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Call for Papers Call for Papers

at the Genetic and Evolutionary Computation Conference (GECCO 2020)

July 8-12, 2020, Cancún, Quintana Roo, Mexico

https://sites.google.com/view/benchmarking-network/home/GECCO20
http://iao.hfuu.edu.cn/benchmark-gecco20

The Good Benchmarking Practices for Evolutionary Computation (BENCHMARK@GECCO) Workshop, a part of the Genetic and Evolutionary Computation Conference (GECCO) 2020, is cordially inviting contributions and the submission of original and unpublished research papers. Here you can download the BB-DOB@gecco Workshop Call for Papers (CfP) in PDF format and here as plain text file.

GECCO 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,
  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.

We particularly welcome position statements addressing or identifying open challenges in benchmarking optimization techniques. We also suggest topics for in-depth discussions. Please also consider to suggest alternative discussion formats - we want this to be a real workshop, not yet another mini-conference!

Papers can be up to 8 pages long, in ACM format and excluding references. We emphasize that we also welcome short position statements, which can be as short as one abstract. The schedule will be designed to encourage a high level of interactivity.

For more information please contact Thomas Weise at This email address is being protected from spambots. You need JavaScript enabled to view it. with CC to This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it., and This email address is being protected from spambots. You need JavaScript enabled to view it..

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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.

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Together with our colleagues Markus Wagner, Jörg Lässig, Bin Li, and Xingyi Zhang, we are organizing a Special Issue on "Benchmarking of Computational Intelligence Algorithms" in the Applied Soft Computing (ASOC). We received quite a lot of very cool submissions until the deadline in April 2019. This is a virtual special issue, which means that the papers will immediately published when they have passed the editorial process and may appear in different physical issues and volumes of the journal. This has now happened for the first few of the submissions we have received. As a result, the journal website for our special issue is now online, too, at https://www.sciencedirect.com/journal/applied-soft-computing/special-issue/10LQLZBS7T4. We still have quite a few very interesting papers in our queue, which will appear step by step on that page as well.

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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!

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From October 20 to 25, 2019, our Profs. Möhring and Weise took part in the Dagstuhl Seminar 19431 with the topic Theory of Randomized Optimization Heuristics, organized by Dr. Carola Doerr (Sorbonne University, Paris, France), Carlos M. Fonseca (University of Coimbra, Portugal), Tobias Friedrich, (Hasso-Plattner-Institut, Potsdam, Germany), and Xin Yao (Southern University of Science and Technology, China). Dagstuhl Seminars are invitation-based meetings taking place in Schloss Dagstuhl, a castle located slightly remotely near the city of Wadern, Germany. The remote location is intentional: Here, researchers can meet for, say, five days, to have in-depth discussions without distractions. This is what Dagstuhl Seminars are, and we were very happy and felt honoured to be invited to attend the seminar on theory on randomized optimization heuristics. Not only did the seminar give us the chance to catch up with old friends and plan future collaborations, there we also very interesting and fruitful research discussions. I (Thomas Weise) was particularly happy to see that the topic of benchmarking and algorithm configuration also becomes more and more interesting for theoreticians. I also learned a lot about the progress that theory on optimization has made the past few years. In summary, the seminar was a very very nice event which indeed brought us one or two steps forward in our research. Many thanks to the organizers for doing such a great job!

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