|ACO-SI - Ant Colony Optimization and Swarm Intelligence|
|CS - Complex Systems|
|ECOM - Evolutionary Combinatorial Optimization and Metaheuristics|
|EML - Evolutionary Machine Learning|
|EMO - Evolutionary Multiobjective Optimization|
|ENUM - Evolutionary Numerical Optimization|
|GA - Genetic Algorithms|
|GECH - General Evolutionary Computation and Hybrids|
|GP - Genetic Programming|
|NE - Neuroevolution|
|RWA - Real World Applications|
|SBSE - Search-Based Software Engineering|
|THEORY - Theory|
ACO-SI - Ant Colony Optimization and Swarm Intelligence
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.
The ACO-SI Track welcomes submissions of original and unpublished work in all experimental and theoretical aspects of SI, including (but not limited to) the following areas: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
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.
Taiyuan University of Science and Technology, China | webpage
Dr. Chaoli Sun received her B.C. and M.S. degrees in Computer Application Technology from Hohai University, Nanjing, Jiangsu, China, and Ph.D. in Mechanical Design and Theory from Taiyuan University of Science and Technology, Taiyuan, Shanxi, China, in 2011. From September 2014 to September 2016, she was a Postdoctoral Research Fellow in Department of Computer science, University of Surrey. Now she is a Professor in the School of Computer Science and Technology, Taiyuan University of Science and Technology. Her areas of expertise include evolutionary computation, swarm intelligence, self-organized robotic systems, fitness estimation and surrogate assisted evolutionary optimization with application to mechanical structural optimization. Prof. Sun is an Associate Editor of the IEEE Transactions on Evolutionary Computation, an Associate Editor of the IEEE Transactions on Artificial Intelligence, and an Associate Editor of the Soft Computing Journal. She is also an Editorial Board Member of Complex and Intelligence Systems and an Editorial Board Member of Memetic Computing. She is a member of the Evolutionary Computation Technical Committee of IEEE CIS and a member of the Intelligent Systems Application Technical Committee of IEEE CIS. She was the chair of TF on Data-Driven
Evolutionary Optimization of Expensive Problems (2015-2020).
CS - Complex Systems
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.
Sorbonne Université, France | webpage
Nicolas Bredeche is Professeur des Universités (full professor) in computer science at Sorbonne Université in Paris, France. He is a member of the Institut des Systèmes Intelligents et de Robotique (Campus Pierre et Marie Curie, CNRS). His research activity revolves around adaptive collective systems with two motivations:
(1) to understand natural systems, using individual-based modeling and simulation methods (e.g.: collective decision making, evolution of cooperation) and
(2) to design adaptive collective/swarm robotic systems using evolutionary and social learning algorithms (e.g.: behavior optimization for collective robotics, online distributed evolutionary learning for swarm robotics).
He is particularly interested in how a collective of individuals, whether artificial or natural, can learn how to self-organize and survive together in open environments.
Dept Computer Science, University of York, UK | webpage
Susan Stepney is Professor of Computer Science at the University of York, UK, where she leads the Non-Standard Computing research group. She has an MA in theoretical physics, an MMath in applied mathematics, and a PhD in theoretical astrophysics, from the University of Cambridge. She worked in commercial R&D for 18 years before rejoining academia in 2002. Her current research interests include Unconventional Computing, including in materio reservoir computing and computational models for synthetic biology; and Artificial Life, including artificial chemistries, life as a cyber-bio-physical system, and open ended evolution. She is an Associate Editor of the Cambridge University Press journal "Programmble Materials", co-Editor in Chief of the MIT Press journal "Artificial Life", and a board member of ISAL, the International Society for Artificial Life.
ECOM - Evolutionary Combinatorial Optimization and Metaheuristics
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.
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
- Hybrid methods, adaptive hybridization techniques and memetic computing
- Hyper-heuristics specific to combinatorial optimization problems
- 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.
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. 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.
University of Lille, CRIStAL, Inria Lille Nord Europe, France | webpage
Bilel Derbel is an associate Professor, with a research habilitation/accreditation, at the Department of Computer Science at the University of Lille, France. He is deputy team leader of BONUS (Big Optimization aNd Ultra-Scale Computing), a joint research group at the CRIStAL Laboratory CNRS and the Inria Lille Nord Europe research center, France. He is a Collaborative Professor at Shinshu University, Nagano, Japan, and a co-founder member of the MODŌ International Associated Laboratory (Massive optimization and Computational Intelligence) with Shinshu University. His current research topics are on the design and analysis of algorithms for solving complex and large scale optimization problems using high-level optimization techniques, stochastic heuristics, ML-inspired search techniques, parallel and distributed computing, multi-objective algorithms.
EML - Evolutionary Machine Learning
The Evolutionary Machine Learning (EML) track at GECCO covers advances in the theory and application of evolutionary computation methods to 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, interpretability of machine learning models, and learning with unbalanced data and missing data.
The global search performed by evolutionary methods frequently provides a valuable complement to the local search of non-evolutionary machine learning methods and combinations of the two often show particular promise in practice.
Authors are strongly encouraged to compare their approach to the state-of-the-art in non-evolutionary machine learning, where appropriate.
We encourage submissions related to theoretical advances, the development of new (or modification of existing) algorithms, as well
as application-focused papers.
More concretely, topics of interest include but are not limited to:
- Theoretical and methodological advances on EML
- Evolutionary ensemble learning
- Evolutionary representation learning, transfer learning and domain adaptation
- Learning Classifier Systems (LCS) and evolutionary rule-based systems
- Genetic Programming (GP) when applied to machine learning tasks (as opposed to function optimisation)
- Hyper-parameter tuning of machine learning (i.e. AutoML approaches) using evolutionary methods
- Evolutionary learning with a small number of examples, unbalanced data or missing data values
- Other EC (e.g. particle swarm optimisation, differential evolution) for machine learning tasks
- Evolutionary computation techniques for feature extraction, feature selection, and feature construction
- Visualising and improving the interpretability of machine learning models
- Generalisation and overfitting
- Policy search and reinforcement learning
- Analysis and robustness in stochastic, noisy, or non-stationary environments
- Scalable, parallel and distributed EML, including approaches such as high performance computing, federated learning, edge computing or GPUs/TPUs
- Non tabular data modalities (e.g. image, sound, accelerometers) and their integration
- Applications of EML (non-exhaustive list):
- Computer vision, image processing and pattern recognition
- Data mining
- Bioinformatics and life sciences
- Computer vision, image processing and pattern recognition
- Dynamic environments, time series and sequence learning
- Cognitive systems and cognitive modelling
- Economic modelling
- Cyber security
Alliance Manchester Business School, University of Manchester, UK | webpage
Julia Handl obtained a Bsc (Hons) in Computer Science from Monash University in 2001, an MSc degree in Computer Science from the University of Erlangen-Nuremberg in 2003, and a PhD in Bioinformatics from the University of Manchester in 2006. From 2007 to 2011, she held an MRC Special Training Fellowship at the University of Manchester, and she is now a Professor in Decision Sciences at Alliance Manchester Business School. A core strand of her work explores the use of multiobjective optimization in unsupervised and semi-supervised classification. She has developed multiobjective algorithms for clustering and feature selection tasks in these settings, and her work has highlighted some of the theoretical and empirical advantages of this approach.
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.
EMO - Evolutionary Multiobjective Optimization
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.
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
- Parallel and distributed models
- Stopping criteria
- Theoretical foundations and search space analysis that bring new insights to EMO
- Implementation aspects
- Algorithm selection and configuration
- 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
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 was EMO track co-chair at GECCO'2013 and GECCO'2014.
The University of Electro-Communications, Japan | webpage
Hiroyuki Sato received B.E. and M.E. degrees from Shinshu University, Japan, in 2003 and 2005, respectively. In 2009, he received Ph. D. degree from Shinshu University. He has worked at The University of Electro-Communications since 2009. He is currently an associate professor. He received best paper awards on the EMO track in GECCO 2011 and 2014, Transaction of the Japanese Society for Evolutionary Computation in 2012, 2015, and 2020. His research interests include evolutionary multi- and many-objective optimization, and its applications. He is a member of IEEE, ACM/SIGEVO.
ENUM - Evolutionary Numerical Optimization
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.
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.
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.
Tel-Hai College and Migal Institute, Israel | webpage
Ofer Shir is an Associate Professor of Computer Science at Tel-Hai College and a Principal Investigator at Migal-Galilee Research Institute – both located in the Upper Galilee, Israel. Ofer Shir holds a BSc in Physics and Computer Science from the Hebrew University of Jerusalem, Israel (conferred 2003), and both MSc and PhD in Computer Science from Leiden University, The Netherlands (conferred 2004, 2008; PhD advisers: Thomas Bäck and Marc Vrakking). Upon his graduation, he completed a two-years term as a Postdoctoral Research Associate at Princeton University, USA (2008-2010), hosted by Prof. Herschel Rabitz in the Department of Chemistry – where he specialized in computational aspects of experimental quantum systems. He then joined IBM-Research as a Research Staff Member (2010-2013), which constituted his second postdoctoral term, and where he gained real-world experience in convex and combinatorial optimization as well as in decision analytics. His current topics of interest include Statistical Learning within Optimization and Deep Learning in Practice, Self-Supervised Learning, Algorithmically-Guided Experimentation, Combinatorial Optimization and Benchmarking (White/Gray/Black-Box), Quantum Optimization and Quantum Machine Learning.
GA - Genetic Algorithms
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 hybrid approaches (e.g., memetic algorithms)
- 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.
The University of Adelaide, Australia | webpage
Aneta Neumann graduated in Computer Science from the Christian-Albrechts-University of Kiel, Germany and received her PhD from the University of Adelaide, Australia. She is currently a researcher at the School of Computer Science, The University of Adelaide, Australia. She received the Lorentz Center Grant 2020, Optimization Meets Machine Learning, Leiden, The Netherlands, the ACM Women scholarship, sponsored by Google, Microsoft, and Oracle, the Hans-Juergen and Marianna Ohff Research Grant in 2018, and the Best Paper Nomination at GECCO 2019 and 2021 in the track “Genetic Algorithms” and at GECCO 2022 in the track “Combinatorial Optimization and Metaheuristics“. She is a co-designer and co-lecturer for the EdX Big Data Fundamentals course in the Big Data MicroMasters® program and Online Pearson Education, Working with Big Data. Her main research interest focuses on bio-inspired computation, particularly dynamic and stochastic optimization, evolutionary diversity optimization, creative AI, and optimisation under uncertainty in practice. She served as the co-chair of the Real-World Applications track at GECCO 2021 and GECCO 2022 and is co-chair of the Genetic Algorithms track at GECCO 2023.
GECH - General Evolutionary Computation and Hybrids
General Evolutionary Computation and Hybrids is a track recognizing that EC methods are often used as part of larger complex systems or in synergy with other algorithms. We welcome high-quality contributions on a wide range of topics that do not fit exclusively into one of the other tracks.
Areas of interest include the following - but the limit should set by your creativity not ours:
- Combining EAs with mechanisms that attempt to learn how to control or to coordinate a set of algorithms, such as parameter tuning, parameter control, and hyper-heuristics,
- Combining EAs with machine learning algorithms (including reinforcement learning and deep learning), e.g., for automated algorithm selection and configuration,
- Surrogate-based or surrogate-assisted optimization of expensive fitness functions, including multi-fidelity approaches,
- Combining different approaches for creating or improving solutions such as co-evolution, neuro-evolution, memetic algorithms, and other hybrids,
- Algorithms that can handle dynamic and stochastic environments, or for settings that enable parallel evaluations,
- Statistical analysis and visualization techniques aimed at understanding problem spaces or the performance and behavior of optimization techniques, including instance space analysis and landscape analysis, as well as
- Toolboxes for better benchmarking of evolutionary algorithms.
CNRS and Sorbonne University, France | webpage
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.
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.
GP - Genetic Programming
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.
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
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. 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.
University of Zagreb, Faculty of electrical engineering and computing, Croatia | webpage
Domagoj Jakobovic is a full professor at the Faculty of Electrical Engineering and Computing, University of Zagreb. His research interests include evolutionary algorithms, optimization methods, machine learning and parallel algorithms. Most notable contributions are in the area of machine supported scheduling, generating scheduling heuristics with genetic programming, optimization problems in cryptography and security, parallelization and improvement of evolutionary algorithms. He has published more than 120 papers, lead several research projects and served as a reviewer and PC member for many international journals and conferences. He has supervised seven doctoral theses and more than 170 bachelor and master theses.
NE - Neuroevolution
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.
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
- Finance, fraud detection and business
- Social media data analysis
- Game playing
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).
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.
RWA - Real World Applications
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.
The real-world applications track is open to all domains and all industries.
University of Nottingham, UK | webpage
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.
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
Search-Based Software Engineering (SBSE) is the application of search algorithms and strategies to the solution of 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 to the SBSE track and, more generally, to GECCO allow to be informed by 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.
Moreover, 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.
As an indication of the wide scope of the field, search techniques include, but are not limited to:
- Ant Colony Optimisation
- Automatically configured and Tuned Algorithms
- Estimation of Distribution Algorithms
- Evolutionary Computation
- Genetic Programming
- Hybrid Algorithms and Neuroevolution
- Iterated Local Search
- Particle Swarm Optimisation
- Simulated Annealing
- Tabu Search
- Variable Neighbourhood Search
The software engineering tasks to which they are applied are drawn from throughout the engineering lifecycle and include, but are not limited to:
- 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
- Program Repair
- Software Security
- Software Transplantation
- System and Software Integration and Verification
- Uncertainty Processing in Software Life Cycle
- Software Architecture
University of Carthage, FSEG-Nabeul, Tunisia | webpage
Slim Bechikh is a Full Professor, having a Research Habilitation, at the department of computer science of the University of Carthage, FSEG-Nabeul, Tunisia. He received his PhD in Computer Science with Business in 2013 from the University of Tunis, ISG-Tunis, Tunisia. He is also a Research Director within the SMART laboratory at ISG-Tunis. He published over 90 papers in peer-reviewed international journals and conferences. His current research interests include evolutionary optimization, SBSE, machine learning, and business analytics. Dr. Bechikh was a recipient of the Best Paper Award of the ACM SAC-2010 in Switzerland. He supervised the Tunisian best national Doctoral thesis in ICT for the year 2019, which earned a presidential prize in scientific research and technology. He was promoted in August 2021 to the grade of IEEE Senior Member. He is Associate Editor for IEEE Transactions on Evolutionary Computation and Swarm and Evolutionary Computation. He serves as reviewer for seventy international journals and four conferences in artificial intelligence and its applications.
Johannes Gutenberg University, 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. Since 2017, he has worked as a research assistant at the Johannes Gutenberg University, Mainz, Germany. 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.
THEORY - Theory
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.
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.
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 and 2014. 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).
Pietro Simone Oliveto
University of Sheffield, UK | webpage
Pietro S. Oliveto is a Professor of Computer Science and Chair of Algorithms at the University of Sheffield, UK. He received the Laurea degree and PhD degree in computer science respectively from the University of Catania, Italy in 2005 and from the University of Birmingham, UK in 2009. He has been EPSRC PhD+ Fellow (2009-2010) and EPSRC Postdoctoral Fellow (2010-2013) at Birmingham and Vice-Chancellor's Fellow (2013-2016) and EPSRC Early Career Fellow at Sheffield.
His main research interest is the performance analysis of bio-inspired computation techniques including evolutionary algorithms, genetic programming, artificial immune systems, hyper-heuristics and algorithm configuration. He has guest-edited journal special issues of Computer Science and Technology, Evolutionary Computation, Theoretical Computer Science, IEEE Transactions on Evolutionary Computation and Algorithmica. He has co-Chaired the the IEEE symposium on Foundations of Computational Intelligence (FOCI) from 2015 to 2021 and has been co-program Chair of the ACM Conference on Foundations of Genetic Algorithms (FOGA 2021) and Theory Track co-chair at GECCO 2022. He is part of the Steering Committee of the annual workshop on Theory of Randomized Search Heuristics (ThRaSH), was Leader of the Benchmarking Working Group of the COST Action ImAppNIO, is member of the EPSRC Peer Review College and is Associate Editor of IEEE Transactions on Evolutionary Computation.