| Title | Organizers |
|---|---|
| AbstractSwarm Multi-Agent Logistics Competition |
|
| Automated Design Competition |
|
| Dynamic Stacking Optimization in Uncertain Environments |
|
| Evolutionary Computation in the Energy Domain: Operation and Planning Applications. |
|
| Evolutionary Submodular Optimisation |
|
| GECCO 2023 Competition on Star Discrepancy Computation |
|
| Interpretable Symbolic Regression for Data Science |
|
| Machine Learning for Evolutionary Computation - Solving the Vehicle Routing Problems (ML4VRP) |
|
| Predicting Good Configurations for Topic Models |
|
| Science Communication for EC - Let’s Show the World, how Evolutionary Computation Works |
|
| SpOC: Space Optimisation Competition |
|
| Travelling Thief Problem Competition |
|
AbstractSwarm Multi-Agent Logistics Competition
Description:
This competition aims to motivate work in the broad field of 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:
2023-06-16Official webpage:
https://abstractswarm.gitlab.io/abstractswarm_competition/Organizers:
Daan Apeldoorn
Daan Apeldoorn primarily 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, and additionally for the Z Quadrat GmbH in Mainz. 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) and serves as the chair of the IEEE CIS Games Technical Committee and the Summer Schools Subcommittee. 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/
Lars Hadidi
Lars Hadidi is a theoretical physicist working as a research fellow at 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. He is currently focusing on neural networks which directly operate on graph structured data, their methods and applications.
Torsten Panholzer
Torsten Panholzer is managing director 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.
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:
2023-04-22Official 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.
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.
In the first track a dynamic environment is provided that represents a simplified stacking scenario. Blocks arrive continuously at a fixed arrival location from which they have to be removed swiftly. If the arrival location is full, the arrival of additional blocks is not possible. To avoid such a state, there is a range of buffer stacks that may be used to store blocks. Each block has a due date before which it should be delivered to the customer. However, blocks may leave the system only when they become ready, i.e., some time after their arrival. To deliver a block it must be put on the handover stack – which must contain only a single block at any given time. There is a single crane that may move blocks from arrival to buffer, between buffers, and from buffer to handover. The optimization must control this crane in that it reacts to changes with a sequence of moves that are to be carried out. The control does not have all information about the world. A range of performance indicators will be used to determine the winner.
The second track represents another dynamic warehouse scenario that is derived from real-world scenarios. It features two cranes and multiple arrival and handover stacks. Similarly, the cranes have a capacity of one. The solver may just provide the moves and the cranes will sort out the order in which these are performed (not optimal though) or the solver may optimize both the moves and the assignment and schedule of the cranes. In this scenario, both arrival and handover are critical, with a focus towards handover in order to avoid large queues of trucks or delayed shipments.
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 2022 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:
2023-06-29Official 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.
Sebastian Leitner
Sebastian Leitner (né Raggl) received his MSc in bioinformatics in 2014 from the University of Applied Sciences Upper Austria. He is currently pursuing his PhD at the Johannes Kepler University Linz, Austria. Since 2015 he is a member of the research group Heuristic and Evolutionary Algorithms Laboratory (HEAL) where he is working on several industrial research projects. He has focused on stacking problems in the steel industry for which he has acquired a lot of experience in the application domain, but also in the scientific state of the art.
Evolutionary Computation in the Energy Domain: Operation and Planning Applications.
Description:
Following the success of the previous editions at IEEE PES-GM; CEC; GECCO, WCCI, we are launching another challenging edition of the competition at major conferences in the field of computational intelligence and power systems. This GECCO 2023 competition proposes two tracks in the energy domain:
Track 1) Risk-based optimization of aggregators’ day-ahead energy resource management (ERM) considering uncertainty associated with the high penetration of distributed energy resources (DER). This test bed is constructed under the same framework of past competitions (therefore, former competitors can adapt their algorithms to this new track), representing a centralized day-ahead ERM in a smart grid with a 13-bus distribution network using a 15-scenario case study with 3 scenarios considering extreme events (high impact, and low probability). A conditional value-at-risk (CVaR) mechanism is used to measure the risk associated with the extreme events for a confidence level (α) of 95%. We also add some restrictions to the initialization of the initial solution and the allowed repairs and tweak-heuristics.
Track 2) Transmission Network Expansion Planning. Long-term transmission network expansion planning (TNEP) is a classic problem of power systems. The objective is to find the optimal expansion plan that identifies the transmission lines that must be installed in the system to allow a proper operation within a predefined planning horizon with the lowest investment cost. The optimal expansion plan should define where and how many lines should be installed. A nonconvex mixed-integer nonlinear programming formulation is used to model the problem. The North-Northeast Brazilian transmission system is considered as case study.
Submission deadline:
2023-05-31Official webpage:
http://www.gecad.isep.ipp.pt/ERM-competitions/2023-2/Organizers:
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.
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.
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.
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.
Leonardo H. Macedo
LEONARDO H. MACEDO (Member, IEEE) received the B.S., M.S., and Ph.D. degrees, all in electrical engineering, from São Paulo State University, Ilha Solteira, Brazil, in 2012, 2015, and 2019, respectively.
During 2016–2017, he was a Visiting Student with the University of Washington, Seattle, WA, USA, and from 2019 to 2020, he was a Postdoctoral Researcher with the Universidad de Castilla-La Mancha, Ciudad Real, Spain. He is currently a Postdoctoral Researcher with São Paulo State University. His current research interests include the development of methods for the optimization, planning, and control of electrical power systems.
Ruben Romero
RUBÉN ROMERO (Senior Member, IEEE) received the B.S. and P.E. degrees in electrical engineering from the National University of Engineering, Lima, Peru, in 1978 and 1984, respectively. He received the M.S. and Ph.D. degrees in electrical engineering from the University of Campinas, Campinas, Brazil, in 1990 and 1993, respectively. He is currently a full Professor of electrical engineering at São Paulo State University, Ilha Solteira, Brazil.
His research interests include methods for the optimization, planning, and control of electrical power systems, applications of artificial intelligence in power systems, and operations research.
Evolutionary Submodular Optimisation
Description:
Submodular functions play a key role in the area of optimisation as they allow to model many real-world optimisation problem. 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.
Benchmarking and assessment of the different approaches will be carried out using IOHProfiler ( https://iohprofiler.github.io/ ) which provides implementations of different submodular optimization problems as well as tools for performance assessment.
A description of the different submodular optimization problems included in this competition can be found in
F. Neumann, A. Neumann, C. Qian, V.A. Do, J. de Nobel, D. Vermetten, S. S. Ahouei, F. Ye, H. Wang, T. Bäck (2023): Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler. In: CoRR abs/2302.01464. https://arxiv.org/abs/2302.01464 (to appear at CEC 2023)
An example on how to access the submodular problems using IOHexperimenter can be found at
https://github.com/IOHprofiler/IOHexperimenter/blob/competition_notebooks/example/Example_Submodular.ipynb
Submission deadline:
2023-06-29Official webpage:
https://cs.adelaide.edu.au/~optlog/CompetitionESO2023.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.
Frank Neumann
Frank Neumann is a professor and the leader of the Optimisation and Logistics group at the University of Adelaide and an Honorary Professorial Fellow at the University of Melbourne. His current position is funded by the Australian Research Council through a Future Fellowship and focuses on AI-based optimisation methods for problems with stochastic constraints. Frank has been the general chair of the ACM GECCO 2016 and co-organised ACM FOGA 2013 in Adelaide. He is an Associate Editor of the journals "Evolutionary Computation" (MIT Press) and ACM Transactions on Evolutionary Learning and Optimization. In his work, he considers algorithmic approaches in particular for combinatorial and multi-objective optimization problems and focuses on theoretical aspects of evolutionary computation as well as high impact applications in the areas of cybersecurity, renewable energy, logistics, and mining.
Chao Qian
Chao Qian is an Associate Professor in the School of Artificial Intelligence, Nanjing University, China. He received the BSc and PhD degrees in the Department of Computer Science and Technology from Nanjing University. After finishing his PhD in 2015, he became an Associate Researcher in the School of Computer Science and Technology, University of Science and Technology of China, until 2019, when he returned to Nanjing University.
His research interests are mainly theoretical analysis of evolutionary algorithms (EAs), design of safe and efficient EAs, and evolutionary learning. He has published one book “Evolutionary Learning: Advances in Theories and Algorithms”, and over 40 papers in top-tier journals (AIJ, ECJ, TEvC, Algorithmica, TCS) and conferences (AAAI, IJCAI, NeurIPS, ICLR). He has won the ACM GECCO 2011 Best Theory Paper Award, the IDEAL 2016 Best Paper Award, and the IEEE CEC 2021 Best Student Paper Award Nomination. He is an associate editor of IEEE Transactions on Evolutionary Computation, a young associate editor of Science China Information Sciences, an editorial board member of the Memetic Computing journal, and was a guest editor of Theoretical Computer Science. He is a member of IEEE Computational Intelligence Society (CIS) Evolutionary Computation Technical Committee, and was the chair of IEEE CIS Task Force on Theoretical Foundations of Bio-inspired Computation. He has regularly given tutorials and co-chaired special sessions at leading evolutionary computation conferences (CEC, GECCO, PPSN), and has been invited to give an Early Career Spotlight Talk "Towards Theoretically Grounded Evolutionary Learning" at IJCAI 2022.
Hao Wang
Hao Wangobtained his PhD (cum laude) from Leiden University in2018. He is currently employed as an assistant professor of computer science at Leiden University. Previously, he has a research stay at Sorbonne University, France. He received the Best Paper Award at the PPSN2016conference and was a best paper award finalist at the IEEE SMC 2017 conference. His research interests are in the analysis and improvement of efficient global optimization for mixed-continuous search spaces, Evolution strategies, Bayesian optimization, and benchmarking.
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.
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.
GECCO 2023 Competition on Star Discrepancy Computation
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_infinity star discrepancy, which measures the largest difference between the volume of boxes of type [0,x) and the fraction |P \cap [0,x)|/|P| of points in P\subseteq [0,1]^d that lie inside this box. Point sets of small L_infinity star discrepancy have important applications in numerical approximation, in computer vision, but also in surrogate-based optimization, where they are commonly used as initial designs (a.k.a. DoEs). Designing point sets of low L_infinity star discrepancy value is therefore an important task. Among the best-known constructions are the sequences by Sobol’ by Halton, by Hammersley, etc. See https://en.wikipedia.org/wiki/Low-discrepancy_sequence for more information about star discrepancy notions.
An important bottleneck in the design of low-discrepancy point sets is the hardness of computing the star discrepancy value for a given point set [1,2]. The best problem-specific algorithm has a runtime that scales as |P|^{d/2+1} [3], which infeasible already for moderate dimensions and point sets. An alternative way to compute the star discrepancy is based on black-box optimization approaches that maximize the local discrepancy value: f(x) = |P \cap [0,x)|/|P|. The current-best such approach was proposed in [4]; it is based on threshold accepting.
The objective of this GECCO 2023 competition is to identify solvers that extend the settings for which we obtain accurate estimates for the L_infinity star discrepancy of a given point set, where “setting” refers to the dimension d and the number of points in P.
What makes this competition particularly interesting for evolutionary computation research is that the problem can be tackled both as a purely numerical problem max{f(x)|x \in [0,1]^d} and as a purely discrete problem max{f(x)| x \in [1..n]^d}.
References:
[1] Michael Gnewuch, Anand Srivastav, Carola Winzen: Finding optimal volume subintervals with k points and calculating the star discrepancy are NP-hard problems. J. Complex. 25(2): 115-127 (2009). https://www.sciencedirect.com/science/article/pii/S0885064X08000745?via%3Dihub
[2] Panos Giannopoulos, Christian Knauer, Magnus Wahlström, Daniel Werner: Hardness of discrepancy computation and ε-net verification in high dimension. J. Complex. 28(2): 162-176 (2012). https://www.sciencedirect.com/science/article/pii/S0885064X11000562?via%3Dihub
[3] David P. Dobkin, David Eppstein, Don P. Mitchell: Computing the Discrepancy with Applications to Supersampling Patterns. ACM Trans. Graph. 15(4): 354-376 (1996). https://dl.acm.org/doi/10.1145/234535.234536
[4] Michael Gnewuch, Magnus Wahlström, Carola Winzen: A New Randomized Algorithm to Approximate the Star Discrepancy Based on Threshold Accepting. SIAM J. Numer. Anal. 50(2): 781-807 (2012). https://epubs.siam.org/doi/10.1137/110833865
Submission deadline:
2023-06-29Official webpage:
https://iohprofiler.github.io/competitions/stardiscrOrganizers:
Carola Doerr
Carola Doerr, formerly Winzen, is a permanent 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 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. Her works have received several awards, among them the CNRS bronze medal, the Otto Hahn Medal of the Max Planck Society, best paper awards at EvoApplications, CEC, and GECCO.
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.
Alexandre D. Jesus
Alexandre D. Jesus is an Invited Assistant Professor at the Department
of Informatics Engineering, University of Coimbra, Portugal. He received
his PhD in 2022 from the University of Coimbra and University of Lille
under a co-tutelle agreement. His research interests include anytime
algorithms, multi-objective optimization, combinatorial optimization,
automated algorithm selection and configuration, and reproducibility.
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 Symbolic Regression for Data Science
Description:
The 2023 edition of the Symbolic Regression (SR) competition will be composed of two tracks: performance track and interpretability track. The participants will have the freedom to apply their own pipeline with the objective of returning symbolic models that best describe the data. In the first track, the models will be evaluated according to accuracy and simplicity. In the second track, participants are further asked to provide a post-analysis focused on the interpretation of their symbolic model.
The competition will start 01-April-2023 and last 2 months. The participants will gain access to 3 synthetic data sets for the first track, and 1 real-world data set for the second track. The tracks run independently and participants can enroll in one or both of them. During the two months, the participants can apply an SR approach or pipeline of their choice, e.g., their own novel algorithm or an existing SR package, as well as pre- and post-processing methods (e.g., feature construction and model simplification, respectively) to find suitable symbolic models for the corresponding data sets.
Enrollment will be done via GitHub Classroom, at a link to be announced soon. This will result in a private repository that can be accessed only by the participating team and the organizers. The repository contains detailed submission instructions, a default directory structure, and the data sets for the two tracks.
For more information see https://cavalab.org/srbench/competition-2023/ and our Discord server at https://discord.gg/Dahqh3Chwy
Submission deadline:
2023-06-30Official webpage:
https://cavalab.org/srbench/competition-2023/Organizers:
Fabricio Olivetti de França
Fabricio Olivetti de França is an associated professor in the Center for Mathematics, Computing and Cognition (CMCC) at Federal University of ABC. He received his PhD in Computer and Electrical Engineering from State University of Campinas. His current research topics are Symbolic Regression, Evolutionary Computation and Functional Data Structures.
Marco Virgolin
Marco Virgolin is a junior researcher at Centrum Wiskunde & Informatica (CWI), the Dutch national center of mathematics and computer science. He received his PhD from Delft University of Technology. Marco works on explainable AI, most notably by means of evolutionary machine learning methods such as genetic programming. He is also interested in medical applications of machine learning and human-machine interaction.
Pierre-Alexandre Kamienny
Pierre-Alexandre is a third-year PhD student at Meta AI Paris and Sorbonne University. He worked on fast adaptation in deep reinforcement learning and has been focused for more than a year on developing neural methods for symbolic regression that exhibits properties such as leveraging past experience as well as fast inference.
Geoffrey Bomarito
Geoffrey is a research engineer at NASA Langley Research Center in the USA. His research centers on uncertainty quantification in machine learning with a goal of building trust in data-driven models. Specifically, his recent work focuses on genetic programming based symbolic regression, physics-informed generative adversarial networks, and multifidelity uncertainty quantification.
Machine Learning for Evolutionary Computation - Solving the Vehicle Routing Problems (ML4VRP)
Description:
This new competition aims to develop machine learning assisted evolutionary algorithms for solving vehicle routing problems (VRP) and serves as a vehicle to bring together the latest developments of evolutionary computation enhanced by machine learning. Current relevant research has collected a large amount of data which captures knowledge of evolutionary computation, however, often discarded or not further investigated in the literature. This includes data on the solutions of different features to inform or drive the evolution / optimization, 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 of different difficulties provide an ideal testbed to enable performance comparison of machine learning-assisted computational optimization. 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.
Important Dates
Two-page abstract submission: 14 April 2023
Description and solution submission: 16 June 2023
Submission deadline:
2023-04-14Official webpage:
https://sites.google.com/view/ml4vrpOrganizers:
Rong Qu
Dr. Rong Qu is an Associated Professor at the University of Nottingham. Her main research interests include the modelling and optimisation with computational algorithms in evolutionary computation, operational research and artificial intelligence. Dr. Qu is Associate Editor at five international journals. She has been awarded the Royal Society Leverhulme Senior Research Fellowship in 2022.
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 received her MSc from King’s College London, UK and graduated from Shandong Normal University, China. She is currently a PhD student at the Computational Optimization and Learning (COL) Lab and a Teaching Associate in the School of Computer Science at the University of Nottingham. Her main research interests focus on Automated Algorithm Design, Hyper-heuristics, Vehicle Routing, and Combinatorial Optimization. She was a Research Assistant in the Amicable Charging (AMiCC) research project, delivering eco-friendly wireless charging solutions for electric vehicles, with a focus on optimizing the charging infrastructure. Weiyao recently completed a KTP project as a KTP Associate to develop and deploy novel and advanced hyper-heuristics-based routing technologies for haulage markets. 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.
Predicting Good Configurations for Topic Models
Description:
Given the vast amount of text that computers enable us to produce, it becomes ever more important to develop automated ways to categorise the different subjects contained in a given text corpus. This is the goal of topic modelling, which is essentially a statistical method to summarise text by discovering semantic patterns within it. Topic modelling dispenses with the necessity for labels and a priori classification, as well as with the need for a training set.
The usual yardstick for model fit in Topic Modelling is the concept of perplexity, which measures how well a probability distribution predicts a given sample. A low perplexity score means the model is good at predicting the sample. Since perplexity is the exponentiation of the entropy, it follows that low perplexity implies a less surprising system.
In this competition, participants are expected to create a model that optimises the parameters of a Latent Dirichlet Allocation (LDA) implementation that models topics for a corpus of data consisting of StackOverflow and GitHub textual data that is generated during the software development lifecycle. Competitors are free to define their feature set, as well as the technology they used to deploy their system.
A paper describing the problem: https://cs.adelaide.edu.au/~markus/pub/2019msr-topicmodelling.pdf
The way the fast finding of algorithm configurations is approached in that article:
1) good LDA configurations are determined for sets of documents,
2) text features are calculated for the same sets of documents,
3) then, a model is created that maps the features to the good configurations, enabling the fast finding of good configurations.
Submission deadline:
2023-06-29Official webpage:
https://sites.google.com/view/topic-model-gecco-2023/homeOrganizers:
Adriano Rodrigues Figueiredo Torres
PhD student.
Markus Wagner
Markus Wagner received his Ph.D. degree from the University of Adelaide and is an active researcher at their Optimisation and Logistics Group. His research interests include heuristic optimisation and software engineering, and he is the recipient of the:
- University Doctoral Research Medal for his work on Theory and Applications of Bio-Inspired Algorithms; and
- Best Paper Awards for his works on Evolving Pacing Strategies for Team Pursuit Track Cycling, on a Hybrid Evolutionary Algorithm Framework for Optimising Power Take Off and Placements of Wave Energy Converters, and on the Optimisation of Large Wave Farms using a Multi-strategy Evolutionary Framework.
Markus has been awarded grants from the Australian Research Council (ARC), Google and Facebook, and he is the Founding Chair of the IEEE CIS Task Forces on "Computational Intelligence in the Energy Domain" and "Task Force on Benchmarking", and Chair of IEEE CIS Education Subcommittees. He has supported previous GECCOs as Workshop Chair, Competitions Chair, and most recently as General Chair.
To date, Marcus has co-authored over 150 refereed publications in the form of book chapters, journal articles, and conference papers.
Christop Treude
Christoph Treude is a Senior Lecturer in Software Engineering in the School of Computing and Information Systems at the University of Melbourne. The goal of his research is to improve the quality of software and the productivity of those producing it, with a particular focus on getting information to software developers when and where they need it.
Sebastian Baltes
Principal Expert for Empirical Software Engineering at SAP SE in Germany, and an Adjunct Lecturer at the University of Adelaide, Australia
Software Engineering
Empirical Research
Interdisciplinarity
Sebastian Baltes
Principal Expert for Empirical Software Engineering at SAP SE in Germany, and an Adjunct Lecturer at the University of Adelaide, Australia
Software Engineering
Empirical Research
Interdisciplinarity
Science Communication for EC - Let’s Show the World, how Evolutionary Computation Works
Description:
This competition is not about producing new results, solving scientific problems or reaching some benchmark level of performance, at least not in the traditional way. Instead, this competition is dedicated to the communication of EC researchers’ work and achievements beyond the traditional boundaries of a scientific conference - and also to advertise their research. This is Sci-Com competition, asking researchers to produce short, engaging, clear and captivating videos that communicate their field and/or results to a broader public, beyond the traditional boundaries of GECCO. Participants should focus on a particular segment of the population or community, for instance K-12 level students, industry professionals, clinicians, policy makers, or community organizers, to mention but a few. They should also specify what is the message they want to share, such as describing a specific paradigm, algorithm, tool, or recent result from GECCO related fields. Finally, participants will submit their videos, which will be judged by an independent panel of judges to determine which gets their message across in the most effective way, hopefully leading towards broader social understanding of the field of evolutionary computation.
Submissions
Please make sure that the submitted videos are available on an online platform such as, e.g., Youtube or a publicly available Google drive, or any other website. Videos should not be shorter than one minute and not longer than 10 minutes.
You can submit your video to this competition via e-mail to the organizers (Stephan.winkler@fh-ooe.at) and explain who is the main target audience of this video.
Prizes
The winners of this competition will be awarded an assortment of finest Austrian and Mexican delicatessen, vouchers for online shopping platforms sponsored by Softwarepark Hagenberg, as well as a certificate.
Submission deadline:
2023-06-15Official webpage:
Organizers:
Leonardo Trujillo
Dr. Leonardo Trujillo is Professor at the Tecnológico Nacional de México/Instituto Tecnológico de Tijuana (ITT), working in the Department of Electrical and Electronic Engineering, and the Engineering Sciences Graduate Program. Dr. Trujillo received an Electronic Engineering degree and a Master's in Computer Science degree from ITT, as well as a doctorate in Computer Science from CICESE research center in Ensenada, Mexico. He is involved in interdisciplinary research in the fields of evolutionary computation, computer vision, machine learning and pattern recognition. His research focuses on Genetic Programming (GP) and developing new learning and search strategies based on this paradigm. Dr. Trujillo has been the PI of several national and international research grants, receiving several distinctions from the Mexican science council (CONACYT). His work has been published in over 70 journal papers, 60 conference papers, 18 book chapters, and he has edited 6 books on EC and GP. He is on the Editorial Board of the journals GPEM (Springer) and MCA (MDPI), and associate editor of AI Communications, Special Issue Guest Editor on 5 occasions, and regularly serves as a reviewer for highly respected journals in AI, EC and ML, is series co-chair of the NEO Workshop series, and has organized, been track chair or served as PC member of various prestigious conferences, including GECCO, GPTP, EuroGP, PPSN, CEC, CVPR and ECCV.
Stephan Winkler
Dr. Stephan Winkler is Head of the Department of Medical and Bioinformatics at University of Applied Sciences Upper Austria. Since its foundation in 2002, Dr. Winkler is member of the Heuristic and Evolutionary Algorithms Laboratory (HEAL), the group developing and maintaining HeuristicLab (https://dev.heuristiclab.com). Since 2011, Stephan Winkler serves as Head of the Bioinformatics Research Group Hagenberg (https://bioinformatics.fh-hagenberg.at/), and since 2022, Stephan Winkler also serves as Scientific Head of the Softwarepark Hagenberg. Furthermore, Stephan Winkler is member of the organization committee of Genetic Programming in Theory and Practice (GPTP) and serves as reviewer for several international journals. Stephan Winkler has (co-)autored more than 100 articles and conference papers on symbolic regression, heuristic optimization, and bioinformatics.
SpOC: Space Optimisation Competition
Description:
In the distant future, humanity has travelled across the galaxies, setting foot on numerous planets and establishing colonies on these new worlds. Yet, the quest for a new home persists, and finally, a glimmer of hope appears on the horizon. After years of tireless exploration, a promising planet is discovered in a faraway solar system.
Dubbed "New Mars" for its striking resemblance to the red planet, the call goes out to send twelve motherships hurtling across the cosmos to explore and settle this new world. The stakes are high, and the challenges are great, but the lure of a new beginning is too strong to resist.
Greetings, SpOC competitors! The time has come to unleash your innovative minds and contribute to humanity's New Mars settlement effort.
The New Mars settlement program is an ambitious undertaking, consisting of three distinct phases, each with its own unique set of design challenges. The first phase involves the travel to the planet itself, while the second phase focuses on the deployment of a state-of-the-art telecommunications infrastructure. Finally, in the third phase, settlers will recover experimental data from the planet's surface with the help of advanced morphing rovers. Each phase will be tied to an optimisation challenge that SpOC participants will have to solve.
1. Travel through the Wormhole Transportation Network
The motherships, each carrying hundreds of settlers and all the necessary resources to establish a new colony, embark on their journey, travelling for years through the vast emptiness of space. To make this journey possible, the settlers use a wormhole transportation network. This technology allows the motherships to traverse enormous distances in a matter of years, dramatically reducing the time and energy required for interstellar travel. The settlers face a unique challenge in their journey to New Mars, not only because they will travel vast distances in space, but also because they will be travelling through time. Due to the nature of the wormhole network, the motherships will arrive at their destination at different times relative to each other, and it will be a significant challenge to coordinate their efforts and ensure that they arrive together at the planet's orbit.
2. Deploy Quantum Communications Constellations
Finally, after a long and perilous journey, the motherships reach New Mars. As the settlement effort on the planet begins, one of the key challenges is establishing reliable communication channels between the planet's surface and the motherships in orbit. To tackle this challenge, teams of engineers have been working tirelessly to develop and deploy quantum communications constellations in the surrounding space. These constellations, composed of advanced quantum satellites, will enable the settlers to transmit data at unparalleled speeds and security. With this breakthrough technology, the settlers can focus on exploring and settling this new world, knowing they have a reliable link back to their home solar systems.
3. Surface Exploration with Morphing Rovers
With the communications network established, scientific exploration of New Mars is now a top priority for the settlers. To achieve this goal, a series of surface experiments were conducted, producing vital physical samples that must be quickly recovered. Advanced morphing rovers will be sent to the surface to collect the samples, these state-of-the-art autonomous rovers have the unique ability to change their morphology between several programmed forms, allowing them to be adapted in advance for the terrain they will encounter. Top engineers and scientists have been assembled to design a morphing rover for the new planet. Equipped with advanced sensors and communication systems, the optimal morphing rover will be able to traverse even the most challenging areas of New Mars, bringing back data that will aid in the settlers' efforts to understand and ultimately thrive on this new world.
Scope of the Competition
Your mission is to tackle up to three optimization challenges associated with the design of the three phases of the New Mars settlement initiative. Starting from 1 April 2022, you will have three months to tackle these challenges and compete for the top spot on the SpOC leaderboard.
Detailed technical descriptions for the three challenges to be solved will be made available on the Optimise (https://optimise.esa.int/) platform from the same date.
Submission deadline:
2023-06-30Official webpage:
https://www.esa.int/gsp/ACT/projects/spoc-2023/Organizers:
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.
Alexander Hadjivanov
Alexander Hadjiivanov started his academic journey with a BS in physics, followed by a MA in linguistics. During his subsequent career as a professional science translator, he followed the development of cutting-edge research in various areas of artificial intelligence. Alexander returned to academia to obtain a PhD in artificial intelligence, whereby he conducted research on natural language processing, neuroevolution and spiking neural networks. After working at a startup for a couple of years, Alexander joined ESA's Advanced Concepts Team as a Research Fellow in Artificial Intelligence. He is currently working on neuromorphic perception and processing, continual / online learning and structural plasticity in neural networks.
Dominik Dold
Dominik is a scientist working on artificial intelligence (AI) and neuromorphic computing. He graduated with a PhD from Heidelberg University and after a Research Residence at the Siemens AI Lab in Munich, he joined the Advanced Concepts Team at ESA as a Research Fellow. In his work, he mainly focuses on biologically inspired AI, graph algorithms and applications of AI, especially for space. He believes that the recent progress in AI offers exciting opportunities for the space sector and will be essential in supporting us to further explore and understand our Solar System (and everything that lies beyond!).
Amy Thomas
Amy is a researcher working on the design and control of morphing structures. She graduated from the University of Bristol with a Masters degree in aerospace engineering, joining the Advanced Concepts Team as a young graduate trainee immediately after university. This year her work focuses on investigating the potential of a novel morphing material; developing a robust simulation environment for the material and testing possible use cases for space applications. She believes that morphing structures will enable future spacecraft and rovers to have much more functionality and scope than before, which will play a pivotal role in expanding our understanding and exploration of the Solar System.
Loic Azzalini
Loïc Azzalini is a Young Graduate Trainee in the Advanced Concepts Team, where he works on biologically-inspired computer vision algorithms for navigation and tracking applications.
He obtained a BASc in Mechatronics Engineering from the University of Waterloo and an MASc in Aerospace Science & Engineering from the University of Toronto. During his masters, he had the opportunity to collaborate with computational neuroscientists on the application of optimal control theory and state estimation to neuronal dynamics in the context of deep brain stimulation. He joined the ACT with the aim of finding other synergies between these two fields, this time drawing inspiration from neuroscience to explore novel research avenues for space!
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.
Website: to be set up in time.
Tentatively, we point at a research group's TTP website for further information: https://cs.adelaide.edu.au/~optlog/research/combinatorial.php
Submission deadline:
2023-06-29Official webpage:
https://sites.google.com/view/ttp-gecco2023/homeOrganizers:
Markus Wagner
Markus Wagner received his Ph.D. degree from the University of Adelaide and is an active researcher at their Optimisation and Logistics Group. His research interests include heuristic optimisation and software engineering, and he is the recipient of the:
- University Doctoral Research Medal for his work on Theory and Applications of Bio-Inspired Algorithms; and
- Best Paper Awards for his works on Evolving Pacing Strategies for Team Pursuit Track Cycling, on a Hybrid Evolutionary Algorithm Framework for Optimising Power Take Off and Placements of Wave Energy Converters, and on the Optimisation of Large Wave Farms using a Multi-strategy Evolutionary Framework.
Markus has been awarded grants from the Australian Research Council (ARC), Google and Facebook, and he is the Founding Chair of the IEEE CIS Task Forces on "Computational Intelligence in the Energy Domain" and "Task Force on Benchmarking", and Chair of IEEE CIS Education Subcommittees. He has supported previous GECCOs as Workshop Chair, Competitions Chair, and most recently as General Chair.
To date, Marcus has co-authored over 150 refereed publications in the form of book chapters, journal articles, and conference papers.
Adriano Rodrigues Figueiredo Torres
PhD student.