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Tracks

TitleTrack Chairs
BBSR - Benchmarking, Benchmarks, Software, and Reproducibility
  • Carola Doerr
  • Arnaud Liefooghe
CS - Complex Systems
  • Mary Katherine Heinrich
  • Emily Dolson
ECOM - Evolutionary Combinatorial Optimization and Metaheuristics
  • Leslie Pérez Cáceres
  • Yi Mei
EML - Evolutionary Machine Learning
  • Jean-Baptiste Mouret
  • Kai Qin
EMO - Evolutionary Multiobjective Optimization
  • Dimo Brockhoff
  • Tapabrata Ray
ENUM - Evolutionary Numerical Optimization
  • Katherine Malan
  • Youhei Akimoto
GA - Genetic Algorithms
  • Aneta Neumann
  • Elizabeth Wanner
GECH - General Evolutionary Computation and Hybrids
  • Alberto Moraglio
  • James McDermott
GP - Genetic Programming
  • Ting Hu
  • Aniko Ekart
L4EC - Learning for Evolutionary Computation
  • Pascal Kerschke
  • Marie-Eléonore Kessaci
NE - Neuroevolution
  • Gabriela Ochoa
  • Antoine Cully
RWA - Real World Applications
  • Ruhul Sarker
  • Patrick Siarry
SBSE - Search-Based Software Engineering
  • Dominik Sobania
  • Aymeric Blot
SI – Swarm Intelligence
  • Christian Blum
  • Paola Pellegrini
THEORY - Theory
  • Benjamin Doerr
  • Christine Zarges

BBSR - Benchmarking, Benchmarks, Software, and Reproducibility

Description

The Benchmarking, Benchmarks, Software, and Reproducibility track welcomes submissions that touch on all aspects of reproducibility, benchmarking, and software of genetic and evolutionary computation methods. In particular, we welcome submissions on the following topics:

  • Benchmarking methodologies for assessing the performance of evolutionary algorithms and related optimization techniques,
  • Benchmark problems and toolboxes for evaluating evolutionary computation methods or enabling the training of meta-learning techniques for these,
  • Statistical analysis and visualization techniques for understanding problem spaces or the performance and behavior of optimization techniques, including instance space analysis and landscape analysis,
  • Reproducibility studies that rigorously replicate published experiments with a substantial shift in confidence in the results of the original study,
  • Innovative software for deploying, evaluating, developing, or teaching genetic and evolutionary computation in original and unique ways.

This is a non-exhaustive list, and we invite the authors to get in touch with the track chairs if in doubt about the suitability of their submission to this track.

Requirements for reproducibility studies

For reproducibility studies, the reasons for the new findings must be clearly explained in order to ensure a meaningful and distinct contribution from the original study (e.g. different benchmarks, application scenarios, technical or implementation differences). The submission must follow the highest reproducibility standards by providing all implementation details, input data, parameters and hardware specifications. All artifacts must be made available in a public repository upon submission and must remain available after publication. The submission must also follow the usual standards in terms of plagiarism.
Of particular interest are replicability studies, defined as follows by the
ACM: "The measurement can be obtained with stated precision by a different team, a different measuring system, in a different location on multiple trials (different team, different experimental setup)."

Anonymization

We acknowledge that for some of the works fitting this track, it may be difficult to submit in completely anonymized form, e.g., when links to demos, data, or software are required to assess the suitability of the submission for GECCO. Whenever possible, we strongly encourage the authors to make use of anonymous repositories (available on Zenodo and for GitHub repositories, for example). In the ideal case, these repositories will be deanonymized only after the notification. Where it is impossible to anonymize repositories, the BBSR track allows to link resources that possibly reveal authors’ identity. However, also in this case, all other elements of the paper shall follow the standard anonymization guidelines. In particular, we require that author names, affiliations, and acknowledgments are suppressed and that, to the maximum extent possible, references to any of the author's own work should be made as if the work belonged to someone else. We strongly recommend the use of the following option:

\documentclass[manuscript,screen,review,anonymous]{acmart}

Track Chairs

Carola Doerr

CNRS and Sorbonne University, France | webpage

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.

Arnaud Liefooghe

University of Littoral, France | webpage

Arnaud Liefooghe is a Professor of Artificial Intelligence at the University of the Littoral Opal Coast (ULCO), France. He is the co-director of the MODŌ international lab between France and Japan. Previously, he was an associate professor at the University of Lille. In 2010, he was a postdoctoral researcher at the University of Coimbra. In 2020, he was on a CNRS sabbatical leave at JFLI and an Invited Professor at the University of Tokyo. Since 2021, he has been appointed as a Collaborative Professor at Shinshu University, Japan. His research lies in the foundations, design and analysis of local search and evolutionary algorithms, with a particular interest in multi-objective optimization and landscape analysis. He has co-authored over a hundred peer-reviewed scientific papers in international journals and conferences. He was the recipient of a best paper award at EvoCOP 2011, GECCO 2015 and GECCO 2023. He was the co-Program Chair of EvoCOP (in 2018 and 2019) and held various responsibilities for GECCO (Proceedings Chair in 2018, co-EMO Track Chair in 2019, Virtualization Chair in 2021, and co-Hybrid Scheduling Chair in 2023). He is currently the Reproducibility Chair for the ACM Transactions on Evolutionary Learning and Optimization (TELO) and the co-Track Chair of the new Benchmarking, Benchmarks, Software, and Reproducibility (BBSR) track for GECCO 2024.


CS - Complex Systems

Description

This track invites all papers addressing the challenges of scaling evolution up to real-life complexity. This includes both the real-life complexity of biological systems, such as artificial life, artificial immune systems, and generative and developmental systems (GDS); and the real-world complexity of physical systems, such as evolutionary robotics and evolvable hardware.

Artificial life, Artificial Immune Systems, and Generative and Developmental Systems all take inspiration from studying living systems. In each field, there are generally two main complementary goals: to better understand living systems and to use this understanding to build artificial systems with properties similar to those of living systems, such as behavior, adaptability, learning, developmental or generative processes, evolvability, active perception, communication, self-organization and cognition. The track welcomes both theoretical and application-oriented studies in the above fields. The track also welcomes models of problem-solving through (social) agent interaction, emergence of collective phenomena and models of the dynamics of ecological interactions in an evolutionary context.

Evolutionary Robotics and Evolvable Hardware study the evolution of controllers, morphologies, sensors, and communication protocols that can be used to build systems that provide robust, adaptive and scalable solutions to the complexities introduced by working in real-world, physical environments. The track welcomes contributions addressing problems from control to morphology, from single robot to collective adaptive systems. Approaches to incorporating human users into the evolutionary search process are also welcome. Contributions are expected to deal explicitly with Evolutionary Computation, with experiments either in simulation or with real robots.

Track Chairs

Mary Katherine Heinrich

Artificial Intelligence Research Laboratory of the Université Libre de Bruxelles (IRIDIA) | webpage

Mary Katherine Heinrich received the B.Sc. degree from the University of Cincinnati, OH, USA, in 2013, the MAI degree from IAAC, Universitat Politécnica de Catalunya, Barcelona, Spain, in 2014, and the Ph.D. degree from the Centre for Information Technology and Architecture, Royal Danish Academy, Copenhagen, Denmark, in 2019. From 2016 to 2018, she was a Recurring Visiting Ph.D. Researcher with the New England Complex Systems Institute, Cambridge, MA, USA, and from 2018 to 2019, a Research Associate with the Service Robotics Group, Institute of Computer Engineering, University of Lübeck, Luebeck, Germany. Since 2019, she has been a Postdoctoral Researcher with IRIDIA, the Artificial Intelligence Laboratory, Université Libre de Bruxelles, Brussels, Belgium. Her research interests include swarm intelligence, swarm robotics, and construction automation.

Emily Dolson

Michigan State University | webpage

Emily Dolson is an assistant professor at Michigan State University in the department of Computer Science & Engineering and core faculty in Ecology, Evolution, & Behavior. She received a dual PhD from these same departments in 2019. In between, she was a postdoctoral fellow at Cleveland Clinic, where she studied cancer evolution. Emily's research interests center around trying to predict and control the outcome of evolution in complex ecological communities. In the context of evolutionary computation, she uses eco-evolutionary theory to understand the properties of different evolutionary algorithms, develop new algorithms, and predict which algorithms are best matched to which problems. She was elected to the International Society for Artificial Life Board of Directors in 2019; in this capacity, she organizes efforts to make information about artificial life more easily accessible on the internet.


ECOM - Evolutionary Combinatorial Optimization and Metaheuristics

Description

The ECOM track aims to provide a forum for the presentation and discussion of high-quality research on metaheuristics for combinatorial optimization problems. Challenging problems from a broad range of applications, including logistics, network design, bioinformatics, engineering and business have been tackled successfully with metaheuristic approaches. In many cases, the resulting algorithms represent the state-of-the-art for solving these problems. In addition to evolutionary algorithms, the class of metaheuristics includes prominent generic problem solving methods, such as tabu search, iterated local search, variable neighborhood search, memetic algorithms, simulated annealing, GRASP and ant colony optimization.

Scope

The ECOM track encourages original submissions on the application of evolutionary algorithms and metaheuristics to combinational optimization problems. The topics for ECOM include, but are not limited to:

  • Representation techniques
  • Neighborhoods and efficient algorithms for searching them
  • Variation operators for stochastic search methods
  • Search space and landscape analysis
  • Comparisons between different techniques (including exact methods)
  • Constraint-handling techniques
  • Automated design of combinatorial optimisation algorithms
  • Characteristics of problems and problem instances


Notice that the submission of very narrowed case studies of real-life problems as well as highly specific theoretical results on the performance of evolutionary algorithms may be better suited to other tracks at GECCO.

Track Chairs

Leslie Pérez Cáceres

Pontificia Universidad Católica de Valparaíso, Chile | webpage

Leslie Pérez Cáceres is a professor at Pontificia Universidad Católica de Valparaíso, Chile since 2018. She is the Director of the Artificial Intelligence Diploma of the PUCV’s Escuela de Ingeniería Informática and the Director of Female Leadership and Participation of the Engineering Faculty of the PUCV. She received the M.S. degree in Engineering Sciences in 2011 from the Universidad Técnica Federico Santa María and, the Ph.D. in Engineering and Technology Sciences from the Université Libre de Bruxelles in 2017. Her research interests are the automatic configuration of optimization algorithms and the design of optimization algorithm for solving combinatorial optimization problems. She is also one of the developers of the irace configuration tool.

Yi Mei

School of Engineering and Computer Science, Victoria University of Wellington, New Zealand | webpage

Yi Mei is an Associate Professor at the School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand. He received his BSc and PhD degrees from University of Science and Technology of China in 2005 and 2010, respectively. His research interests include evolutionary computation and learning in scheduling and combinatorial optimisation, hyper-heuristics, genetic programming, automatic algorithm design, explainable AI, etc. Yi has more than 200 fully refereed publications, including the top journals in EC and Operations Research (OR) such as IEEE TEVC, IEEE Transactions on Cybernetics, European Journal of Operational Research, ACM Transactions on Mathematical Software, and top EC conferences (GECCO). He won an IEEE Transactions on Evolutionary Computation Outstanding Paper Award 2017, and a Victoria University of Wellington Early Research Excellence Award 2018. As the sole investigator, he won the 2nd prize of the Competition at IEEE WCCI 2014: Optimisation of Problems with Multiple Interdependent Components. He serves as a Vice-Chair of the IEEE CIS Emergent Technologies Technical Committee, a member of three IEEE CIS Task Forces and two IEEE CIS Technical Committees. He is an Associated Editor of IEEE Transactions on Evolutionary Computation, an Editorial Board Member/Associate Editor of other four international journals, and a guest editor of a special issue of the Genetic Programming Evolvable Machine journal. He was an Outstanding Reviewer for Applied Soft Computing in 2015 and 2017, and IEEE Transactions on Cybernetics in 2018. He is a Fellow of Engineering New Zealand, ACM Member and IEEE Senior Member.


EML - Evolutionary Machine Learning

Description

The Evolutionary Machine Learning (EML) track at GECCO covers advances in the theory and application of using evolutionary computation methods to solve Machine Learning (ML) problems. Evolutionary methods can tackle many different tasks within the ML context, including problems related to supervised, unsupervised, semi-supervised, and reinforcement learning, as well as more recent topics such as transfer learning and domain adaptation, deep learning, representation learning, interpretability of machine learning models, and learning with unbalanced data and missing data.

The global search capability featured by evolutionary methods provides a valuable complement to the local search process that typically underpins non-evolutionary ML methods, and combinations of the two often demonstrate desirable promise in practice.

We encourage submissions related to theoretical advances, innovation of new algorithms, and renovation/improvement of existing algorithms, as well as application-focused papers. Authors are strongly encouraged to compare their EML approaches to the corresponding state-of-the-art non-evolutionary ML methods, where appropriate.

If your work focuses on the use of ML for solving evolutionary computation problems, please consider the new L4EC track that complements this track.

Scope

More concretely, topics of interest include but are not limited to:

  • Theoretical and methodological advances on EML
  • Evolutionary ensemble learning
  • Evolutionary transfer learning and domain adaptation
  • Evolutionary transfer learning and domain adaptation
  • Evolutionary representation learning
  • Learning Classifier Systems (LCS) and evolutionary rule-based systems
  • Evolutionary computation techniques (e.g. genetic programming, particle swarm optimisation, and differential evolution) for solving ML tasks such as clustering, dimension reduction (feature selection, extraction, and construction), and representation learning
  • AutoML (e.g. hyper-parameter tuning for ML) via evolutionary methods
  • EML with a small number of examples, unbalanced data or missing data
  • Visualizing or improving the interpretability of ML models via evolutionary approaches
  • Parallel, distributed, and decentralized EML, including approaches based on high performance computing (with GPUs/TPUs), cloud computing ,and edge computing as well as federated learning
  • Applications of EML (non-exhaustive list):
    • Computer vision and image processing
    • Pattern recognition and data mining
    • Bioinformatics, life sciences, medicine, and health
    • Space technology
    • Cognitive systems and modelling
    • Economic modelling
    • Intelligent transportation
    • Cyber security

Track Chairs

Jean-Baptiste Mouret

Inria Nancy - Grand Est, CNRS, Université de Lorraine, France | webpage

Jean-Baptiste Mouret is a senior researcher ("directeur de recherche) at Inria, a French research institute dedicated to computer science and mathematics. He was previously an assistant professor ("mâitre de conférences) at ISIR (Institute for Intelligent Systems and Robotics), which is part of Université Pierre et Marie Curie - Paris 6 (UPMC, now Sorbonne Université). He obtained a M.S. in computer science from EPITA in 2004, a M.S. in artificial intelligence from the Pierre and Marie Curie University (Paris, France) in 2005, and a Ph.D. in computer science from the same university in 2008. He was the principal investigator of an ERC grant (ResiBots - Robots with animal-like resilience, 2015-2020) and was the recipient of a French "ANR young researcher grant (Creadapt - Creative adaptation by Evolution, 2012-2015). Overall, J.-B. Mouret conducts researches that intertwine evolutionary algorithms, neuro-evolution, and machine learning to make robots more adaptive. His work was featured on the cover of Nature (Cully et al., 2015) and it received the "2017 ISAL Award for Distinguished Young Investigator in the field of Artificial Life, the "Outstanding Paper of 2015 award from the Society for Artificial Life (2016), the French "La Recherche" award (2016), 3 GECCO best paper awards (2011, GDS track; 2017 & 2018, CS track), and the IEEE CEC "best student paper" award (2009). He co-chaired the "Evolutionary Machine Learning track at GECCO 2019 and the "Generative and Developmental Systems'' track in 2015.

Kai Qin

School of Science, Computing and Engineering Technologies, Swinburne University of Technology | webpage

Kai Qin is a Professor at Swinburne University of Technology, Melbourne, Australia. Currently, he is the Director of Swinburne Intelligent Data Analytics Lab and the Deputy Director of Swinburne Space Technology and Industry Institute. Before joining Swinburne, he worked at Nanyang Technological University (Singapore), the University of Waterloo (Canada), INRIA Grenoble Rhône-Alpes (France), and RMIT University (Australia). His major research interests include machine learning, evolutionary computation, collaborative learning and optimization, computer vision, remote sensing, services computing, and edge computing. He was a recipient of the 2012 IEEE Transactions on Evolutionary Computation Outstanding Paper Award and the 2022 IEEE Transactions on Neural Networks and Learning Systems Outstanding Associate Editor. He is currently the Chair of the IEEE Computational Intelligence Society (CIS) Student Activities and Young Professionals Sub-committee, the Vice-Chair of the IEEE CIS Neural Networks Technical Committee, the Vice-Chair of the IEEE CIS Emergent Technologies Task Force on “Multitask Learning and Multitask Optimization”, the Vice-Chair of the IEEE CIS Neural Networks Task Force on “Deep Edge Intelligence, and the Chair of the IEEE CIS Neural Networks Task Force on “Deep Vision in Space”. He serves as the Associate Editor for several top-tier journals, e.g., IEEE TEVC, IEEE TNNLS, IEEE CIM, NNs, and SWEVO. He was the General Co-Chair of the 2022 IEEE International Joint Conference on Neural Networks (IJCNN 2022) held in Padua, Italy, and was the Chair of the IEEE CIS Neural Networks Technical Committee during the 2021-2022 term.


EMO - Evolutionary Multiobjective Optimization

Description

In many real-world applications, several objective functions have to be optimized simultaneously, leading to a multiobjective optimization problem (MOP) for which an single ideal solution seldomly exists. Rather, MOPs typically admit multiple compromise solutions representing different trade-offs among the objectives. Due to their applicability to a wide range of MOPs, including black-box problems, evolutionary algorithms for multiobjective optimization have given rise to an important and very active research area, known as Evolutionary Multiobjective Optimization (EMO). No continuity or differentiability assumptions are required by EMO algorithms, and problem characteristics such as nonlinearity, multimodality and stochasticity can be handled as well. Furthermore, preference information provided by a decision maker can be used to deliver a finite-size approximation to the optimal solution set (the so-called Pareto-optimal set) in a single optimization run.

Scope

The Evolutionary Multiobjective Optimization (EMO) Track is intended to bring together researchers working in this and related areas to discuss all aspects of EMO development and deployment, including (but not limited to):

  • Handling of continuous, combinatorial or mixed-integer problems
  • Test problems and performance assessment
  • Benchmarking studies, especially in comparison to non-EMO methods
  • Selection mechanisms
  • Variation mechanisms
  • Hybridization
  • Parallel and distributed models
  • Stopping criteria
  • Theoretical foundations and search space analysis that bring new insights to EMO
  • Implementation aspects
  • Algorithm selection and configuration
  • Visualization
  • Preference articulation
  • Interactive optimization
  • Many-objective optimization
  • Large-scale optimization
  • Expensive function evaluations
  • Constraint handling
  • Uncertainty handling
  • Real-world applications, where the results presented extend beyond the solving of the applied problem, bringing new and broader EMO insights

Track Chairs

Dimo Brockhoff

Inria and Ecole Polytechnique, France | webpage

Dimo Brockhoff received his diploma in computer science from University of Dortmund, Germany in 2005 and his PhD (Dr. sc. ETH) from ETH Zurich, Switzerland in 2009. After two postdocs at Inria Saclay Ile-de-France (2009-2010) and at Ecole Polytechnique (2010-2011), he joined Inria in November 2011 as a permanent researcher (first in its Lille - Nord Europe research center and since October 2016 in the Saclay - Ile-de-France one). His research interests are focused on evolutionary multiobjective optimization (EMO), in particular on theoretical aspects of indicator-based search and on the benchmarking of blackbox algorithms in general. Dimo has co-organized all BBOB workshops since 2013 and has been EMO track co-chair at GECCO in 2013, 2014, and 2023.

Tapabrata Ray

University of New South Wales, Canberra | webpage

Tapabrata Ray is a Professor with the School of Engineering and Information Technology. He is the founder and leader of the Multidisciplinary Design Optimization Research Group at UNSW, Canberra.


ENUM - Evolutionary Numerical Optimization

Description

The ENUM track (Evolutionary NUMerical optimization) is concerned with randomized search algorithms and continuous search spaces. The scope of the ENUM track includes, but is not limited to, stochastic methods such as differential evolution (DE), evolution strategies (ES), estimation-of-distribution algorithms (EDAs) and particle swarm optimization (PSO). The track is also concerned with the analyses of continuous search spaces to better understand the complexity of optimization problems and benchmarking of continuous optimization.

Scope

The ENUM track invites submissions that present original work regarding theoretical analysis, algorithmic design, and experimental validation of algorithms for optimization in continuous domains, including work on large-scale and budgeted optimization, handling of constraints, multi-modality, noise, uncertain and/or changing environments, and mixed-integer problems. Work that advances experimental methodology and benchmarking, problem and search space analysis is also encouraged.

Application papers reporting on solving a particular real-world optimization problem with continuous search space, with a relevant methodology, should be sent primarily to the Real-World Applications (RWA) track, with ENUM being a possible secondary track. On the other hand, if one or more "real-world-like" problems are used as a testbed for a comparison of several relevant methods, ENUM is the right primary track.

Papers dealing with theoretical analyses of evolutionary algorithms in continuous search spaces should be primarily sent to the Theory Track, possibly with ENUM as a secondary track.

Track Chairs

Katherine Malan

University of South Africa | webpage

Katherine Malan is an associate professor in the Department of Decision Sciences at the University of South Africa. She received her PhD in computer science from the University of Pretoria in 2014 and her MSc & BSc degrees from the University of Cape Town. She has over 25 years' lecturing experience, mostly in Computer Science, at three different South African universities. Her research interests include automated algorithm selection in optimisation and learning, fitness landscape analysis and the application of computational intelligence techniques to real-world problems. She is editor-in-chief of South African Computer Journal, associate editor for Engineering Applications of Artificial Intelligence, and has served as a reviewer for over 20 Web of Science journals.

Youhei Akimoto

University of Tsukuba | webpage

Youhei Akimoto is an associate professor at University of Tsukuba, Japan. He received the B.S. degree in computer science in 2007, and the M.S. and Ph.D. degrees in computational intelligence and systems science from Tokyo Institute of Technology, Japan, in 2008 and 2011, respectively. From 2010 to 2011, he was a Research Fellow of JSPS in Japan, and from 2011 to 2013, he was a Post-Doctoral Research Fellow with INRIA in France. From 2013 to 2018, he was an Assistant Professor with Shinshu University, Japan. Since 2018, he has been an Associate Professor with University of Tsukuba, Japan as well as a Visiting Researcher with the Center for Advanced Intelligence Projects, RIKEN. He served as a Track Chair for the continuous optimization track of GECCO in 2015 and 2016. He is an Associate Editor of ACM TELO and is on the editorial board of the ECJ. He won the Best Paper Award at GECCO 2018 and FOGA 2019. His research interests include design principles, theoretical analyses, and applications of stochastic search heuristics and reinforcement learning algorithms.


GA - Genetic Algorithms

Description

The Genetic Algorithm (GA) track has always been a large and important track at GECCO. We invite submissions to the GA track that present original work on all aspects of genetic algorithms, including, but not limited to:

  • Practical, methodological and foundational aspects of GAs
  • Design of new GA operators including representations, fitness functions, initialization, termination, parent selection, replacement strategies, recombination, and mutation
  • Design of new and improved GAs
  • Fitness landscape analysis
  • Comparisons with other methods (e.g., empirical performance analysis)
  • Design of tailored GAs for new application areas
  • Handling uncertainty (e.g., dynamic and stochastic problems, robustness)
  • Metamodeling and surrogate assisted evolution
  • Interactive GAs
  • Co-evolutionary algorithms
  • Parameter tuning and control (including adaptation and meta-GAs)
  • Constraint Handling
  • Diversity management (e.g., fitness sharing and crowding, automatic speciation, spatial models such as island/diffusion)
  • Bilevel and multi-level optimization
  • Ensemble based genetic algorithms
  • Model-Based Genetic Algorithms


As a large and diverse track, the GA track will be an excellent opportunity to present and discuss your research/application with a wide variety of experts and participants of GECCO.

Track Chairs

Aneta Neumann

The University of Adelaide, Australia | webpage

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.

Elizabeth Wanner

School of Engineering and Applied Science, Aston University | webpage

I re-joined Aston in 2023, and am a Reader in Computer Science. My research is concerned with population-based multiobjective optimization and metaheuristics, multi-criteria decision analysis, and mathematical and statistical aspects of optimization theory. Previously, I was a Professor at the CEFET-MG (CERCIA) in the Department of Computer Engineering, Belo Horizonte, Brazil. I obtained my Ph.D. at the Universidade Federal de Minas Gerais, on the topic of Local Search operators for Genetic Algorithms based on derivative-free quadratic approximation. I also hold an MSc in Mathematics from the Universidade Federal de Minas Gerais, and before that, I read for a BSc in Mathematics at the Universidade Federal de Minas Gerais.


GECH - General Evolutionary Computation and Hybrids

Description

General Evolutionary Computation and Hybrids is a track focusing on how EAs are used as part of larger systems in synergy with other algorithms, including hybrid methods and other, more general combinations of EAs with other components. We also welcome high-quality contributions on a wide range of EA topics which do not fit exclusively into other GECCO tracks. We don’t consider hybrids based only on superficial metaphors (Sörensen, 2015) as on-topic for this track.

Scope

Areas of interest include the following - but the limit should be set by your creativity not ours:

  • Combining EAs with mechanisms to control or coordinate a set of algorithms, such as hyper-heuristics (selective and generative);
  • Combining EAs with constructive heuristics;
  • Combining EAs with classical methods (linear and integer programming, dynamic programming, constraint programming, etc.);
  • Combining EAs and traditional AI methods such as A-star, tree search, Monte Carlo tree search;
  • EAs incorporating multi-fidelity and multi-resolution objective function evaluation techniques;
  • Hybridising approaches such as EA+EA (e.g., meta-EA), EA+PSO, EA+ACO, EA+LS (memetic), EA+Fuzzy;
  • EA+A-life including co-evolutionary methods, both competitive and co-operative;
  • Search algorithms combining quantum and classical computation;
  • EAs using special techniques for parallel and distributed computing, or high performance hardware such as GPUs;
  • Hybrid EAs which use landscape analysis techniques as part of the search.

Track Chairs

Alberto Moraglio

University of Exeter, UK | webpage

Alberto Moraglio is a Senior Lecturer at the University of Exeter, UK. He holds a PhD in Computer Science from the University of Essex and Master and Bachelor degrees (Laurea) in Computer Engineering from the Polytechnic University of Turin, Italy. He is the founder of a Geometric Theory of Evolutionary Algorithms, which unifies Evolutionary Algorithms across representations and has been used for the principled design and rigorous theoretical analysis of new successful search algorithms. He gave several tutorials at GECCO, IEEE CEC and PPSN, and has an extensive publication record on this subject. He has served as co-chair for the GP track, the GA track and the Theory track at GECCO. He also co-chaired twice the European Conference on Genetic Programming, and is an associate editor of Genetic Programming and Evolvable Machines journal. He has applied his geometric theory to derive a new form of Genetic Programming based on semantics with appealing theoretical properties which is rapidly gaining popularity in the GP community. In the last three years, Alberto has been collaborating with Fujitsu Laboratories on Optimisation on Quantum Annealing machines. He has formulated dozens of Combinatorial Optimisation problems in a format suitable for the Quantum hardware. He is also the inventor of a software (a compiler) aimed at making these machines usable without specific expertise by automating the translation of high-level description of combinatorial optimisation problems to a low-level format suitable for the Quantum hardware (patented invention).

James McDermott

University of Galway, Ireland | webpage

James McDermott is Lecturer and Director of Research in the School of Computer Science, University of Galway, Ireland. He has previously worked and studied in Hewlett-Packard, University of Limerick, University College Dublin, and Massachussetts Institute of Technology. His research interests are in artificial intelligence, including genetic programming, evolutionary optimisation, and deep learning, with applications in sustainability and AI music. He has chaired international conferences including EuroGP and EvoMUSART, and is a member of the Genetic Programming and Evolvable Machines journal editorial board, and associate editor of the ACM SIGEvolution newsletter. He is leading Work Package 3 of the Horizon Europe Polifonia project in musical cultural heritage.


GP - Genetic Programming

Description

Genetic Programming is an evolutionary computation technique that automatically generates solutions/programs to solve a given problem. In GP, various representations have been used, such as tree structures, linear sequences of code, graphs and grammars. Provided that a suitable fitness function is devised, computer programs solving the given problem emerge, without the need for humans to explicitly program the computer. The GP track invites original contributions on all aspects of evolutionary generation of computer programs or other executable structures for specific tasks.

Scope

Advances in genetic programming include but are not limited to:

  • Analysis: Information Theory, Complexity, Run-time, Visualization, Fitness Landscape, Generalisation, Domain adaptation
  • Synthesis: Programs, Algorithms, Circuits, Systems
  • Applications: Classification, Clustering, Control, Data mining, Big-Data analytics, Regression, Semi-supervised Learning, Policy search, Prediction, Continuous and Combinatorial Optimisation, Streaming Data, Design, Inductive Programming, Computer Vision, Feature Engineering and Feature Selection, Natural Language Processing
  • Environments: Static, Dynamic, Interactive, Uncertain
  • Operators: Replacement, Selection, Crossover, Mutation, Variation
  • Performance: Surrogate functions, Multi-Objective, Coevolutionary, Human Competitive, Parameter Tuning
  • Populations: Demes, Diversity, Niches
  • Programs: Decomposition, Modularity, Semantics, Simplification, Software Improvement, Bug Repair, Software/Program Testing
  • Programming Languages: Imperative, Declarative, Object-oriented, Functional
  • Representations: Cartesian, Grammatical, Graphs, Linear, Rules, Trees, Geometric and Semantic
  • Systems: Autonomous, Complex, Developmental, Gene Regulation, Parallel, Self-Organizing, Software

Track Chairs

Ting Hu

School of Computing, Queen's University, Canada | webpage

Ting Hu is an Associate Professor at the School of Computing, Queen's University in Kingston, Canada. She received her PhD in Computer Science from Memorial University in St. John's, Canada and completed her postdoctoral training in bioinformatics from Dartmouth College in Hanover, New Hampshire, USA. Her research focuses on evolutionary algorithm methodology and its applications in biomedicine, and recently on explainable AI and interpretable machine learning. Ting is an Area Editor of the journal Genetic Programming and Evolvable Machines and an Associate Editor of the journal Neurocomputing. Ting has served as program co-chairs for EuroGP and GECCO-GP track.

Aniko Ekart

Aston University, UK | webpage

Anikó Ekárt is professor of Artificial Intelligence at Aston University and Director of the new Aston Centre for Artificial Intelligence Research and Application (ACAIRA). Following her PhD at the Eötvös Loránd University, Hungary, she worked at the University of Birmingham as lecturer and at the Institute for Computer Science and Control, Budapest Hungary as senior research fellow. Her research interests are centred around artificial intelligence methods and their application, with a focus on evolutionary algorithms and genetic programming in particular. Following genetic programming performance improving methods, she has successfully contributed to applications of AI techniques to health, engineering, transport, and art. She is Partner Editor for the Springer Nature Computer Science - SPECIES partnership. In 2022 she was the winner of the Evo* Award for Outstanding Contribution to Evolutionary Computation in Europe.


L4EC - Learning for Evolutionary Computation

Description

The "Learning for Evolutionary Computation" (L4EC) track was initiated this year to recognize high-quality research that uses machine learning (ML) or statistical techniques and concepts to improve heuristics and algorithmic components in the field of evolutionary computation (EC).

Scope

This track focuses on heuristics, methods, and concepts that leverage machine learning (including deep learning and reinforcement learning) or statistics to enhance EC methods.

As such, topics of interest include, but are not limited to:

  • Methods for automated algorithm design, selection, and configuration,
  • Mechanisms that learn how to control or coordinate a set of EC algorithms, such as parameter tuning, parameter control, dynamic algorithm selection/configuration, and meta-heuristics,
  • EC algorithms integrating methods to extract knowledge from the search trajectory and/or the genotype,
  • Surrogate-based or surrogate-assisted optimization of expensive fitness functions, including multi-fidelity approaches,
  • Feature-based methods that learn to characterize optimization problems, such as exploratory landscape analysis (ELA) and fitness landscape analysis.

In focusing on the use of learning methods for EC, this track complements the existing EML track, which focuses on the use of EC for machine learning problems.

Track Chairs

Pascal Kerschke

TU Dresden, Germany | webpage

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.

Marie-Eléonore Kessaci

Université de Lille, France | webpage

Marie-Eléonore Kessaci carries out her research in the ORKAD team and she teaches in the department Informatique et Statistique (Computer science and statistics) in the engineering school Polytech Lille. She hold her habilitation (HDR in France), entitled "Knowledge-based Design of Stochastic Local Search Algorithms in Combinatorial Optimization", in November 2019. Between September 2012 and August 2013, she had a postdoctoral position at Université Libre de Bruxelles. She got her PhD in Computer Science in December 2011 at Université Lille 1.


NE - Neuroevolution

Description

Neuroevolution is a machine learning approach that applies evolutionary computation (EC) to constructing artificial neural networks (NNs). Compared with other neural network training methods, Neuroevolution is highly general and allows learning without explicit targets, with arbitrary neural models and network structures. Neuroevolution has been successfully used to address challenging tasks in a wide range of areas, such as reinforcement learning, supervised learning, unsupervised learning, image analysis, computer vision, and natural language processing.

The Neuroevolution track at GECCO aims to encourage knowledge exchange between interested researchers in this area. It covers advances in the theory and applications of Neuroevolution, including all different EC methods for evolving all types of neural networks, alone and in combination with other neural learning algorithms. Authors are invited to submit their original and unpublished work to this track.

Scope

More concretely, topics of interest include but are not limited to:

  • Neuroevolution algorithms involving:
    • Any EC method, e.g. genetic algorithms, evolutionary strategy, and genetic programming, particle swarm optimisation, differential evolution, meta-heuristics, Quality-Diversity, and hybrid methods.
    • Any type of neural networks, e.g. Convolutional neural network (CNN), Recurrent neural network (RNN), Long short-term memory (LSTM), Deep Belief Network (DBN), transformers, and autoencoders.
    • Evolutionary neural architecture search
    • Optimisation of network hyperparameters, activation and loss functions, learning dynamics, data augmentation, and initialisation
    • Novel candidate representations
    • Novel search mechanisms
    • Novel fitness functions
    • Surrogate assisted Neuroevolution
    • Methods for improving efficiency
    • Methods for improving regularisation
    • Multi-objective Neuroevolution
    • Neuroevolution for reinforcement learning, supervised learning, unsupervised learning
    • Neuroevolution for transfer learning, one-short learning, few-short learning, multitask learning
    • Parallelised and distributed realisations of Neuroevolution
    • Combinations of Neuroevolution and other neural learning algorithms
    • Interpretable/explainable model learning
  • Applications of Neuroevolution:
    • Computer vision, image processing and pattern recognition
    • Text mining, natural language processing
    • Speech recognition
    • Neural Architecture Search
    • Machine translation
    • Medical and biological problems
    • Evolutionary robotics
    • Artificial life
    • Time series analysis
    • Cyber security
    • Scheduling and combinatorial optimization
    • Healthcare
    • Finance, fraud detection and business
    • Social media data analysis
    • Game playing
    • Visualisation

Track Chairs

Gabriela Ochoa

University of Stirling, UK | webpage

Gabriela Ochoa is a Professor of Computing Science at the University of Stirling in Scotland, UK. Her research lies in the foundations and applications of evolutionary algorithms and metaheuristics, with emphasis on adaptive search, fitness landscape analysis and visualisation. She holds a PhD from the University of Sussex, UK, and has worked at the University Simon Bolivar, Venezuela, and the University of Nottingham, UK. Her Google Scholar h-index is 40, and her work on network-based models of computational search spans several domains and has obtained 4 best-paper awards and 8 other nominations. She collaborates cross-disciplines to apply evolutionary computation in healthcare and conservation. She has been active in organisation and editorial roles in venues such as the Genetic and Evolutionary Computation Conference (GECCO), Parallel Problem Solving from Nature (PPSN), the Evolutionary Computation Journal (ECJ) and the ACM Transactions on Evolutionary Learning and Optimisation (TELO). She is a member of the executive board for the ACM interest group in evolutionary computation, SIGEVO, and the editor of the SIGEVOlution newsletter. In 2020, she was recognised by the leading European event on bio-inspired algorithms, EvoStar, for her outstanding contributions to the field.

Antoine Cully

Imperial College London, UK | webpage

Antoine Cully is Lecturer (Assistant Professor) at Imperial College London (United Kingdom). His research is at the intersection between artificial intelligence and robotics. He applies machine learning approaches, like evolutionary algorithms, on robots to increase their versatility and their adaptation capabilities. In particular, he has recently developed Quality-Diversity optimization algorithms to enable robots to autonomously learn large behavioural repertoires. For instance, this approach enabled legged robots to autonomously learn how to walk in every direction or to adapt to damage situations. Antoine Cully received the M.Sc. and the Ph.D. degrees in robotics and artificial intelligence from the Sorbonne Université in Paris, France, in 2012 and 2015, respectively, and the engineer degree from the School of Engineering Polytech’Sorbonne, in 2012. His Ph.D. dissertation has received three Best-Thesis awards. He has published several journal papers in prestigious journals including Nature, IEEE Transaction in Evolutionary Computation, and the International Journal of Robotics Research. His work was featured on the cover of Nature (Cully et al., 2015), received the "Outstanding Paper of 2015" award from the Society for Artificial Life (2016), the French "La Recherche" award (2016), and two Best-Paper awards from GECCO (2021, 2022).


RWA - Real World Applications

Description

The Real-World Applications (RWA) track welcomes rigorous experimental, computational and/or applied advances in evolutionary computation (EC) in any discipline devoted to the study of real-world problems. The RWA track covers also real-world problems arising in creative arts, including design, games, and music (having been merged with the former track DETA - Digital Entertainment Technologies and Arts). The aim is to bring together contributions from the diverse application domains into a single event. The focus is on applications including but not limited to:

  • Papers that present novel developments of EC, grounded in real-world problems.
  • Papers that present new applications of EC to real-world problems.
  • Papers that analyse the features of real-world problems, as a basis for designing EC solutions.
  • Papers that would fall into the DETA domain, such as ones focussing on aesthetic measurement and control, biologically-inspired creativity, interactive environments and games, composition, synthesis and generative arts.


All contributions should be original research papers demonstrating the relevance and applicability of EC within a real-world problem. Papers covering multiple disciplines are welcome; we encourage the authors of such papers to write and present them in a way that allows researchers from other fields to grasp the main results, techniques, and their potential applications. Papers on novel EC research problems and novel application domains of the arts, music, and games are especially encouraged.

Scope

The real-world applications track is open to all domains and all industries.

Track Chairs

Ruhul Sarker

School of Systems & Computing, UNSW Canberra | webpage

Ruhul A Sarker obtained his PhD from Dalhousie University (former TUNS), Canada. He is a Professor in the School of Systems & Computing at UNSW Canberra located at ADFA. He served as the Director of Faculty PG Research (June 2015 to May 2020) and as the Deputy Head of School (Research) of SEIT (2011-2014). Prof. Sarker’s broad research interests are decision analytics, CI / evolutionary computation, operations research, and applied optimization with an emphasis on Augmenting Human Intelligence (AHI). His name appeared on the recent lists of top 2% of world's scientists-researchers prepared by (i) Stanford University and also (ii) Elsevier/Scopus/digitalcommonsdata (Research fields: Artificial Intelligence and Operations Research).

Patrick Siarry

Université Paris-Est Créteil (UPEC), Laboratoire Images, Signaux et Systèmes Intelligents, France | webpage

Patrick Siarry received the PhD degree from the University Paris 6, in 1986 and the Doctorate of Sciences (Habilitation) from the University Paris 11, in 1994. He was first involved in the development of analog and digital models of nuclear power plants at Electricité de France (E.D.F.). Since 1995 he is a professor in automatics and informatics. His main research interests are the development and the applications of new stochastic global optimization heuristics to various engineering fields. He is also interested in the fitting of process models to experimental data, the learning of fuzzy rule bases and neural networks.


SBSE - Search-Based Software Engineering

Description

Search-Based Software Engineering (SBSE) is the application of search algorithms and strategies to software engineering problems. Evolutionary computation is a foundation of SBSE, and since 2002 the SBSE track at GECCO has provided the unique opportunity to present SBSE research in the widest context of the evolutionary computation community. Last but not least, participating in the SBSE track and, more generally, to GECCO allows us to be informed of advances in evolutionary computation, new cutting edge metaheuristic ideas, novel search strategies, approaches and findings.

We invite papers that address problems in the software engineering domain through the use of heuristic search. We particularly encourage papers demonstrating novel applications and adaptations of existing or new search strategies to software engineering problems framed as optimization tasks. While empirical results are important, papers that do not contain strong empirical results - but instead present new sound approaches, concepts, or theory in the search-based software engineering area - are also very welcome.

We also encourage the submission of both full papers and poster-only papers describing negative results as well as industrial reports on the practical use of search-based solution approaches. Moreover poster-only papers presenting frameworks or tools for search-based software engineering are also welcome.

Scope

The SBSE Track covers all work in which evolutionary and search techniques are applied to software engineering tasks.
Topics of interest include (but are not limited to):

  • Automated Program Repair and Bug Fixing
  • Creating Recommendation Systems to Support Life Cycle (Software Requirement, Design, Development, Evolution and Maintenance, etc.)
  • Developing Dynamic Service-Oriented and Mobile Systems
  • Enabling Self-Configuring/Self-Healing/Self-Optimising Software Systems
  • Network Design and Monitoring
  • Predictive Modelling and Analytics for Software Engineering Tasks
  • Project Management and Planning
  • Testing including test data generation, regression test optimisation, test suite evolution
  • Requirements Engineering
  • Software Evolution and Maintenance
  • Software Security
  • Software Transplantation
  • System and Software Integration and Verification
  • Uncertainty Processing in Software Life Cycle
  • Software Architecture

Track Chairs

Dominik Sobania

Johannes Gutenberg University Mainz, Germany | webpage

Dominik Sobania received a bachelor's degree in computer science from the Johannes Gutenberg University, Mainz, Germany, and master's degrees in internet- and web-based systems as well as in computer science from the Technical University Darmstadt, Darmstadt, Germany. Currently, he works as a researcher at the Johannes Gutenberg University, Mainz, Germany, where he also received his PhD. His current research focuses on automatic program synthesis with genetic programming. In particular, he studies the structure and the generalization ability of the generated programs.

 

Aymeric Blot

University of Rennes, FR | webpage

Aymeric Blot is an senior lecturer (associate professor) at the University of Rennes, France, conducting research in genetic improvement of software. Before that he worked at the University of the Opal Coast in Calais and at the University College London. He received in 2018 a doctorate from the University of Lille following work on automated algorithm design for multi-objective combinatorial optimisation. His research focuses on strengthening GI techniques using knowledge from automated machine learning, algorithm configuration, and evolutionary computation. He maintains the community website on genetic improvement.


SI – Swarm Intelligence

Description

Swarm Intelligence (SI) is the collective problem-solving behavior of groups of animals or artificial agents that results from the local interactions of the individuals with each other and with their environment. SI systems rely on certain key principles such as decentralization, stigmergy, self-organization, local interaction, and emergent behaviors. Since these principles are observed in the organization of social insect colonies and other animal aggregates, such as bird flocks or fish schools, SI systems are typically inspired by these natural systems.
The two main application areas of SI have been optimization and robotics. In the first category, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) constitute two of the most popular SI optimization techniques with numerous applications in science and engineering, but other SI-based optimization algorithms are possible. Papers that study and compare SI mechanisms that underly these different SI approaches, both theoretically and experimentally, are welcome. In the second category, SI has been successfully used to control large numbers of robots in a decentralized way, which increases the flexibility, robustness, and fault-tolerance of the resulting systems.

Scope

The ACO-SI Track welcomes submissions of original and unpublished work in all experimental and theoretical aspects of SI, including (but not limited to) the following areas:

  • Biological foundations
  • Modeling and analysis of new approaches
  • Hybrid schemes with other algorithms
  • Multi-swarm and self-adaptive approaches
  • Constraint-handling and penalty function approaches
  • Combinations with local search techniques
  • Approaches to solve multi- and many-objective optimization problems
  • Approaches to solve dynamic and noisy optimization problems
  • Approaches to multi-modal optimization, i.e., to find multiple solutions (niching)
  • Benchmarking and new empirical results
  • Parallel/distributed implementations and applications
  • Large-scale applications
  • Software and high-performance implementations
  • Theoretical and experimental research in swarm robotics
  • Theoretical and empirical analysis of SI approaches to gain a better understanding of SI algorithms and to inform on the development of new, more efficient approaches
  • Position papers on future directions in SI research
  • Applications to machine learning and data analytics

Track Chairs

Christian Blum

Artificial Intelligence Research Institute (IIIA-CSIC), Spain | webpage

Christian Blum received a PhD degree in Applied Sciences from the Free University of Brussels, Belgium, in 2004. He is currently a Senior Research Scientist with the Artificial Intelligence Research Institute (IIIA-CSIC), Bellaterra, Spain. His research interests include solving complex optimization problems using swarm intelligence techniques and combinations of metaheuristics with exact techniques. He currently acts as editor for the journal Computers & Operations Research. In 2021, he won the SEIO-FBBVA award (a Spanish national award) for the best methodological contribution in Operations Research. During his career, he has published more than 200 papers in journals, books, and conferences. To date, his work has received more than 17.000 citations and his current h-index is 43.

Paola Pellegrini

Université Gustave Eiffel | webpage

Paola Pellegrini works on optimization algorithms for difficult real-world problems. These algorithms span from mixed-integer programming to metaheuristics. Particularly, she is an expert in railway planning and operational management. Her current research field covers the development of optimization approaches to effectively exploit railway infrastructure capacity, aiming to process automation. In particular, she has designed a state-of-the-art algorithm for the centralized real-time railway traffic management problem named RECIFE-MILP.
Paola Pellegrini is member of the Board of the International Association of Railway Operations Research (IAROR) and has covered leading roles in a number of European research projects. She is member of the editorial board of Engineering Applications of Artificial Intelligence and IET Intelligent Transport Systems.


THEORY - Theory

Description

The theory track welcomes all papers performing theoretical analyses or concerning theoretical aspects in evolutionary computation and related areas. Results can be proven with mathematical rigor or obtained via a thorough experimental investigation.

In addition to traditional areas in evolutionary computation like Genetic and Evolutionary Algorithms, Evolutionary Strategies, and Genetic Programming we also highly welcome theoretical papers in Artificial Life, Ant Colony Optimization, Swarm Intelligence, Estimation of Distribution Algorithms, Generative and Developmental Systems, Evolutionary Machine Learning, Search Based Software Engineering, Population Genetics, and more.

Scope

Topics include (but are not limited to):

  • analytical methods like drift analysis, fitness levels, Markov chains, large deviation bounds,
  • dynamic and static parameter choices,
  • fitness landscapes and problem difficulty,
  • population dynamics,
  • problem representation,
  • runtime analysis, black-box complexity, and alternative performance measures,
  • single- and multi-objective problems,
  • statistical approaches,
  • stochastic and dynamic environments,
  • variation and selection operators.


Papers submitted to the theory track may contain an appendix to give additional information. The appendix will not be part of the proceedings, and is consulted only at the discretion of the program committee. All technical details necessary for a proper evaluation must be contained in the 8-page submission or in the appendix, including full proofs and/or complete descriptions of experiments.

Track Chairs

Benjamin Doerr

École Polytechnique, France | webpage

Benjamin Doerr is a full professor at the French Ecole Polytechnique. He received his diploma (1998), PhD (2000) and habilitation (2005) in mathematics from Kiel University. His research area is the theory of both problem-specific algorithms and randomized search heuristics like evolutionary algorithms. Major contributions to the latter include runtime analyses for existing evolutionary algorithms, the determination of optimal parameter values, and complexity theoretic results. Benjamin's recent focus is the theory-guided design of novel operators, on-the-fly parameter choices, and whole new evolutionary algorithms, hoping that theory not only explains, but also develops evolutionary computation.

Together with Frank Neumann and Ingo Wegener, Benjamin Doerr founded the theory track at GECCO and served as its co-chair 2007-2009, 2014, and 2023. He is a member of the editorial boards of "Artificial Intelligence", "Evolutionary Computation", "Natural Computing", "Theoretical Computer Science", and three journals on classic algorithms theory. Together with Anne Auger, he edited the the first book focused on theoretical aspects of evolutionary computation ("Theory of Randomized Search Heuristics", World Scientific 2011). Together with Frank Neumann, he is an editor of the recent book "Theory of Evolutionary Computation - Recent Developments in Discrete Optimization" (Springer 2020).

Christine Zarges

Aberystwyth University, Wales, UK | webpage

Christine Zarges is currently a Senior Lecturer (Associate Professor) in the Department of Computer Science at Aberystwyth University which she joined as a Lecturer in 2016. Before, she held a postdoctoral research position at the University of Warwick, UK, and a Birmingham Fellowship at the University of Birmingham, UK. She obtained her PhD from TU Dortmund, Germany, in 2011.

Christine's research focuses on heuristic search in the context of optimisation. She is particularly interested in the theoretical analysis of all kinds of randomised search heuristics such as evolutionary algorithms and artificial immune systems with the aim to understand their working principles and guide their design and application. She is also interested in applications in combinatorial optimisation as well as computational and theoretical aspects of natural processes and systems. She has given tutorials on these topics at various conferences and workshops and contributed to the organisation of these conferences in different capacities, most importantly as track and event chair at GECCO, workshop chair at PPSN, and programme chair at FOGA and EvoCop. She will also act as local chair of EvoStar 2024. She is member of the editorial board of Evolutionary Computation (MIT Press) and Associate Editor of Engineering Applications of Artificial Intelligence (Elsevier). She is a member of the Executive Board of SPECIES, the Society for the Promotion of EC In Europe and Surroundings, and a Management Committee member for the UK in European research networks concerned with Randomised Optimisation Algorithms (COST actions CA15140 and CA22137).