GECCO 2020 Workshop
Good Benchmarking Practices for Evolutionary Computation
Brace yourself for a highly interactive workshop, with plenty of room for discussions and interaction. This is not just another mini-conference, but a platform to come together and to discuss recent progress and challenges in the area of benchmarking iterative optimization heuristics.
Scope and Objectives
Benchmarking aims to illuminate the strengths and weaknesses of algorithms regarding different problem characteristics. To this end, several benchmarking suites have been designed which target different types of characteristics.
Gaining insight into the behavior of algorithms on a wide array of problems has benefits for different stakeholders. It helps engineers new to the field of optimization find an algorithm suitable for their problem. It also allows experts in optimization to develop new algorithms and improve existing ones.
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:
(i) most commonly-used benchmarks are small and do not cover the space of meaningful problems,
(ii) benchmarking suites lack the complexity of real-world problems,
(iii) proper statistical analysis techniques that can easily be applied depending on the nature of the data are lacking or seldom used, and
(iv) user-friendly, openly accessible benchmarking techniques and software need to be developed and spread.
We wish to enable a culture of sharing to ensure direct access to resources as well as reproducibility. This helps to avoid common pitfalls in benchmarking such as overfitting to specific test cases. We aim 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.
As the goal of the workshop is to discuss, develop and improve benchmarking practices in evolutionary computation, we particularly welcome informal position statements addressing or identifying open challenges in benchmarking, as well as all other suggestions and contributions for a discussion. Possible contributions include, but are not limited to:
- lists of open questions/issues in benchmarking
- examples of good benchmarking
- descriptions of common pitfalls in benchmarking and how to avoid them.
We also welcome the submission of workshop papers to be published in the GECCO companion proceedings. The Workshop Call for Participation (CfP) can also be downloaded in PDF format here.
Our goal for the WORKshop is to collaboratively produce output that improves the state-of-the-art of benchmarking in evolutionary computation, not to organize yet another mini-conference!
The topics of interest for this workshop include, but are not limited to:
- the selection of meaningful (real-world) benchmark problems,
- performance measures for comparing algorithm behavior,
- novel statistical approaches for analyzing empirical data,
- 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.
(all dates are strict, i.e., no extensions possible!):
Submission opening: February 27, 2020
- Submission deadline: April 17, 2020
- Notification of acceptance: May 1, 2020
- Camera-Ready Material: May 8, 2020
- Author registration deadline: April 27, 2020 (extension to May 8 likely, but we are waiting for a formal confirmation)
Please also note that, by GECCO rules, each accepted paper needs to have at least one author registered by the author registration deadline. If an author is presenting more than one paper at the conference, she/he does not pay any additional registration fees.
Send your suggestions for presentations and/or discussions by e-mail to all five main contacts listed below. Please indicate the format of your suggested contribution (talk, discussion, breakout, brainstorming, etc.) and how much time you suggest for this activity.
- Thomas Bäck (Leiden University, The Netherlands)
- Bilel Derbel (University of Lille, Lille, France)
- Carola Doerr (CNRS researcher at Sorbonne University, Paris, France)
- Tome Eftimov (Jožef Stefan Institute, Ljubljana, Slovenia)
- Pascal Kerschke (University of Münster, Germany)
- William La Cava (University of Pennsylvania, USA)
- Manuel López-Ibáñez (University of Manchester, UK)
- Katherine Malan (University of South Africa)
- Boris Naujoks (TH Cologne, Germany)
- Pietro S. Oliveto (University of Sheffield, UK)
- Patryk Orzechowski (University of Pennsylvania, USA)
- Mike Preuss (Leiden University, The Netherlands)
- Jérémy Rapin (Facebook AI Research, Paris, France)
- Ofer M. Shir (Tel-Hai College and Migal Institute, Israel)
- Olivier Teytaud (Facebook AI Research, Paris, France)
- Heike Trautmann (University of Münster, Germany)
- Ryan J. Urbanowicz (University of Pennsylvania, USA)
- Vanessa Volz (modl.ai, Copenhagen, Denmark)
- Markus Wagner (The University of Adelaide, Australia)
- Hao Wang (LIACS, Leiden University, The Netherlands)
- Thomas Weise (Institute of Applied Optimization, Hefei University, Hefei, China)
- Borys Wróbel (Adam Mickiewicz University, Poland)
- Aleš Zamuda (University of Maribor, Slovenia)
A similar benchmarking best practices workshop will be held at PPSN 2020, which takes place from September 5-9, 2020, in Leiden, The Netherlands: https://sites.google.com/view/benchmarking-network/home/activities/PPSN20. Contributions to this workshop are welcome in any format until June 8, 2020.
This workshop is organized as part of ImAppNIO Cost Action 15140.