This year GECCO took place in Madrid on 11-15 of July. Many interesting tutorials and workshops were presented in this conference. On 14th of July we presented our paper about a new repair method for constrained optimization. You can download the presentation slides from here.











We are glad to inform you that SACOBRA package is now available on CRAN. 

SACOBRA package performs surrogate-assisted optimization by utilizing radial basis function interpolation. SACOBRA solver is a derivative-free optimizer appropriate to address constrained expensive optimization problems.


A new technical report on temporal difference (TD) learning for games and "self-play" algorithms for game-agent training is available. This report by Wolfgang Konen features a gentle introduction to TD learning for game play and gives hints for the practioner on the implementation of such algorithms . It shows the references to the most recent applications in this field and discusses in an appendix the more advanced topic of eligibility traces and how and why they work.

This report should be a help for people starting new in the field of TD learning for games and for people who work already in this field but struggle with specific details. It is an updated English translation of an earlier report in German language.


Konen, Wolfgang (2015): Reinforcement Learning for Board Games: The Temporal Difference Algorithm. Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Sciences, 2015.

PDF English

PDF German

Ms. Samineh Bagheri has won the 3rd price in the annual Erzquell award with her master thesis „Efficient Surrogate Assisted Optimization for Constrained Black-Box Problems“. My most cordial congratulations to her! This thesis is part of the MONREP project (Model-based Optimization for Nonlinear, REstricted Production processes) and it was conducted under my supervision. Samineh Bagheri did a great job in her thesis, she carried out complicated computer experiments and showed that certain constrained optimization problems can be solved with much less function evaluations than there are usually required in other state-of-the-art optimization algorithms like genetic or evolutionary algorithms. The main idea of her thesis is to use flexible models, which are built during the optimization run. Ms. Bagheri uses Radial Basis Function (RBF) models for that purpose.

Price Winner Erzquell

left to right: Markus Thill M.Sc., M. Bagheri, Samineh Bagheri M. Sc.,
Prof. Dr. Wolfgang Konen                  (Bild: Manfred Stern / FH Köln)


Read more about the annual Erzquell award and the price winners (sorry, in German only) here. The Erzquell award has the additional advantage that they serve at the award ceremony one of the famous local beers :-) !

Many real-world optimization problems are dealing with constraints. The valid solution for constrained optimization problems (COP) lies somewhere in the feasible region which is a sub-set of the input-space. The borders of the feasible area are defined by one or many constraints. Existance of constraints makes the optimization problems more demanding. There are different approaches to handle the constraints but none of them can outperform all others for all different types of COPs. 


There are only a few constraint handlers which work on the basis of fixing a good "infeasible solution" and generating feasible solutions by using the information coming from the infeasible candidates. We proposed a new technique to repair infeasible solutions. The correspoding work was accepted as a paper in GECCO 2015. A talk about this work will be given in GECCO conference on 11-15 of July in Madrid.