AABOH 2022

Home Introduction Topics of Interest Program Important Dates Instructions for Authors Organizers Program Committee Chair Biographies

GECCO 2022

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AABOH 2021

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1. Introduction

The Analysing Algorithmic Behaviour of Optimisation Heuristics Workshop (AABOH), as part of the 2022 Genetic and Evolutionary Computation Conference (GECCO'22), invited the submission of original and unpublished research papers. You can download the AABOH Special Session Call for Papers in PDF format and plain text file.

Optimisation and Machine Learning tools are among the most used tools in the modern world with its omnipresent computing devices. Yet, the dynamics of these tools have not been analysed in detail. Such scarcity of knowledge on the inner workings of heuristic methods is largely attributed to the complexity of the underlying processes that cannot be subjected to a complete theoretical analysis. However, this is also partially due to a superficial experimental set-up and, therefore, a superficial interpretation of numerical results. Indeed, researchers and practitioners typically only look at the final result produced by these methods. Meanwhile, the vast amount of information collected over the run(s) is wasted. In the light of such considerations, it is now becoming more evident that such information can be useful and that some design principles should be defined that allow for online or offline analysis of the processes taking place in the population and their dynamics.

Hence, with this workshop, we call for both theoretical and empirical achievements identifying the desired features of optimisation and machine learning algorithms, quantifying the importance of such features, spotting the presence of intrinsic structural biases and other undesired algorithmic flaws, studying the transitions in algorithmic behaviour in terms of convergence, any-time behaviour, performances, robustness, etc., with the goal of gathering the most recent advances to fill the aforementioned knowledge gap and disseminate the current state-of-the-art within the research community.

1. Topics of Interest

We encourage submissions exploiting carefully designed experiments or data-heavy approaches that can come to help in analysing primary algorithmic behaviours and modelling internal dynamics causing them. As an indication, some (but not all) relevant topics of interests are reported in the list below:

• global search vs. local search,
• exploration vs. exploitation,
• time and space complexity,
• premature convergence and stagnation,
• structural bias,
• genotypic or phenotypic diversity,
• robustness of the produced solution,
• secondary benchmarking,
• anytime performance.

All accepted papers of this workshop will be included in the Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'22) Companion Volume.

2. Program

The AABOH workshop will take place on Sunday, July 10, 08:30-12:40 (EDT) and will consist of 2 sessions

Session 1: Contributed papers
Welcome Talk	08:30
Survivor Selection in a Crossoverless Evolutionary Algorithm	08:35
Nielis Brouwer, Danny Dijkzeul, Levi Koppenhol, Iris Pijning, Daan van den Berg
Exactly characterizable parameter setings in a crossoverless evolutionary algorithm	08:50
Levi Koppenhol, Nielis Brouwer, Danny Dijkzeul, Iris Pijning, Joeri Sleegers, Daan van den Berg
Examining Algorithm Behavior using Recurrence Quantification and Landscape Analyses	09:05
Mario Munoz Acosta
The Effect of Decoding Fairness on Particle Median Problem	09:20
Pavel Kromer, Vojtech Uher
Dynamic Computational Resource Allocation for CFD Simulations Based on Pareto Front Optimization	09:35
Gašper Petelin, Margarita Antoniou, Gregor Papa
Using Structural Bias to Analyse the Behaviour of Modular CMA-ES	09:50
Diederick Vermetten, Fabio Caraffini, Bas van Stein, Anna Kononova
Closing	10:05
Session 2: Theoretical and Empirical Analysis of Optimisation Heuristics
Opening Talk	10:50
Invited Talk	10:55
Benjamin Doerr, École Polytechnique, Palaiseau, France
Title: Don't Implement, Think! Abstract: It is clear that the vast majority of algorithm analyses is experimental. In this talk, I shall argue that mathematical analyses of algorithms have a few undeniable advantages. I shall then argue that theory vs. experiments is not an exclusive-or. Rather, you should use your mathematical skills to understand as much as possible via theoretical means (allowing any degree of imprecision or unproven assumptions that is necessary to survive). Then, and only then, is the time to design a meaningful experiment that does not just blindly collect a mass of ununderstandable data, but that confirms (or disproves) the theories obtained before. Of course you should implement, but backed up by as much theory as possible!
Invited Talk	11:40
Thomas Bartz-Beielstein, TH Koeln, Institute for Data Science, Engineering, and Analytics, Germany
Title: Hyperparameter Tuning of Deep Neural Networks Abstract: A surrogate model based Hyperparameter Tuning (HPT) approach for Deep Learning (DL) is presented. We will demonstrate how the architecture- level parameters (hyperparameters) of Deep Neural Networks (DNNs) that were implemented in keras/tensorflow can be optimized. The implementation of the tuning procedure is 100% accessible from R, the software environment for statistical computing. The performances of six Machine Learning (ML) methods (k-Nearest-Neighbor (KNN), Elastic Net (EN), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)) are compared to the results from the DNN. The R package SPOT is used as a "datascope" to analyze the results from the HPT runs from several perspectives: in addition to Classification and Regression Trees (CART), the analysis combines results from surface, sensitivity and parallel plots with a classical regression analysis. This study provides valuable insights in the tunability of several ML and DL methods, which is of great importance for the AI practitioner. This keynote presents results from the forthcoming book "Hyperparameter Tuning for Machine and Deep Learning with R", which is edited by Eva Bartz, Thomas Bartz-Beielstein, Martin Zaefferer, and Olaf Mersmann that will be published by Springer.
Panel Discussion	12:25
Closing Remarks	12:35

3. Important Dates

• Paper submission open: February 11, 2022
• Paper submission deadline: April 11, 2022
• Notification of Acceptance: April 25, 2022
• Camera-Ready Copy Due: May 2, 2022
• Author Registration deadline: May 2, 2022

If you have questions or suggestions, please contact Dr. Kononova at This email address is being protected from spambots. You need JavaScript enabled to view it. and Dr. Wang at This email address is being protected from spambots. You need JavaScript enabled to view it..

4. Instructions for Authors

Our workshop invites the submission of papers of at most 8 pages (excluding references), which should present original work that meets the high-quality standards of GECCO. Accepted papers appear in the ACM digital library as part of the Companion Proceedings of GECCO.

Each paper submitted to our workshop will be rigorously evaluated in a double-blind review process. Review criteria include the significance of the work, technical soundness, novelty, clarity, writing quality, relevance and, if applicable, sufficiency of information to permit replication.

Each paper accepted needs to have at least one author registered by the author registration deadline. By submitting a paper you agree to register and present at the conference in case the paper is accepted.

Papers must be submitted via the online submission system: https://ssl.linklings.net/conferences/gecco/.

As a published ACM author, you and your co-authors are subject to all ACM Publications Policies (https://www.acm.org/publications/policies/toc), including ACM's new Publications Policy on Research Involving Human Participants and Subjects (https://www.acm.org/publications/policies/research-involving-human-participants-and-subjects).

Please refer to https://gecco-2022.sigevo.org/Paper-Submission-Instructions for more detailed instructions.

5. Organizers

• Anna V. Kononova, LIACS, Leiden University, Leiden, The Netherlands
• Hao Wang, LIACS, Leiden University, Leiden, The Netherlands
• Michael Emmerich, LIACS, Leiden University, Leiden, The Netherlands
• Peter A.N. Bosman, CWI, Amsterdam, The Netherlands
• Daniela Zaharie, West University of Timisoara, Romania
• Fabio Caraffini, De Montfort University, Leicester, UK
• Johann Dreo, Pasteur Institute and CNRS, Computational Biology Departement, USR 3756, System Biology Group

6. Program Committee

• Thomas Bäck, Leiden University, The Netherlands
• Johannes Lengler, ETH Zürich, Switzerland
• Patryk Orzechowski, University of Pennsylvania, US
• Ales Zamuda, University of Maribor, Slevenia
• Jacob de Nobel, Leiden University, The Netherlands
• Per Kristian Lehre, University of Birmingham
• Tome Eftimov, Jozef Stefan Institute, Slovenia
• Mario Andrés Muñoz, The University of Melbourne, Australia
• Maxim Buzdalov, ITMO University, Russia
• Thomas Weise, Institute of Applied Optimization, Hefei University, Hefei, China
• Stefan Wagner, University of Applied Sciences Upper Austria, Hagenberg, Austria
• Carsten Witt, Technical University of Denmark
• Xingyi Zhang, Anhui University, Hefei, Chin
• Martin Krejca, Sorbonne Université, LIP6, Paris, France
• Michael Affenzeller, University of Applied Sciences Upper Austria
• Diederick Vermetten, Leiden University, The Netherlands
• Beham Andreas, University of Applied Sciences Upper Austria, Hagenberg, Austria
• Christine Zarges, Aberystwyth University, Wales, United Kingdom
• Pietro S. Oliveto, University of Sheffield, UK
• Dirk Thierens, Utrecht University, The Netherlands
• Katherine Malan, University of South Africa
• Arina Buzdalova, ITMO University, Russia
• Mike Preuss, Leiden University, The Netherlands

7. Chair Biographies