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On July 28, 2017, I gave my research talk Automating Scientific Research in Optimization at the Enterprise Application Development (EAD) group of Prof. Dr. Jörg Lässig at the Faculty of Electrical Engineering and Computer Science of the University of Applied Sciences Zittau/Görlitz (HZG, Hochschule Zittau/Görlitz) in the historical city of Görlitz, Germany, directly next to the border to Poland. Prof. Lässig is an old friend, from the same university, and we have collaborated on numerous joint projects.

The EAD group is interested in all aspects of enterprise application development. Their leading topics are Energy Efficiency, i.e., supporting companies, users, and even energy providers to measure and reduce energy consumption, and Computational Sustainability, i.e., sustainable information technologies and applications beyond Green IT paradigm, as it covers both technologies for more simple, effective, scalable, robust, adaptable and enhanced software solutions as well as improvements towards more sustainable processes, services and products. The group follows several directions in these contexts, such as:

The group collaborates with several companies, such as the European Aeronautic Defence and Space Company, the Bosch Rexroth AG, the CBS Information Technologies AG, the Rapid-I GmbH, Saxonia Systems, ARC Solutions and Deutsche Software & Research. They are a truly unique group which combine strong fundamental and applied research. We have many common interests, for instance on the field of logistics and optimized management, as can be seen from our collaboration history.

Like my visit last year, it was a very, very pleasant experience to visit Jörg's group. The audience was again unusually big, especially for the current vacation time, and the discussions following my talk were both enlightening and interesting.

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On July 27, 2017, I gave the research talk Automating Scientific Research in Optimization at the Computational Intelligence Group of Prof. Dr. Rudolf Kruse at the Institute of Intelligent Cooperating Systems, Faculty of Computer Science of the Otto von Guericke University Magdeburg (OVGU, Otto-von-Guericke-Universität Magdeburg).

The Computational Intelligence Group has a tradition of more than twenty years. It is focused on intelligent data analysis using computational intelligence technologies, such as neural networks, fuzzy systems, evolutionary algorithms, Bayesian networks, machine learning (e.g., clustering), and approximate reasoning. The have conducted numerous fundamental research projects with support from DFG, COST, DAAD, and the EU as well as a wide variety of successful industry collaborations with companies such as BMW, Daimler, VW, Dresdner Bank, HDI, BT, Beiersdorf, SAP, and Siemens. They have published more than 35 books and 475 refereed papers. Recently, for instance, Prof. Kruse et al. published the second edition of their book Computational Intelligence: A Methodological Introduction (Springer, ISBN 978-1-4471-7294-9, doi:10.1007/978-1-4471-7296-3), which discusses neural networks, evolutionary algorithms, fuzzy systems, and Bayes networks. Remarkably, they also provide lecture slides for the whole book.

It was a real pleasure to visit this research group and a honor to meet Prof. Kruse and I really enjoyed our talk.

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On July 26, 2017, I presented our work on Automating Scientific Research in Optimization at the Institute of Applied Stochastics and Operations Research (IASOR) of the Mathematics branch of the Faculty of Mathematics/Computer Science and Mechanical Engineering of the Clausthal University of Technology (Technische Universität Clausthal) in Clausthal-Zellerfeld, Germany. The IASOR consists of four groups, Applied Statistics, Stochastic Models in Engineering Science, Stochastic Optimization, and the Discrete Optimization group.

The visit gave me the chance to have very nice talks with Prof. Dr. Stephan Westphal and Prof. Dr. Michael Kolonko.

Prof. Westphal leads the Discrete Optimization group, which is specialized in solving hard combinatorial problems, such as variants of the traveling umpire- and traveling tournament-, knapsack-, bin packing-, as well as other vehicle routing- and scheduling problems. Prof. Westphal is well-known for applying optimization methods to construct the gaming schedules of the German football and basketball leagues.

The Stochastic Optimization group of Prof. Dr. Michael Kolonko works on stochastic modeling and simulation in operations research, heuristic optimization methods, optimization and robustness of timetables in traffic networks and airport landing strips, as well as the simulation of particle mixtures. Moreover, they provide very nice teaching material in form of Java and JavaScript applets.

It is needless to say that there were many interesting topics and common interests to discuss with this outstanding group.

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On July 25, 2017, I gave my research talk on Automating Scientific Research in Optimization at the Simulation and Optimal Processes Group of  Prof. Dr. Pu Li [李浦]. at the Institute for Automation and Systems EngineeringDepartment of Computer Science and Automation at the Technische Universität Ilmenau (TU Ilmenau) in Ilmenau, Germany.

The group contributes both fundamental as well as applied research. In terms of the former, they focus on the dynamic optimization of large systems, optimization under uncertainties, real-time optimization, and non-linear model-driven control. They apply their researcher to energy systems, autonomous driving, water management, systems biology, and process engineering. What is fascinating about this research group is that they combine several research fields of applied optimization which will have a huge future: The work on energy and water network optimization is directly relevant to the immediate challenges our society faces in the next few decades. Global warming causes problems with water supply and trying to reduce global warming means that we need to change and improve our way of obtaining, transmitting, and using (renewable) energy. Autonomous driving, on the other hand, is one of the trends which will change transportation and traffic significantly. Trying to optimize any complex real-world system, be it a energy or water network, the production lines in a factory or the logistics of an enterprise cannot be done efficiently without dealing with uncertainties since we make plans for the future but can never know the future exactly. Optimization of complex systems or of engineering designs often requires simulations.

I am thankful for the chance to present at this strong research group and to Prof. Li for arranging my talk. It definitely was a very nice meeting.

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On July 24, 2017, I gave a research talk at the Computational Intelligence Group of the Faculty of Applied Computer‐ and Bio­sciences, University of Applied Sciences Mittweida (HSMW, Hochschule Mittweida). During these days, the group hosts the 9th Mittweida Workshop on Computational Intelligence (MiWoCI) and Prof. Dr. Thomas Villmann, the group head and one of the two workshop organizer, kindly provided me a time slot at the workshop for my presentation*.

The workshop itself had a very international line up of both research and industry talks with researchers from the University of Groningen (The Netherlands), the Cluster of Excellence Cognitive Interaction Technology (CITEC) of the Bielefeld University (Germany), Porsche, the Honda Research Institute (Germany), the University of Applied Sciences Würzburg-Schweinfurt (FHWS, Germany), the  Dresden (HTW, Germany) and of course the Computational Intelligence Group itself. I really regret that I could only attend the workshop at its first day. The talks that I could attend were all very interesting and underlined the excellent contributions that Computational Intelligence and Operations Research can make to the industry.

Visiting Mittweida also allowed me to get to know the Computational Intelligence Group and their research, which is mainly focused on secure classification of high-dimensional data and BigData with machine learning methods. They work on self-organizing maps, semi-supervised learning, fuzzy classification and clustering, information theoretic learning, non-standard metrics in vector quantization, prototype-based classification learning vector quantization (LVQ), Evolutionary Algorithms, and high-dimensional data analysis for, e.g., mass spectrometry data analysis in medicine as well as class visualization and projection. Like our institute, they work both on theoretical questions and practical applications.

Presenting and discussing our work in this environment of a workshop and an application-oriented research group, both on exactly our topic area, was a very pleasant experience and I want to than Prof. Villmann again for making this possible.

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