In the afternoon of September 22, 2020, the Mid-Autumn Festival and National Holiday Celebration for Foreign Experts [2020年度在皖高层次外国专家迎中秋庆国庆活动] of our province Anhui [安徽] took place at the Anhui Museum of Innovation [安徽创新馆]. The event was co-organized by the Department of Science and Technology of the Province Anhui [安徽省科学技术厅], the Hefei Municipal Bureau of Science and Technology [合肥市科学技术局], the Public Relations Department of the Provincial Party Committee [安徽省委宣传部], and the museum itself. More than 30 foreign experts from more than 10 different countries attended this event – a very high number given the current international COVID-19 pandemic. Together with Mr. Lei HONG [洪磊] of our International Office, I attended this event as representatives of our Hefei University [合肥学院].

On September 15, 2020, Prof. Weise introduced the team of our Institute of Applied Optimization (IAO) [应用优化研究所] to the fresh graduate students of our the School of Artificial Intelligence and Big Data [人工智能与大数据学院]. Each of our team members follows a different research direction within the overall field of Computational Intelligence, Optimization, Machine Learning, and Artificial Intelligence. Within the framework of our research, we can offer science-centric graduate student supervision. The presentation of our group's work has been received well, and several graduate students will join our team soon. We are looking forward to welcome them!

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).

On July 8 (Cancún time, July 9 in Chinese time), our Good Benchmarking Practices for Evolutionary Computation (BENCHMARK@GECCO) workshop took place as part of the Genetic and Evolutionary Computation Conference (GECCO) in form of an online meeting. It was a great success and more than 70 international researchers from all over the world took part in it. There were many discussions and talks, which will have a lasting impact and which probably have started quite some new efforts in our community. It is clear that all of us strive for improving the way metaheuristic algorithms and optimization problems are investigated. Good research requires sound and reproducible experimentation. Our report Benchmarking in Optimization: Best Practice and Open Issues, published on July 7 on Arxiv, was one step into that direction. Together with the input from the workshop, it will further be improved. Other community efforts discussed in this workshop include the benchmarking network and the IOHprofiler. All-in-all, many questions and topics have been raised, so we now have lots of exciting work to do to answer, research, and implement them.

Today, Mr. Alexander Jahl from the Distributed Systems Group of the Department of Electrical Engineering and Computer Science of the University of Kassel in Germany published the source code of the Distributed Algorithms Simulator on GitHub. This is an educational piece of software written in Java, which allows students to implement many of the algorithms from the field of distributed systems, ranging from distributed critical sections over time synchronization to message flooding to the distributed computation of the greatest common divisor. The simulator then shows a visualization of how the algorithms progress and how the messages travel over the network.

The original code of the Distributed Algorithms Simulator was written by me back in 2007 to support the students of the lecture "Basisalgorithmen Verteilter Systeme" held by my PhD father Prof. Dr. Kurt Geihs. It is very rewarding and I am happy to see that this piece of educational software is still doing its job after more than a decade. I hope that the 13 generations of students in the lecture as well as the colleagues who held the exercises for them after me (and who have maintained and improved the software) found it helpful in their studies and teaching, respectively. Now that it became open source, I am curious to see how long it can remain useful.

Thank you, Alex, for maintaining it for so long and for making it OSS!

feed-image rss feed-image atom