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 "IEEE Symposium Series on Computational Intelligence" (IEEE 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 field 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 http://www.gm.fh-koeln.de/~konen/Publikationen/Bagh16-SSCI.pdf. If you are attending the SSCI 2016 conference, don't miss our presentation 
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