Digital Day organiezed by TH Köln and Opitz consulting Group has taken place in TH Köln Campus Gummersbach last Sturday. Many talks and a few keynotes were presented in the field of digitalising (model-based optimization, Big Data, Oracle, Gamification, smart home, ...) by Opitz employees as well as TH Köln students. Our group also joined this coneference with a presentation about the main ideas behind the model-based optimization for real world problems.

You can find a short video about Digital Day here.

We had the chance to present our work in the field of online model selection for surrogate assisted multivariate optimization in the 15th workshop of quality improvment methods which was held in Dortmund and organized by the statistics faculty of TU Dortmund. You can read more about the quality improvemnet annual workshops here.

Due to a major release change in SPOT, the newest package version SPOT 2.0 on CRAN is currently not compatible with TDMR 1.5 (and TDMR 1.4). Therefore TDMR is currently archived on CRAN. We are working on an update. For the time being:

You can download TDMR 1.5 (and 1.4) together with the last compatible SPOT 1.1.0 from this web site. Please follow the installation instructions here on our download page.

We are happy to announce that Samineh Bagheri has won the Best Student Paper Award at "IEEE Symposium Series on Computational Intelligence" (IEEE SSCI 2016), a conference held in Athens, Greece . Read more here ...


We are delighted to announce that our recent research work related to online model selection for constrained optimization has been accepted for publication and it will be presented at SSCI 2016.  The SSCI 2016 conference will be held in Athens, Greece, from 6th to 9th of December, 2016. Many plenary and keynote talks are planned in the wide filed of computational intelligence. 

Our article "Online Selection of Surrogate Models for Constrained Black-Box Optimization" received many positive reviews. In our former work, we have developed a surrogate assisted algorithm for optimization under constraints which builds a separate surrogate model for each constraint and objective function. Then it tries to solve the optimization problem on the surrogates. So far, we always used the same model type for all functions. Now, these functions can be of completely different types for each constraint and objective, leading to possibly better models. This formed our motivation to think about an online algorithm which selects out of an ensemble the best model for each function in each iteration. We have shown that the SACOBRA optimizer with ensembles of models improves success rates by 15% in comparison to SACOBRA with a fixed model. 

The figure shows our best ensemble "MQ-Cubic" (an ensemble of different multiquadric RBFs and cubic RBFs, red curve), in comparison to fixed variants (blue and violet curves). The data profile is a measure of optimization success on a suite of problems, the higher the better:



If you are interested in reading our paper you can download it from If you are attending the SSCI 2016 conference, don't miss our presentation wink