heuristic combining classical and modern statistical techniques for the purpose of efficient optimization. It can be applied in two manners:
– to efficiently tune and select the parameters of other search algorithms, or
– to optimize expensive-to-evaluate problems directly, via shifting the load of evaluations to a surrogate model.
SPO is especially useful in scenarios where no experience of how to choose the parameter setting of an algorithm is available, a comparison with other algorithms is needed,
an optimization algorithm has to be applied effectively and efficiently to a complex real-world optimization problem, and the objective function is a black-box and expensive to evaluate.
The Sequential Parameter Optimization Toolbox SPOT provides enhanced statistical techniques such as design and analysis of computer experiments, different methods for surrogate modeling and optimization to effectively use sequential parameter optimization in the above mentioned scenarios.
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.
In this presentation we show how the new interface of SPOT can be used to efficiently optimize the geometry of an industrial dust filter (cyclone). Based on a simplified simulation of this real world industry problem, some of the core features of SPOT are demonstrated.
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.
Bartz-Beielstein, Thomas, Christian Lasarczyk, and Mike Preuss. 2005. “Sequential Parameter Optimization.” In Proceedings Congress on Evolutionary Computation 2005 (Cec’05), 1553. Edinburgh, Scotland. https://www.spotseven.de/wp-content/papercite-data/pdf/blp05.pdf.
Forrester, Alexander, Andras Sobester, and Andy Keane. 2008. Engineering Design via Surrogate Modelling. Wiley.
More information can be found here https://user2017.sched.com/event/AxpH/the-revised-sequential-parameter-optimization-toolbox