Portrait of Dr. Rongwang Yin.

The Institute of Applied Optimization welcomes Dr. Rongwang Yin [殷荣网博士], who today has joined our team as researcher. Before joining our institute, he finished his PhD research at the School of Engineering Science (SES) [工程科学学院] of the University of Science and Technology of China (USTC) [中国科学技术大学] in Hefei [合肥], Anhui [安徽], China [中国]. Dr. Yin is an expert in applying optimization and AI technologies to problems in the petroleum industry, such as identifying multistage fracturing horizontal well parameters and physical reservoir properties, but has also contributed work to renewable energies and wireless network communication.

We are very happy that Dr. Yin joined our team. We are looking forward to working together on his exciting research topics.

Today, December 2nd, 2020, marks the 40th Anniversary of our Hefei University [合肥学院]. In 1980, our uni was founded under the name Hefei United University [合肥联合大学] was founded by the Secretary of the Hefei Municipal Party Committee Rui ZHENG [合肥市委书记郑锐] and the Vice President of USTC, Mr. Chengzong YANG [中国科大副校长杨承宗]. In 2002, Hefei United University merged with the Hefei Institute of Education [合肥教育学院] and Hefei Normal School [合肥师范学校] to Hefei University [合肥学院] with the approval of the Ministry of Education [育部批]. Since its establishment, our university has implemented the concepts of locality, application-orientation, and internationalization [“地方性、应用型、国际化”]. The Chinese-German collaboration always played a large role in the development of our university, which spearheaded the adaptation of German concepts for application-oriented education such as the dual system of cooperative education [双元制合作教育] to Chinese needs. With the support of both the Chinese and the German government and inspired by the meeting of the German Chancelor Dr. Angela Merkel and the Chinese Premier Keqiang LI [李克强 (国务院总理)] in our university, the Demonstration Base for Sino-German Educational Cooperation [中德教育合作示范基地] was launched here in 2015. Today, our university has more than 17'000 students and 1000 teachers. All of us are thankful for the foundation laid by 40 years of hard work by the teachers, students, administration staff, and service workers of our university. We will try our best to keep improving our Hefei University for a long and successful future. Happy Birthday, Hefei University!

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.

Call for Papers Call for Papers

2021 IEEE Congress on Evolutionary Computation (CEC 2021)

June 28-July 1, 2021 in Kraków, Poland
http://iao.hfuu.edu.cn/bocia21

 

The Special Session on Benchmarking of Computational Intelligence Algorithms (BOCIA), as part of the 2021 IEEE Congress on Evolutionary Computation (CEC 2021), took place on June 30, 2021 at 10am. Here you can download the BOCIA Special Session Call for Papers (CfP) in PDF format and here as plain text file.

Computational Intelligence (CI), including Evolutionary Computation, Optimization, Machine Learning, and Artificial Intelligence, is a huge and expanding field which is rapidly gaining importance, attracting more and more interests from both academia and industry. It includes a wide and ever-growing variety of optimization and machine learning algorithms, which, in turn, are applied to an even wider and faster growing range of different problem domains. For all of these domains and application scenarios, we want to pick the best algorithms. Actually, we want to do more, we want to improve upon the best algorithm. This requires a deep understanding of the problem at hand, the performance of the algorithms we have for that problem, the features that make instances of the problem hard for these algorithms, and the parameter settings for which the algorithms perform the best. Such knowledge can only be obtained empirically, by collecting data from experiments, by analyzing this data statistically, and by mining new information from it. Benchmarking is the engine driving research in the fields of Computational Intelligence for decades, while its potential has not been fully explored.

The goal of this special session was to solicit original works on the research in benchmarking: Works which contribute to the domain of benchmarking of algorithms from all fields of Computational Intelligence, by adding new theoretical or practical knowledge. Papers which only apply benchmarking are not in the scope of the special session.

This special session was technically supported by the IEEE CIS Task Force on Benchmarking.

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