Today, on December 1st, 2020, the "13. Deutsch-Chinesisches Symposium zur Anwendungsorientierten Hochschulausbildung" [第十三届中德应用型高等教育研讨会], i.e., 13th Chinese-German Symposium on Application-Oriented University Education, took place in Hefei [安徽省合肥市]. The location of the event is switched yearly between Hefei and Osnabrück (Germany) and it is always co-organized by Hefei University [合肥学院] and the Hochschule Osnabrück [奥斯纳布吕克应用科学大] under guidance of the Ministry of Education of the Province Anhui, China and the Ministry for Science and Culture of Lower Saxony, Germany. The topic this year was Smart-Learning, Industrie-Lehre-Integration und Hochwertige Anwendungsorientierte Hochschulausbildung, i.e., Smart-Learning, Industry-Education-Integration, and High-Quality Application-Oriented University Education. Due to this year's special situation, the symposium was condensed to a single day. Nevertheless, it featured many highly interesting talks given by university professors and educational leaders both from China and Germany. After the opening ceremony and five insightful keynotes, three parallel sessions were held. German and Chinese professors took turns in giving insightful presentations in two of the sessions. In the third session, the presidents of several universities exchanged their perspectives and ideas. The meeting can be considered as highly successful and had a high attendance. The integration of presence talks and web-based presentations was smooth and seamless, allowing for the full participation of experts who could not attend the meeting physically. For thirteen years now, this symposium has significantly influenced the development of application-oriented university education in both China and Germany. Next year, its 14th iteration will take place in Osnabrück and I am very much looking forward to it.

From November 9 to 13, 2020, the Lorentz Center Workshop "Benchmarked: Optimization Meets Machine Learning" is jointly organized by Carola Doerr, Thomas Stützle, Mike Preuss, Marc Schoenauer, and Joaquin Vanschoren as an online event. It brings together experts from all fields of benchmarking and automated algorithm configuration and selection with focus on optimization and had more than 100 registered participants. In particular, the Benchmarked: Optimization Meets Machine Learning workshop, the goal is to discuss the impact of automated decision-making on heuristic optimization. More specifically, it is discussed how the possibility to automatically select and configure optimization heuristics changes the requirements for their benchmarking. The key objectives of this Lorentz Center workshop are:

  • to develop a joint vision on the next generation of benchmarking optimization heuristics in the context of automated algorithm selection and configuration, and
  • to design a clear road-map guiding the research community towards this vision.

It is discussed what an ideal benchmarking environment would look like, how such an "ideal tool" compares to existing software, and how we can close the gap by improving the compatibility between ongoing and future projects. The aim is to designing a full benchmarking engine that ranges from modular algorithm frameworks over problem instance generators and landscape analysis tools to automated algorithm configuration and selection techniques, all the way to a statistically sound evaluation of the experimental data.

In this setting, Prof. Thomas Weise organized a first and co-organizes a second breakout session on "Data Formats for Benchmarking." The rationale behind these specific sessions is that technical details are often ignored in research. However, technicalities such as the data format used for storing experimental results can nevertheless have a big influence on our research. The data format determines what information will be available after experiment. This includes what information is available for evaluation. But it also determines whether the experiment will be easy to replicate or whether the results can be validated. It also determines which tools we can use for evaluating the results. The data format may even determine how we can execute an experiment (in parallel? in a distributed fashion? can experiments be restarted?). The goal of the breakout session is to collect thoughts and ideas about suitable data formats for storing the output of experiments in optimization and machine learning. The aim is to collect a set of requirements for a good format and structure. If these are well understood, it may be possible to eventually define a simple and clear standard for the future – or at least some guidelines that can help researchers to not miss any detail that should be considered when storing experimental data.

Our team warmly welcomes Mrs. Yuanyuan LEI [雷园园], Mr. Zhiyang LIU [刘志洋] , Mr. Peng GAO [高鹏], and Mrs. Shuoyi RAN [冉烁依] to join our team as graduate students for the period from 2020 to 2023. Mr. LEI and Mrs. GAO will be supervised by Assoc. Prof. Dr. Xinlu Li, whereas Mr. LIU and Mrs. RAN will be jointly co-supervised by Prof. Dr. Thomas Weise and Assoc. Prof. Dr. Zhize Wu. We are looking forward to working with you. Welcome aboard!

Today, on September 29, 2020, I attended the Mid-Autumn Tea Party for People from All Walks of Life [合肥市各界人士国庆中秋茶话会] in the city hall of Hefei [合肥市]. Like last year’s celebration of the 70th Anniversary of the People's Republic of China, it was a very nice event. As the name suggests, the event was attended by people from a variety of different career paths, including business representatives, representatives from several different religions, party members, military leaders, researchers, and representatives of the international community of our city. The event began with four ceremonial speeches – the first of which was held by Prof. Dr. Chunmei WU [吴春梅], the president of our Hefei University [合肥学院院长]. Then, a very nice program with different songs followed. There were several professional opera singers giving great performances. I found the performance of the CPCC youth chorus especially nice, because they are normal employees of the city hall and government, but they sang absolutely perfectly. In summary, this was a very enjoyable afternoon and I even had the chance to chat with some new friends. I hope that I will be able to attend more such events in the future. (Postscript: And I indeed got the chance to attend the 2021 Hefei National Day Tea Party for People from All Walks of Life.)

Today, our  Special Issue on Benchmarking of Computational Intelligence Algorithms in the Applied Soft Computing Journal (ASOC) has finally been completed. We accepted 14 articles, which either introduce new new benchmarks or benchmark generators, propose new visualization and evaluation methods, provide new tools, or study general topics in the field. Each article makes an important contribution to the art of analyzing and understanding the performance of computational intelligence methods. We are very thankful to the ASOC journal to allow us to make this happen – and especially for the outstanding support and the help provided by Mario Koeppen and Bas van Vlijmen of the editor team as well as Xinrui Wang, the publishing specialized at Elsevier. Of course, we are as same as thankful to our authors and reviewers, who put in a lot of work to ensure that all articles were refined and refined and refined again until they were perfect. Now that the editorial of the issue is out, this journey is complete. Benchmarking of optimization is an interesting topic which is gaining more and more momentum in the research community. For instance, there currently is a large community effort ongoing with the goal to gather good practices for benchmarking, which resulted in the technical report Benchmarking in Optimization: Best Practice and Open Issues and to which we are also contributing (and which directly picks up some of the topics tackled in our special issue).

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