| Title | Organizers |
|---|---|
| AbstractSwarm Multi-Agent Logistics Competition |
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| Anytime Algorithms for Many-affine BBOB Functions |
|
| Automated Design Competition |
|
| Benchmarking Niching Methods for Multimodal Optimization |
|
| Dynamic Stacking Optimization in Uncertain Environments |
|
| Evolutionary Computation in the Energy Domain: Optimal PV System Allocation |
|
| Evolutionary Submodular Optimisation |
|
| Interpretable Control Competition |
|
| Machine Learning for Evolutionary Computation - Solving the Vehicle Routing Problems (ML4VRP) |
|
| Numerical Global Optimization Competition on GNBG-generated Test Suite |
|
| SpOC: Space Optimisation Competition |
|
| Star Discrepancy Competition @GECCO 2024 |
|
| Travelling Thief Problem Competition |
|
AbstractSwarm Multi-Agent Logistics Competition
Description:
This competition aims to motivate work in the broad field of multi-agent systems and logistics. We have prepared a benchmarking framework which allows the development of multi-agent swarms to process a variety of test environments. Those can be extremely diverse, highly dynamic and variable of size. The ultimate goal of this competition is to foster comparability of multi-agent systems in logistics-related problems (e. g., in hospital logistics). Many such problems have good accessibility and are easy to comprehend, but hard to solve. Problems of different diffculty have been designed to make the framework interesting for educational purposes. However, finding effcient solutions for different a priori unknown test environments remains a challenging task for practitioners and researchers alike.
Following these ideas, in the AbstractSwarm Multi-Agent Logistics Competition, participants must develop agents that are able to cooperatively solve different a priori unknown logistics problems. A logistics problem is given as a graph containing agents and stations. An agent can interact with the graph (1) by deciding which station to visit next, (2) by communicating with other agents, and (3) by retrieving a reward for its previous decision. While simulating a scenario, a timetable in the form of a Gantt-chart is created according to the decisions of all agents. Submissions will be ranked according to the total number of idle time of all agents in several different a priori unknown problem scenarios in conjunction with the number of iterations needed to come to the solution.
Submission deadline:
2024-05-30Official webpage:
https://abstractswarm.gitlab.io/abstractswarm_competition/Organizers:
Daan Apeldoorn
Daan Apeldoorn works for the Institute for Medical Biostatistics, Epidemiology and Informatics (IMBEI) in the Medical Informatics department at the University Medical Centre of the Johannes Gutenberg University Mainz, Germany. His research focuses on the extraction and exploitation of knowledge bases in the context of learning agents. He is also active in the field of multi-agent systems with application in (hospital) logistics. In the past, he worked as a scientific staff member at the TU Dortmund University and the University of Koblenz-Landau.
Alexander Dockhorn
Alexander Dockhorn is Junior professor for Computer Science at the Gottfried Wilhelm Leibniz University Hannover. His research is focused on the topics of machine learning, decision-making, AI in games, and game development. He is an active member of the Institute of Electrical and Electronics Engineers (IEEE). Previously, he has been the Chair of the IEEE CIS Competitions Subcommittee and organized the Hearthstone AI competition as well as several other competitions. Personal webpage: https://adockhorn.github.io/
Torsten Panholzer
Torsten Panholzer is head of the division Medical Informatics at the Institute for Medical Biostatistics, Epidemiology and Informatics (IMBEI) at the University Medical Centre Mainz, Germany. He studied natural sciences and graduated as PhD at the Johannes Gutenberg University Mainz. His research focus is on system and data integration, identity management and artificial intelligence.
Anytime Algorithms for Many-affine BBOB Functions
Description:
The many-affine BBOB function suite MA-BBOB extends the classic BBOB benchmark suite of the COCO environment by combining its 24 base functions. With this competition, we solicit algorithms designed to optimize anytime performance on the MA-BBOB functions.
The MA-BBOB suite, introduced in https://arxiv.org/abs/2306.10627, is available as part of the IOHprofiler, https://iohprofiler.github.io/.
Participation is simple: we provide around 1,000 training instances for which we have already evaluated some baseline algorithms. You are free to use these and you can use as many training instances from the MA-BBOB framework as you like. To participate in the competition, you submit a link to your code that we then execute on the test instances, sampled from the same distribution as the training instances. For details about the computational budget, problem dimensions, test instances, etc., please confer the dedicated competition website at LINK TO BE ADDED.
We particularly welcome submissions that are accompanied by a short paper or technical report (up to 2 pages following the GECCO formatting requirements) describing the key approaches used by the submitted algorithms. Each team can submit a total of up to three algorithms.
Submission deadline:
2024-06-12Official webpage:
https://iohprofiler.github.io/competitions/stardiscr; https://cs.adelaide.edu.au/~optlog/CompetitionESO2023.php (to be updated soon).Organizers:
Diederick Vermetten
Diederick Vermetten is a PhD student at LIACS. He is part of the core development team of IOHprofiler, with a focus on the IOHanalyzer. His research interests include benchmarking of optimization heuristics, dynamic algorithm selection and configuration as well as hyperparameter optimization.
Pascal Kerschke
Pascal Kerschke is professor of Big Data Analytics in Transportation at TU Dresden, Germany. His research interests cover various topics in the context of benchmarking, data science, machine learning, and optimization - including automated algorithm selection, Exploratory Landscape Analysis, as well as continuous single- and multi-objective optimization. Moreover, he is the main developer of flacco, co-authored further R-packages such as smoof and moPLOT, co-organized numerous tutorials and workshops in the context of Exploratory Landscape Analysis and/or benchmarking, and is an active member of the Benchmarking Network and the COSEAL group.
Carola Doerr
Carola Doerr, formerly Winzen, is a CNRS research director at Sorbonne Université in Paris, France. Carola's main research activities are in the analysis of black-box optimization algorithms, both by mathematical and by empirical means. Carola is associate editor of IEEE Transactions on Evolutionary Computation, ACM Transactions on Evolutionary Learning and Optimization (TELO) and board member of the Evolutionary Computation journal. She is/was program chair for the BBSR track at GECCO 2024, the GECH track at GECCO 2023, for PPSN 2020, FOGA 2019 and for the theory tracks of GECCO 2015 and 2017. She has organized Dagstuhl seminars and Lorentz Center workshops. Together with Pascal Kerschke, Carola leads the 'Algorithm selection and configuration' working group of COST action CA22137. Carola's works have received several awards, among them the CNRS bronze medal, the Otto Hahn Medal of the Max Planck Society, best paper awards at GECCO, CEC, and EvoApplications.
Automated Design Competition
Description:
The competition concerns the development of an efficient algorithm to optimize active 3D designs (i.e., simulated agents or robots). The simulation environment is Framsticks, and participants have a Python binding available to the native simulator library, so algorithms should be implemented entirely in Python. Technical details are described on the dedicated competition web page (link below).
The goal of the competition is to propose an algorithm that will discover agents whose center of gravity moves in the desired way in different environments used during optimization. The properties of the desired movement are defined by the fitness function (unknown to participants); examples of such movements are: following a specific path in 3D, swinging or jumping. The set of parameters that define each environment (such as gravity, water level, terrain, and initial agent rotation) is published, but their values will be set during the evaluation phase. Each submitted algorithm will be tested to optimize agents in 10 different settings (environments and desired movements). These settings will be the same for all participants.
Each submission must contain a short description of the algorithm and a standalone Python source code. The source code can use any freely and publicly available libraries, but participants should take care to describe the way dependencies are supposed to be installed to allow the organizers to run their algorithm. The algorithm will not have access to the Internet.
Submission deadline:
2024-06-29Official webpage:
http://www.framsticks.com/gecco-competitionOrganizers:
Maciej Komosinski
Maciej Komosinski is an associate professor at the Institute of Computing Science, Poznan University of Technology. His professional fields of interest include modeling of life processes and life forms, evolutionary algorithms and new approaches to optimization, simulation (artificial life, evolution, learning, complex adaptive systems, collective and multi-agent systems, virtual worlds), artificial intelligence, neural networks, and machine learning. His research is interdisciplinary and concerns the above mentioned topics as well as biology, medicine, biomedical applications of computer sciences, and cognitive science.
Konrad Miazga
Konrad Miazga is a research assistant at the Institute of Computing Science, Poznan University of Technology. His main research interests include metaheuristic optimization, machine learning, artificial intelligence and artificial life.
Agnieszka Mensfelt
Agnieszka Mensfelt is a research assistant at the Institute of Computing Science, Poznan University of Technology. Her scientific interests include computational and artificial intelligence, simulation, optimization, machine learning and cognitive science.
Benchmarking Niching Methods for Multimodal Optimization
Description:
This competition aims to provide a fair platform for unbiased, comprehensive, and informative evaluation and comparison of methods for box-constrained continuous multimodal optimization. It employs a new set of fully scalable and tunable test problems that simulate diverse challenges associated with multimodal optimization. These test problems were designed to address some drawbacks of the well-known CEC’2013 test suite for benchmarking niching methods for multimodal optimization \cite{li2013benchmark}. The new test suite aims to not only differentiate relevant methods but also pinpoint their strengths and weaknesses more reliably.
A user-friendly platform for the test problems is provided which can easily be integrated with almost any existing multimodal optimization method. For more details on the test problems, evaluation criteria, computer codes, and submission of results, please refer to the official website of the competition.
Submission deadline:
2024-06-30Official webpage:
https://sites.google.com/view/evopt/projects/gecco2024-mmoOrganizers:
Ali Ahrari
Ali Ahrari is a Lecturer at the University of New South Wales, Australia. His current position is funded by the Australia Research Council through the Discovery Early Career Researcher Award. His research concentrates on evolutionary algorithms and their application to engineering optimization. He is a member of the IEEE CIS Task Force on Multimodal Optimization.
Jonathan Fieldsend
Jonathan Fieldsend, Editor-in-Chief University of Exeter, UK is Professor of Computational Intelligence at the University of Exeter. He has a degree in Economics from Durham University, a Masters in Computational Intelligence from the University of Plymouth and a PhD in Computer Science from the University of Exeter. He has over 100 peer-reviewed publications in the evolutionary computation and machine learning domains, with particular interests in multiple-objective optimisation, and the interface between optimisation and machine learning. Over the years, he has been a co-organiser of a number of different Workshops at GECCO (VizGEC, SAEOpt and EAPwU), as well as EMO Track Chair in GECCO 2019 and GECCO 2020. He is an Associate Editor of IEEE Transactions on Evolutionary Computation, and ACM Transactions on Evolutionary Learning and Optimization, and on the Editorial Board of Complex and Intelligence Systems. He is a vice-chair of the IEEE Computational Intelligence Society (CIS) Task Force on Data-Driven Evolutionary Optimisation of Expensive Problems, and sits on the IEEE CIS Task Force on Multi-modal Optimisation and the IEEE CIS Task Force on Evolutionary Many-Objective Optimisation.
Mike Preuss
Mike Preuss is assistant professor at LIACS, the Computer Science department of Leiden University. He works in AI, namely game AI, natural computing, and social media computing. Mike received his PhD in 2013 from the Chair of Algorithm Engineering at TU Dortmund, Germany, and was with ERCIS at the WWU Muenster, Germany, from 2013 to 2018. His research interests focus on the field of evolutionary algorithms for real-valued problems, namely on multi-modal and multi-objective optimization, and on computational intelligence and machine learning methods for computer games. Recently, he is also involved in Social Media Computing, and he is publications chair of the upcoming multi-disciplinary MISDOOM conference 2019. He is associate editor of the IEEE ToG journal and has been member of the organizational team of several conferences in the last years, in various functions, as general co-chair, proceedings chair, competition chair, workshops chair.
Xiaodong Li
Xiaodong Li received his B.Sc. degree from Xidian University, Xi'an, China, and Ph.D. degree in Artificial Intelligence from University of Otago, Dunedin, New Zealand, respectively. He is a Professor with the School of Computing Technologies, RMIT University, Melbourne, Australia. His research interests include machine learning, evolutionary computation, neural networks, deep learning, data analytics, multiobjective optimization, operational research, and swarm intelligence. He served as an Associate Editor of the IEEE Transactions on Evolutionary Computation, Swarm Intelligence (Springer), and International Journal of Swarm Intelligence Research. He is a founding member of IEEE CIS Task Force on Swarm Intelligence, a former vice-chair of IEEE Task Force on Multi-modal Optimization, and a former chair of IEEE CIS Task Force on Large Scale Global Optimization. He is the recipient of 2013 ACM SIGEVO Impact Award and 2017 IEEE CIS IEEE Transactions on Evolutionary Computation Outstanding Paper Award. He is an IEEE Fellow.
Michael G. Epitropakis
Michael G. Epitropakis received his B.S., M.S., and Ph.D. degrees from the Department of Mathematics, University of Patras, Patras, Greece. Currently, he is a Lecturer in Foundations of Data Science at the Data Science Institute and the Department of Management Science, Lancaster University, Lancaster, UK. His current research interests include computational intelligence, evolutionary computation, swarm intelligence, machine learning and search≠ based software engineering. He has published more than 35 journal and conference papers. He is an active researcher on Multi≠modal Optimization and a co≠-organized of the special session and competition series on Niching Methods for Multimodal Optimization. He is a member of the IEEE Computational Intelligence Society and the ACM SIGEVO.
Dynamic Stacking Optimization in Uncertain Environments
Description:
Stacking problems are central to multiple billion-dollar industries. The container shipping industry needs to stack millions of containers every year. In the steel industry the stacking of steel slabs, blooms, and coils needs to be carried out efficiently, affecting the quality of the final product. The immediate availability of data – thanks to the continuing digitalization of industrial production processes – makes the optimization of stacking problems in highly dynamic environments feasible.
There are two tracks in this competition, same as in the last competition.
Rolling Mill (RM): In the first track, a dynamic environment is provided that represents a simplified real-world stacking scenario. Blocks arrive continuously at a fixed arrival locations from which they have to be removed swiftly. If an arrival location is full, the arrival of additional blocks on this location is not possible. To avoid such a state, there is a range of buffer stacks that may be used to store blocks. Each block must eventually be delivered to a predefined handover location, after which it is further processed by a respective rolling mill. The handover location is defined by the rolling mill program, of which only a few next entries (i.e. block to be delivered) are known. Blocks placed on a handover location other than then one specified in the rolling mill program count as rolling mill mess-ups. Two crane must be tasked to move blocks from arrival to handover locations. Both share a single crane lane, i.e. they cannot overtake each other. Furthermore, location access of cranes is limited, as one crane can only access all arrival and buffer locations and the other crane can only access all buffer and handover locations. Finally, the cranes have a carrying capacity greater than one, i.e. multiple blocks can be moved at a time, which represents an additional challenge for the solver. A range of performance indicators will be used to determine the winner.
Crane Scheduling (CS): The second track features another simplified scenario which focuses mainly on the scheduling aspect of real-world crane operations. It features various arrival and handover locations and much more buffer locations to choose from. Blocks enter and leave the warehouse at arrival and handover locations, respectively, and two cranes can be manipulated to move blocks around. Both cranes share a single lane but this time can access every location. The scenario creates move requests which determine blocks that have to be picked up at arrival or dropped off at handover locations, and also creates predefined crane moves to fulfill these move requests. An external optimizer must create crane schedules to fulfill these requests. It can do so by scheduling the predefined moves (which use naïve stacking rules and thus are not optimal), but it can also create optimized crane moves and schedule those. Therefore, the second scenario integrates three optimization problems: stacking (if custom moves are created), assignment and scheduling.
The dynamic environments are implemented in form of a realtime simulation which provides the necessary change events. The simulation runs in a separate process and publishes its world state and change events via message queuing (ZeroMQ), and also listens for crane orders. Thus, control algorithms may be implemented as standalone applications using a wide range of programming languages. Exchanged messages are encoded using protocol buffers – again libraries are available for a large range of programming languages. As in the 2023 competition a website will be used that participants can use to create experiment and test their solvers. In addition, the simulation models are available at GitHub for offline testing and development at https://github.com/dynstack/dynstack . We gladly accept pull requests for new starter kits, existing algorithms and approaches, as well as additions to the bibliography on works that have used the competition for scientific research.
Submission deadline:
2024-06-29Official webpage:
https://dynstack.adaptop.atOrganizers:
Johannes Karder
Johannes Karder received his master's degree in software engineering in 2014 from the University of Applied Sciences Upper Austria and is a research associate in the Heuristic and Evolutionary Algorithms Laboratory at the Research Center Hagenberg. His research interests include algorithm theory and development, simulation-based optimization and optimization networks. He is a member of the HeuristicLab architects team. He is currently pursuing his PhD in technical sciences at the Johannes Kepler University, Linz, where he conducts research on the topic of dynamic optimization problems.
Stefan Wagner
Stefan Wagner received his MSc in computer science in 2004 and his PhD in technical sciences in 2009, both from Johannes Kepler University Linz, Austria. From 2005 to 2009 he worked as associate professor for software project engineering and since 2009 as full professor for complex software systems at the Campus Hagenberg of the University of Applied Sciences Upper Austria. From 2011 to 2018 he was also CEO of the FH OÖ IT GmbH, which is the IT service provider of the University of Applied Sciences Upper Austria. Dr. Wagner is one of the founders of the research group Heuristic and Evolutionary Algorithms Laboratory (HEAL) and is project manager and head architect of the open-source optimization environment HeuristicLab. He works as project manager and key researcher in several R&D projects on production and logistics optimization and his research interests are in the area of combinatorial optimization, evolutionary algorithms, computational intelligence, and parallel and distributed computing.
Bernhard Werth
Bernhard Werth received his MSc in computer science in 2016 from Johannes Kepler University Linz, Austria. He works as a researcher at the R&D facility at University of Applied Sciences Upper Austria, Hagenberg Campus. Mr Werth is contributor to the open source software environment HeuristicLab and member of the Heuristic and Evolutionary Algorithms Laboratory (HEAL) research group led by Dr. Affenzeller. He has authored and co-authored several papers concerning evolutionary algorithms, fitness landscape analysis, surrogate-assisted optimization and data quality monitoring.
Andreas Beham
Andreas Beham received his MSc in computer science in 2007 and his PhD in engineering sciences in 2019, both from Johannes Kepler University Linz, Austria. He works as assistant professor at the R&D facility at University of Applied Sciences Upper Austria, Hagenberg Campus and is leading several funded research projects. Dr. Beham is co-architect of the open source software environment HeuristicLab and member of the Heuristic and Evolutionary Algorithms Laboratory (HEAL) research group led by Dr. Affenzeller. He has published more than 80 documents indexed by SCOPUS and applied evolutionary algorithms, metaheuristics, mathematical optimization, data analysis, and simulation-based optimization in industrial research projects. His research interests include applying dynamic optimization problems, algorithm selection, and simulation-based optimization and innovization approaches in practical relevant projects.
Evolutionary Computation in the Energy Domain: Optimal PV System Allocation
Description:
Following the success of the previous editions at IEEE PES; CEC; GECCO, WCCI, we are launching another challenging edition of competition at major conferences in the field of computational intelligence. This edition of GECCO 2024 competition proposes the following one track in the energy domain:
Optimal PV systems allocation in an unbalanced distribution network. As photovoltaic (PV) penetration into distribution networks continues to grow, the transition from passive to active networks has brought about a new level of complexity in terms of planning and operation. The optimal PV allocation (sizing and location) is challenging because it is a mixed-integer non-linear programming with three-phase non-linear unbalanced power flow equations. The objective is to find the optimal PV systems allocation that maximize the PV penetration within a predefined planning horizon while satisfying operation constraints such as voltage limits. The IEEE 37-bus test feeder is considered as case study.
Note: The track is developed to run under the same framework of past competitions.
Submission deadline:
2024-06-20Official webpage:
https://www.gecad.isep.ipp.pt/ERM-competitionsOrganizers:
Joao Soares
João Soares has a B.Sc. in computer science (2008) and a master (2011) in Electrical Engineering by Polytechnic of Porto. He attained his Ph.D. degree in Electrical and Computer Engineering at UTAD university (2017). He is a researcher at ISEP/GECAD and his research interests include optimization in power and energy systems, including heuristic, hybrid and classical optimization.
Fernando Lezama
Fernando Lezama received the Ph.D. in ICT from the ITESM, Mexico, in 2014. Since 2017, he is a researcher at GECAD, Polytechnic of Porto, where he contributes in the application of computational intelligence (CI) in the energy domain. Dr. Lezama is part of the National System of Researchers of Mexico since 2016, Chair of the IEEE CIS TF 3 on CI in the Energy Domain, and has been involved in the organization of special sessions, workshops, and competitions (at IEEE WCCI, IEEE CEC and ACM GECCO), to promote the use of CI to solve complex problems in the energy domain.
José Almeida
José Almeida has a degree in Electrical and Computer Engineering (2019) from Polytechnic Institute of Porto, Porto. He is currently working towards the M.Sc. degree in electrical engineering from the Polytechnic Institute of Porto (ISEP/IPP), Porto, Portugal. He is a Researcher with GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, ISEP/IPP. His research interests include optimization in power and energy systems; electric vehicles; smart grids; distributed energy resource management; and electricity markets.
Wenlei Bai
Wenlei Bai (M’15) received Ph. D. degree in Electrical and Computer Engineering in 2017 from Baylor University. He is currently a principal application developer at Oracle America, Inc. to conduct research and development on advanced distribution management system. His research interests include power system planning and operation, electricity market, supply chain optimization, inteligente control, and AI in power systems.
Thomas Bäck
Thomas Bäck is Full Professor of Computer Science at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University, The Netherlands, where he is head of the Natural Computing group since 2002. He received his PhD (adviser: Hans-Paul Schwefel) in computer science from Dortmund University, Germany, in 1994, and then worked for the Informatik Centrum Dortmund (ICD) as department leader of the Center for Applied Systems Analysis. From 2000 - 2009, Thomas was Managing Director of NuTech Solutions GmbH and CTO of NuTech Solutions, Inc. He gained ample experience in solving real-life problems in optimization and data mining through working with global enterprises such as BMW, Beiersdorf, Daimler, Ford, Honda, and many others. Thomas Bäck has more than 350 publications on natural computing, as well as two books on evolutionary algorithms: Evolutionary Algorithms in Theory and Practice (1996), Contemporary Evolution Strategies (2013). He is co-editor of the Handbook of Evolutionary Computation, and most recently, the Handbook of Natural Computing. He is also editorial board member and associate editor of a number of journals on evolutionary and natural computing. Thomas received the best dissertation award from the German Society of Computer Science (Gesellschaft für Informatik, GI) in 1995 and the IEEE Computational Intelligence Society Evolutionary Computation Pioneer Award in 2015.
Zita Vale
Zita Vale received the Ph.D. degree in electrical and computer engineering from the University of Porto, Porto, Portugal, in 1993. She is currently a Professor with the Polytechnic Institute of Porto, Porto. Her research interests focus on artificial intelligence applications, smart grids, electricity markets, demand response, electric vehicles, and renewable energy sources.
Evolutionary Submodular Optimisation
Description:
Submodular functions play a key role in the area of optimisation as they allow to model many real-world optimisation problems. Submodular functions model a wide range of problems where the benefit of adding solution components diminishes with the addition of elements. They form an important class of optimization problems, and are extensively studied in the literature. Problems that may be formulated in terms of submodular functions include influence maximization in social networks, maximum coverage, maximum cut in graphs, sensor placement problem, and sparse regression. In recent years, the design and analysis of evolutionary algorithms for submodular optimisation problems has gained increasing attention in the evolutionary computation and artificial intelligence community.
The aim of the competition is to provide a platform for researchers working evolutionary computing methods and interested in benchmarking them on a wide class of combinatorial optimization problems. The competition will benchmark evolutionary computing techniques for submodular optimisation problems and enable performance comparison for this type of problems. It provides an idea vehicle for researchers and students to design new algorithms and/or benchmark their existing approaches on a wide class of combinatorial optimization problems captured by submodular functions.
Submission deadline:
2024-06-18Official webpage:
https://cs.adelaide.edu.au/~optlog/CompetitionESO2024.phpOrganizers:
Aneta Neumann
Aneta Neumann is a researcher in the School of Computer and Mathematical Sciences at the University of Adelaide, Australia, and focuses on real world problems using evolutionary computation methods. She is also part of the Integrated Mining Consortium at the University of Adelaide. Aneta graduated in Computer Science from the Christian-Albrechts-University of Kiel, Germany, and received her PhD from the University of Adelaide, Australia. She served as the co-chair of the Real-World Applications track at GECCO 2021 and GECCO 2022, and is a co-chair of the Genetic Algorithms track at GECCO 2023. Her main research interests are bio-inspired computation methods, with a particular focus on dynamic and stochastic multi-objective optimization for real-world problems that occur in the mining industry, defence, cybersecurity, creative industries, and public health.
Saba Sadeghi Ahouei
Saba Sadeghi Ahouei is a PhD student in computer science at the University of Adelaide. She received her MSc in Industrial Engineering at Sharif University of Technology in 2021. Her main research interests are Stochastic Optimization, Chance-constrained Optimization, Evolutionary Algorithms, Algorithm Selection and Configuration, and Benchmarking Optimization Algorithms.
Diederick Vermetten
Diederick Vermetten is a PhD student at LIACS. He is part of the core development team of IOHprofiler, with a focus on the IOHanalyzer. His research interests include benchmarking of optimization heuristics, dynamic algorithm selection and configuration as well as hyperparameter optimization.
Jacob de Nobel
Jacob de Nobel is a PhD student at LIACS, and is currently one of the core developers for the IOHexperimenter. His research concerns the real world application of optimization algorithms for finding better speech encoding strategies for cochlear implants, which are neuroprosthesis for people with profound hearing loss.
Thomas Bäck
Thomas Bäck is Full Professor of Computer Science at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University, The Netherlands, where he is head of the Natural Computing group since 2002. He received his PhD (adviser: Hans-Paul Schwefel) in computer science from Dortmund University, Germany, in 1994, and then worked for the Informatik Centrum Dortmund (ICD) as department leader of the Center for Applied Systems Analysis. From 2000 - 2009, Thomas was Managing Director of NuTech Solutions GmbH and CTO of NuTech Solutions, Inc. He gained ample experience in solving real-life problems in optimization and data mining through working with global enterprises such as BMW, Beiersdorf, Daimler, Ford, Honda, and many others. Thomas Bäck has more than 350 publications on natural computing, as well as two books on evolutionary algorithms: Evolutionary Algorithms in Theory and Practice (1996), Contemporary Evolution Strategies (2013). He is co-editor of the Handbook of Evolutionary Computation, and most recently, the Handbook of Natural Computing. He is also editorial board member and associate editor of a number of journals on evolutionary and natural computing. Thomas received the best dissertation award from the German Society of Computer Science (Gesellschaft für Informatik, GI) in 1995 and the IEEE Computational Intelligence Society Evolutionary Computation Pioneer Award in 2015.
Interpretable Control Competition
Description:
Control systems are pervasive in modern technology.
Safety-critical applications, in particular, require efficient yet interpretable control systems to ensure trustworthiness.
However, there is a prevalent reliance on opaque systems in the field, prioritizing performance over interpretability, compounded by a lack of objective metrics to assess interpretability.
The goal of this competition is to ignite the research domain of interpretable control, by establishing a basis of comparison for performance and interpretability trade-offs.
In addition, we aim to identify key characteristics that enhance control policy interpretability through the insights of human evaluators.
The competition has two tracks, a continuous control track and a discrete control track.
The former involves a robot locomotion task, while the second encompasses the 2048 game.
Participants can take part in either or both of the tracks.
They will have the freedom to apply their preferred methods to generate and interpret policies that effectively address the proposed task.
However, we promote the inclusion of EC techniques into either the policy generation or explanation process, as a valuable component of addressing the proposed task.
The submissions will be evaluated according to their performance and their interpretability.
We will measure the performance by simulating the submitted policy, whereas the interpretability will be appraised by a panel of judges.
For more details about the competition and the submission process, see https://giorgia-nadizar.github.io/interpretable-control-competition/ or join our Discord server https://discord.gg/dA8jpFVa9t.
Submission deadline:
2024-06-11Official webpage:
https://giorgia-nadizar.github.io/interpretable-control-competition/Organizers:
Giorgia Nadizar
Giorgia Nadizar is a third year PhD student at the University of Trieste, Italy. Her research interests lie at the intersection of embodied AI and explainable/interpretable AI.
Luigi Rovito
Luigi Rovito is a third year PhD student at the University of Trieste, Italy. His research interests are genetic programming for cryptography and interpretable ML.
Dennis G. Wilson
Dennis G. Wilson is an Assistant Professor of AI and Data Science at ISAE-SUPAERO in Toulouse, France. He obtained his PhD at the Institut de Recherche en Informatique de Toulouse (IRIT) on the evolution of design principles for artificial neural networks. Prior to that, he worked in the Anyscale Learning For All group in CSAIL, MIT, applying evolutionary strategies and developmental models to the problem of wind farm layout optimization. His current research focuses on genetic programming, neural networks, and the evolution of learning.
Eric Medvet
Eric Medvet is an Associate Professor at the University of Trieste, Italy. His research interests include embodied AI, artificial life, and evolutionary optimization.
Machine Learning for Evolutionary Computation - Solving the Vehicle Routing Problems (ML4VRP)
Description:
This competition aims to bring together the latest advances of machine learning-assisted evolutionary algorithms for solving vehicle routing problems (VRP). Current relevant research has collected a large amount of data designing evolutionary algorithms, which captures rich knowledge in evolutionary computation. However, this data is often discarded or not further investigated in the literature. This includes solutions of different features to inform or drive the evolution/optimisation, data on evolutionary algorithms of different settings and different operators/heuristics, and data on the search space or fitness evaluation. This provides an excellent new problem domain for the machine learning community to enhance evolutionary computation.
Variants of VRP provide an ideal testbed to enable performance comparison of machine learning-assisted evolutionary computation. Fostering, reusing, and benchmarking the rich knowledge building ML4VRP remains a challenge for researchers across disciplines, however, is highly rewarding to further advances to human-designed evolutionary computation.
Submission deadline:
2024-06-13Official webpage:
https://sites.google.com/view/ml4vrpOrganizers:
Rong Qu
Prof. Rong Qu is a Professor at the University of Nottingham. Her main research interests include the modelling and optimisation of combinatorial optimisation problems using evolutionary computation, integrated with operational research and artificial intelligence. Prof. Qu is an Associate Editor at five international journals. She has been awarded the 2022 Leverhulme Senior Research Fellowship by The Royal Society.
Nelishia Pillay
Nelishia Pillay is a Professor at the University of Pretoria, South Africa. She holds the Multichoice Joint-Chair in Machine Learning and SARChI Chair in Artificial Intelligence for Sustainable Development. She is chair of the IEEE Technical Committee on Intelligent Systems Applications, IEEE CIS WCI sub-commitee and the IEEE Task Force on Automated Algorithm Design, Configuration and Selection. Her research areas include hyper-heuristics, automated design of machine learning and search techniques, combinatorial optimization, genetic programming, genetic algorithms and deep learning for and more generally machine learning and optimization for sustainable development. These are the focus areas of the NICOG (Nature-Inspired Computing Optimization) research group which she has established.
Weiyao Meng
Weiyao Meng is a Teaching Associate in the School of Computer Science at the University of Nottingham. She received her PhD in Computer Science from the University of Nottingham in 2023. Her main research interests focus on automated algorithm design and data-driven decision support for sustainable transportation. She has also served as a reviewer for leading journals including IEEE Transactions on Evolutionary Computation, Engineering Applications of Artificial Intelligence, and Journal of the Operational Research Society. She is also a member of the IEEE Task Force on Automated Algorithm Design, Configuration and Selection and the IEEE Task Force on Evolutionary Scheduling and Combinatorial Optimisation at the IEEE Computational Intelligence Society.
Numerical Global Optimization Competition on GNBG-generated Test Suite
Description:
This competition invites researchers to test the mettle of their global optimization algorithms against a meticulously curated set of 24 problem instances from the Generalized Numerical Benchmark Generator (GNBG). This test suite spans a wide array of problem terrains, from smooth unimodal landscapes to intricately rugged multimodal realms. The suite encompasses:
• Unimodal instances (f_1 to f_6),
• Single-component multimodal instances (f_7 to f_15), and
• Multi-component multimodal instances (f_16 to f_24).
With challenges that include various degrees of modality, ruggedness, asymmetry, conditioning, variable interactions, basin linearity, and deceptiveness, the competition provides a robust assessment of algorithmic capabilities. But this competition is not just about finding optimal solutions. It is about understanding the journey to these solutions. Participants will decipher how algorithms navigate deceptive terrains, traverse valleys, and adapt to the unique challenges posed by each instance. In essence, it is a quest for deeper insights into optimization within complex numerical landscapes. We warmly invite researchers to partake in this competition and subject their global optimization algorithms to this rigorous test.
Submission deadline:
2024-06-28Official webpage:
https://competition-hub.github.io/GNBG-Competition/Organizers:
Amir H Gandomi
Amir H. Gandomi is a Professor of Data Science and an ARC DECRA Fellow at the Faculty of Engineering & Information Technology, University of Technology Sydney. He is also affiliated with Obuda University, Budapest, as a Distinguished Professor. Prior to joining UTS, Prof. Gandomi was an Assistant Professor at Stevens Institute of Technology, and a distinguished research fellow at BEACON center, Michigan State University. Prof. Gandomi has published over three hundred journal papers and 12 books which collectively have been cited 44,000+ times (H-index = 94). He has been named as one of the most influential scientific minds and received the Highly Cited Researcher award (top 1% publications and 0.1% researchers) from Web of Science for six consecutive years, from 2017 to 2022. In the recent most impactful researcher list, done by Stanford University and released by Elsevier, Prof Amir H Gandomi is ranked among the top 1,000 researchers (top 0.01%) and top 50 researchers in AI and Image Processing subfield in 2021! He also ranked 17th in GP bibliography among more than 15,000 researchers. He has received multiple prestigious awards for his research excellence and impact, such as the 2023 Achenbach Medal and the 2022 Walter L. Huber Prize, the highest-level mid-career research award in all areas of civil engineering. He has served as associate editor, editor, and guest editor in several prestigious journals, such as AE of IEEE Networks and IEEE IoTJ. Prof Gandomi is active in delivering keynotes and invited talks. His research interests are global optimisation and (big) data analytics using machine learning and evolutionary computations in particular.
Kalyanmoy Deb
Kalyanmoy Deb is Koenig Endowed Chair Professor at Department of Electrical and Computer Engineering in Michigan State University, USA. Prof. Deb's research interests are in evolutionary optimization and their application in multi-criterion optimization, modeling, and machine learning. He has been a visiting professor at various universities across the world including University of Skövde in Sweden, Aalto University in Finland, Nanyang Technological University in Singapore, and IITs in India. He was awarded IEEE Evolutionary Computation Pioneer Award for his sustained work in EMO, Infosys Prize, TWAS Prize in Engineering Sciences, CajAstur Mamdani Prize, Distinguished Alumni Award from IIT Kharagpur, Edgeworth-Pareto award, Bhatnagar Prize in Engineering Sciences, and Bessel Research award from Germany. He is fellow of IEEE, ASME, and three Indian science and engineering academies. He has published over 548 research papers with Google Scholar citation of over 149,000 with h-index 123. He is in the editorial board on 18 major international journals. More information about his research contribution can be found from https://www.coin-lab.org.
Danial Yazdani
Danial Yazdani received his Ph.D. degree in computer science from Liverpool John Moores University, Liverpool, United Kingdom, in 2018. He is a data and system analyst and algorithm and simulation designer with 10+ years of research experience in academia. He is currently a Research Fellow at the Data Science Institute, University of Technology Sydney, Sydney, Australia. Prior to that, he was a Research Assistant Professor with the Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China. His current research interests include learning and optimization in dynamic environments. He is an invited committee member of the IEEE Task Force on Evolutionary Computation in Dynamic and Uncertain Environments and the IEEE Task Force on Large-Scale Global Optimization. He was a recipient of the 2023 IEEE CIS Outstanding PhD Dissertation Award, the Best Thesis Award from the Faculty of Engineering and Technology, Liverpool John Moores University, and the SUSTech Presidential Outstanding Postdoctoral Award from the Southern University of Science and Technology.
Rohit Salgotra
Rohit Salgotra is an Adjunct Researcher at the AGH University of Krakow in Poland. He specializes in Nature-Inspired Computing and has authored over 40 Science Citation Indexed (SCI) publications. Dr. Salgotra has been listed among Stanford University’s Top 2% Most Influential Scientists for the years 2021–2022, within the category of Indian researchers.
Before joining AGH, Dr. Salgotra was a Research Officer at Swansea University, where he conducted studies on the socio-economic aspects of the COVID-19 pandemic. Dr. Salgotra is an Academic Editor for "Mathematical Problems in Engineering" and a reviewer for several journals, including "IEEE Transactions on Evolutionary Computing" and "Swarm and Evolutionary Computing," among more than twenty other SCI journals.
Mohammad Nabi Omidvar
Nabi Omidvar is a University Academic Fellow (Assistant Professor) with the School of Computing, University of Leeds, and Leeds University Business School, UK. He is an expert in large-scale global optimization and is currently a senior member of the IEEE and the chair of IEEE Computational Intelligence Society's Taskforce on Large-Scale Global Optimization. He has made several award winning contributions to the field including the state-of-the-art variable interaction analysis algorithm which won the IEEE Computational Intelligence Society's best paper award in 2017. He also coauthored a paper which won the large-scale global optimization competition in the IEEE Congress on Evolutionary Computation in 2019. Dr. Omidvar's current research interests are high-dimensional (deep) learning and the applications of artificial intelligence in financial services.
SpOC: Space Optimisation Competition
Description:
Welcome to the third edition of the Space Optimization Competition (SpOC), hosted by ESA's Advanced Concepts Team. This competition serves as a driving force for the application of evolutionary techniques to address optimization challenges relevant to mission analysis and space-related endeavors. Participants are tasked with solving optimization problems inspired by realistic mission scenarios set in the distant future.
The primary objective of SpOC is to bridge the gap between the engineering-focused space community and researchers specializing in genetic and evolutionary algorithms. We aim to explore innovative solutions for mission-critical tasks through the application of these algorithms, while simultaneously presenting fresh and challenging benchmark sets to the research community.
Commencing on April 1, 2024, participants will have a three-month window to tackle the presented challenges and vie for the top position on the SpOC leaderboard. The competition will be hosted on the Optimise platform (https://optimise.esa.int/) provided by the European Space Agency, offering access to benchmark scenarios, evaluation tools, and a publicly accessible leaderboard.
Submission deadline:
2024-06-29Official webpage:
https://optimise.esa.int/Organizers:
Max Bannach
Emmanuel Blazquez
Emmanuel Blazquez graduated in Aerospace Engineering from the Institut Supérieur de l’Aéronautique et de l’Espace (ISAE-SUPAERO) in 2017 and pursued a second master in Space
Engineering at Politecnico di Milano graduating with a master thesis on GPU-accelerated N-body asteroid aggregation models. He was awarded a Ph.D. by the University of Toulouse Paul-Sabatier in 2021 for his work on rendezvous optimization and GNC design on cislunar near-rectilinear Halo orbits, which was the result of a collaboration between the European Space Agency, ISAE-SUPAERO and Airbus Defence and Space. Emmanuel recently joined the Advanced Concepts Team of ESA as a research fellow in advanced mission analysis with a focus on on-board real-time optimization asssisted by Artificial Intelligence. His research interests also include Autonomous Guidance and Control architectures, Multibody Astrodynamics and Global trajectory optimization.
Dario Izzo
Dr. Izzo graduated in Aeronautical Engineering from the University Sapienza of Rome in 1999 and later obtained a second master in “Satellite Platforms” at the University of Cranfield in the UK and a Ph.D. in Mathematical Modelling in 2003, at the University Sapienza of Rome. In 2004, he moved to the European Space Agency (ESA) in the Netherlands as a research fellow in Mission Analysis Dr. Izzo is now heading the Advanced Concepts Team and manageing its interface to the rest of ESA. During the years spent with tha ACT, he has led studies in interplanetary trajectory design and artificial intelligence and he took part in several other innovative researches on diverse fields. He started the Global Trajectory Optimization Competitions events, the ESA’s Summer of Code in Space, and the Kelvins competition platform (https://kelvins.esa.int/). Dr. Izzo has published more than 150 papers in journals, conferences and books. In GECCO 2013, he received the Humies Gold Medal for the work on grand tours of the galilean moons and, the following year, he won the 8th edition of the Global Trajectory Optimization Competition, organized by NASA/JPL, leading a mixed team of ESA/JAXA scientists. His interests range from computer science, open source software development, interplanetary trajectory optimization, biomimetics and artificial intelligence.
Star Discrepancy Competition @GECCO 2024
Description:
Discrepancy measures are designed to quantify how well a set of points is distributed in a given domain. One of the most widely studied discrepancy notions is the L∞ star discrepancy, which measures the largest difference between the volume of boxes of type [0,x) and the fraction |P∩[0,x)|/|P| of points in P⊆0,1
Point sets of small L∞ star discrepancy have important applications in numerical approximation, in computer vision, but also in surrogate-based optimization. Designing point sets of low L∞ star discrepancy value is therefore an important task. Among the best-known constructions are the sequences by Sobol, by Halton, by Hammersley, etc.
The objective of this GECCO 2024 competition is to identify solvers that extend the settings for which we obtain accurate estimates for the L∞ star discrepancy of a given point set, where setting refers to the dimension d and the number of points in P.
The competition will host two tracks:
(1) A white-box track, welcoming solvers that make explicit use of the problem structure, e.g., by modelling it as a non-linear mathematical program.
(2) A grey-box track, welcoming solvers such as evolutionary approaches, local search algorithms, and similar that work in an iterative sampling-based fashion. After having focused on numerical solvers in 2023, the focus of our 2024 competition is on solvers that exploit the problem structure by operating on the grid defined by the points in P. To this end, the solvers search on the grid {1,2,...,|P|+1}^d. A detailed explanation of the problem, along with examples will be made available on the competition homepage.
The goal of the competition is to identify solvers that outperform the current state of the art https://epubs.siam.org/doi/10.1137/110833865, free version available at https://arxiv.org/abs/1103.2102, code available at https://zenodo.org/records/7630260 under TA.zip" rel="external">Gnewuch, Wahlström, Winzen, SIAM JoNA 2013, https://epubs.siam.org/doi/10.1137/110833865, free version available at https://arxiv.org/abs/1103.2102, code available at https://zenodo.org/records/7630260 under TA.zip.
Submission deadline:
2024-06-12Official webpage:
[will be made available soon, following the example from last year's competition: https://iohprofiler.github.io/competitions/stardiscr]Organizers:
Carola Doerr
Carola Doerr, formerly Winzen, is a CNRS research director at Sorbonne Université in Paris, France. Carola's main research activities are in the analysis of black-box optimization algorithms, both by mathematical and by empirical means. Carola is associate editor of IEEE Transactions on Evolutionary Computation, ACM Transactions on Evolutionary Learning and Optimization (TELO) and board member of the Evolutionary Computation journal. She is/was program chair for the BBSR track at GECCO 2024, the GECH track at GECCO 2023, for PPSN 2020, FOGA 2019 and for the theory tracks of GECCO 2015 and 2017. She has organized Dagstuhl seminars and Lorentz Center workshops. Together with Pascal Kerschke, Carola leads the 'Algorithm selection and configuration' working group of COST action CA22137. Carola's works have received several awards, among them the CNRS bronze medal, the Otto Hahn Medal of the Max Planck Society, best paper awards at GECCO, CEC, and EvoApplications.
Francois Clement
Francois Clement is a second-year PhD student at Sorbonne University. His research is centered around computational aspects of star discrepancies.
Diederick Vermetten
Diederick Vermetten is a PhD student at LIACS. He is part of the core development team of IOHprofiler, with a focus on the IOHanalyzer. His research interests include benchmarking of optimization heuristics, dynamic algorithm selection and configuration as well as hyperparameter optimization.
Jacob de Nobel
Jacob de Nobel is a PhD student at LIACS, and is currently one of the core developers for the IOHexperimenter. His research concerns the real world application of optimization algorithms for finding better speech encoding strategies for cochlear implants, which are neuroprosthesis for people with profound hearing loss.
Thomas Bäck
Thomas Bäck is Full Professor of Computer Science at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University, The Netherlands, where he is head of the Natural Computing group since 2002. He received his PhD (adviser: Hans-Paul Schwefel) in computer science from Dortmund University, Germany, in 1994, and then worked for the Informatik Centrum Dortmund (ICD) as department leader of the Center for Applied Systems Analysis. From 2000 - 2009, Thomas was Managing Director of NuTech Solutions GmbH and CTO of NuTech Solutions, Inc. He gained ample experience in solving real-life problems in optimization and data mining through working with global enterprises such as BMW, Beiersdorf, Daimler, Ford, Honda, and many others. Thomas Bäck has more than 350 publications on natural computing, as well as two books on evolutionary algorithms: Evolutionary Algorithms in Theory and Practice (1996), Contemporary Evolution Strategies (2013). He is co-editor of the Handbook of Evolutionary Computation, and most recently, the Handbook of Natural Computing. He is also editorial board member and associate editor of a number of journals on evolutionary and natural computing. Thomas received the best dissertation award from the German Society of Computer Science (Gesellschaft für Informatik, GI) in 1995 and the IEEE Computational Intelligence Society Evolutionary Computation Pioneer Award in 2015.
Luís Paquete
Luís Paquete is Associate Professor at the Department of Informatics Engineering, University of Coimbra, Portugal. He received his Ph.D. in Computer Science from T.U. Darmstadt, Germany, in 2005 and a M.S. in Systems Engineering and Computer Science from the University of Algarve, Portugal, in 2001. His research interest is mainly focused on exact and heuristic solution methods for multiobjective combinatorial optimization problems. He is in editorial board of Operations Research Perspectives and Area Editor at ACM Transactions on Evolutionary Learning and Optimization.
Kathrin Klamroth
Travelling Thief Problem Competition
Description:
Real-world optimization problems often consist of several NP-hard combinatorial optimization problems that interact with each other. Such multi-component optimization problems are difficult to solve not only because of the contained hard optimization problems, but in particular, because of the interdependencies between the different components. Interdependence complicates a decision making by forcing each sub- problem to influence the quality and feasibility of solutions of the other sub-problems. This influence might be even stronger when one sub-problem changes the data used by another one through a solution construction process. Examples of multi-component problems are vehicle routing problems under loading constraints, the maximizing material utilization while respecting a production schedule, and the relocation of containers in a port while minimizing idle times of ships. The goal of this competition is to provide a platform for researchers in computational intelligence working on multi-component optimization problems. The main focus of this competition is on the combination of TSP and Knapsack problems. However, we plan to extend this competition format to more complex combinations of problems (that have typically been dealt with individually in the past decades) in the upcoming years.
There will be a number of tracks, because the used instances (from https://dl.acm.org/doi/10.1145/2576768.2598249) vary massively and across multiple orders of magnitude:
Track 1) Analysing the results only, because not everybody has access to a compute cluster: Track 1.1) ~10 small instances Track 1.2) ~10 mid-sizes instances Track 1.3) ~10 large instances
Track 2) Run the code on some subset (possibly again three subtracts). The computational limits might remain tight, i.e. limited to a single core, to 10 minutes, and to a single run per instance, but these details are yet to be finalised.
Submission deadline:
2024-06-29Official webpage:
https://sites.google.com/view/ttp-gecco2024/Organizers:
Adriano Rodrigues Figueiredo Torres
PhD student.
Markus Wagner
Markus Wagner is an Associate Professor at Monash University, Australia. He has done his PhD studies at the Max Planck Institute for Informatics in Saarbruecken, Germany and at the University of Adelaide, Australia. For the outcomes of his research, he has received a University Doctoral Research Medal, four best paper awards, a best poster award, a best presentation award, and a Humies Gold Award. His research topics range from mathematical runtime analysis of heuristic optimisation algorithms and theory-guided algorithm design to applications of heuristic methods to renewable energy production, professional team cycling and software engineering. So far, he has been a program committee member 80+ times, and he has written 150+ articles with 200+ different co-authors. He is an ACM Lifetime Member, is on SIGEVO's Executive Board and serves as the first ever Sustainability Officer. He has contributed to GECCOs as Workshop Chair and Competition Chair, and he has chaired several education-related committees within the IEEE CIS, where he also served as founding chair of task forces on benchmarking and on energy.