Call for Papers Call for Papers

at the Sixteenth International Conference on Parallel Problem Solving from Nature (PPSN XVI)

September 5-9, 2020 in Leiden, The Netherlands

https://sites.google.com/view/benchmarking-network/home/PPSN20
http://iao.hfuu.edu.cn/benchmark-ppsn20

 

The Good Benchmarking Practices for Evolutionary Computation Workshop (BENCHMARK@PPSN), a part of the Sixteenth International Conference on Parallel Problem Solving from Nature (PPSN XVI), is cordially inviting the submission of contributions. Here you can download the BB-DOB@PPSN Workshop Call for Papers (CfP) in PDF format and here as plain text file.

PPSN 2020 Conference Logo

In the era of explainable and interpretable AI, it is increasingly necessary to develop a deep understanding of how algorithms work and how new algorithms compare to existing ones, both in terms of strengths and weaknesses. For this reason, benchmarking plays a vital role for understanding algorithms’ behavior. Even though benchmarking is a highly-researched topic within the evolutionary computation community, there are still a number of open questions and challenges that should be explored:

  1. most commonly-used benchmarks are too small and cover only a part of the problem space,
  2. benchmarks lack the complexity of real-world problems, making it difficult to transfer the learned knowledge to work in practice,
  3. we need to develop proper statistical analysis techniques that can be applied depending on the nature of the data, and
  4. we need to develop user-friendly, openly accessible benchmarking software.

This enables a culture of sharing resources to ensure reproducibility, and which helps to avoid common pitfalls in benchmarking optimization techniques. As such, we need to establish new standards for benchmarking in evolutionary computation research so we can objectively compare novel algorithms and fully demonstrate where they excel and where they can be improved.

The topics of interest for this workshop include, but are not limited to:

  • performance measures for comparing algorithms behavior,
  • novel statistical approaches for analyzing empirical data,
  • the selection of meaningful benchmark problems,
  • landscape analysis,
  • data mining approaches for understanding algorithm behavior,
  • transfer learning from benchmark experiences to real-world problems, and
  • benchmarking tools for executing experiments and analysis of experimental results.

Second Institute Workshop on Applied Optimization

《合肥学院运筹与优化研讨会》

We are happy to announce that the Second Institute Workshop on Applied Optimization will be held in form of the Hefei University Operations Research and Optimization Seminar [合肥学院运筹与优化研讨会] in the conference room of the Institute of Applied Optimization on November 5, 2019. It follows the First Institute Workshop on Applied Optimization, which was still held 2018 in the meeting room near our old offices. Talks will be given by:

  • Prof. Dr. Rolf H. MÖHRING, Hefei University [合肥学院] and Technische Universtiät Berlin [柏林工业大学] ,
  • Prof. Dachuan XU [徐大川教授], Beijing University of Technology [北京工业大学] ,
  • Prof. Xiaoyan ZHANG [张晓岩教授], Nanjing Normal University [南京师范大学] ,
  • Prof. Longkun GUO [郭龙坤教授], Fuzhou University [福州大学] ,
  • Dr. Yong ZHANG, [张涌研究员], Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences [中科院深圳先进技术研究院] , and
  • Dr. Zijun WU [吴自军博士], Hefei University [合肥学院] .

我校人工智能与大数据学院将于2019年11月5日上午在中德应用优化研究所会议室召开第一次“合肥学院运筹与优化研讨会”。届时, Rolf MÖHRING教授(合肥学院,柏林工业大学)、徐大川教授(北京工业大学)、张晓岩教授(南京师范大学)、郭龙坤教授(福州大学)、张涌研究员(中科院深圳先进技术研究院)以及吴自军博士(合肥学院)等国内外运筹与优化领域专家学者将作学术报告,欢迎我校师生踊跃参加!

On Monday, 2019-07-08, we had the honor to host our distinguished guest Prof. Dr. Alexander A. Solovev, full professor at Fudan University [复旦大学] (Shanghai). Prof. Solovev gave the talk "Man-Made Nano- and Micromachines" at 10:00 in Room 210 of the Sino-German Incubator of our Hefei University (the newly renovated Building 33), in which we learned about the astonishing achievements of tiny devices, such as nano-tubes and the advantages and potential of such microscopic motors for a variety of applications, ranging from medicine over clean water/air/energy to improving the cost/benefit ratio of chemical reactions by orders of magnitude. We are thankful to Prof. Solovev for his interesting talk and the exchange of thoughts and inspiring discussions that followed. This talk was followed by the presentation "GAIN – The Global Academic Innovation Network" given by Mrs. Irina Lebedeva.

It is often not easy for researchers to find other scientists to partner with for projects or to share expensive equipment with. Academic researchers also often find it hard to get in touch with industry partners to turn theoretical or fundamental research into real products, whereas small and mid-sized companies have a hard time discovering and teaming up with scientists to find answers to their research problems. On Monday, 2019-07-08, Mrs. Irina Lebedeva, COO of GAIN and PhD candidate in Computer Science and Artificial Intelligence at the East China University of Science and Technology (ECUST) [华东理工大学] in Shanghai, gave the talk "GAIN – The Global Academic Innovation Network" at 10:45 in Room 210 of the Sino-German Incubator of our Hefei University (the newly renovated Building 33). GAIN answers the above-mentioned problems by providing a comprehensive web platform, bringing together researchers from different domains, faculties, and universities as well as industry partners. We are thankful to Mrs. Lebedeva for her introduction into this helpful and indeed very needed platform, as well as for her work to develop and conceptualize it together with Prof. Dr. Alexander A. Solovev – who gave his talk "Man-Made Nano- and Micromachines" at the same meeting.

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