Three days of presentations of the latest high-quality results in 13 separate and independent program tracks specializing in various aspects of genetic and evolutionary computation.
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The ACO-SI Track welcomes submissions of original and unpublished work in all experimental and theoretical aspects of SI, including (but not limited to) the following areas: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
- 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
Professor of Computer Science at INSA Lyon
Researcher at LIRIS (Computational Geometry and Constrained Optimization team)
Andries Engelbrecht received the Masters and PhD degrees in Computer Science from the University of Stellenbosch, South Africa, in 1994 and 1999 respectively. He is Professor in Computer Science at the University of Pretoria, and is appointed as the Director of the Institute for Big Data and Data Science. He holds the position of South African Research Chair in Artificial Intelligence, and leads the Computational Intelligence Research Group. His research interests include swarm intelligence, evolutionary computation, neural networks, artificial immune systems, and the application of these paradigms to data mining, games, bioinformatics, finance, and difficult optimization problems. He has published over 350 papers in these fields and is author of two books, Computational Intelligence: An Introduction and Fundamentals of Computational Swarm Intelligence.
CS - Complex Systems (Artificial Life/Artificial Immune Systems/Generative and Developmental Systems/Evolutionary Robotics/Evolvable Hardware)
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.
Stéphane Doncieux is Professeur des Universités (Professor) in Computer Science at Sorbonne Université, Paris, France. His research is mainly concerned with the use of evolutionary algorithms in the context of optimization or synthesis of robot controllers. He worked in a robotics context to design, for instance, controllers for flying robots, but also in the context of modelling where he worked on the use of multi-objective evolutionary algorithms to optimize and study computational models. More recently, he focused on the use of multi-objective approaches to tackle learning problems like premature convergence or generalization.
He is the head of the AMAC (Architecture and Models of Adaptation and Cognition) research team with 12 permanent researchers, 3 post-doc students and 13 PhD students. Researchers of the team work on different aspects of learning in the context of motion control and cognition, both from a computational neuroscience perspective and a robotics perspective.
Sebastian Risi is an Associate Professor at the IT University of Copenhagen where he is part of the Center for Computer Games Research and the Robotics, Evolution and Art Lab (REAL). His interests include computational intelligence in games, neuroevolution, evolutionary robotics and human computation. Risi completed his PhD in computer science from the University of Central Florida. He has won several best paper awards at GECCO, EvoMusArt, IJCNN, and the Continual Learning Workshop at NIPS for his work on adaptive systems, the HyperNEAT algorithm for evolving complex artificial neural networks, and music generation.
DETA - Digital Entertainment Technologies and Arts
The intersection of culture, science and technology is attracting increasingly more public attention, with frequent exhibitions, competitions and industrial involvement worldwide.
The Digital Entertainment Technologies and Arts (DETA) track at GECCO, in its ninth edition in 2019, focusses on the key application fields of arts, music, and games from the perspective of evolutionary computation, biologically inspired techniques, and more generally computational intelligence.
We invite submissions describing original work involving the use of computational intelligence techniques in the creative arts, including design, games, and music. Works of a methodological, experimental, or theoretical nature within the context of digital entertainment will be considered.
Topics of interest include, but are not limited to:
- Aesthetic measurement and control
- Machine learning for predicting or controlling aesthetic preference
- Aesthetic measures for sound, photos, textures and other content
- User modeling
- Stylistic recognition and classification
- Content-based similarity or recommendation
- Biologically-inspired creativity
- Evolutionary arts and evolutionary algorithms for creative applications
- Interactive evolutionary algorithms
- Creative virtual ecosystems
- Artificial creative agents
- Definition or classification of creativity
- Interactive environments and games
- Virtual worlds
- Reactive worlds and immersive environments
- Procedural content generation
- Game AI
- Intelligent interactive narrative
- Learning and adaptation in games
- Search methods for games
- Player experience measurement and optimization
- Composition, synthesis, generative arts
- Visual art, architecture and design
- Creative writing
- Cinema music composition and sound synthesis
- Generative art
- Synthesis of textures, images, animations
- Non-realistic rendering
- Generation or learning of environmental responses
- Analysis of computational intelligence techniques for games, music and the arts
Penousal Machado leads the Cognitive and Media Systems group at the University of Coimbra. His research interests include Evolutionary Computation, Computational Creativity, and Evolutionary Machine Learning. In addition to the numerous scientific papers in these areas, his works have been presented in venues such as the National Museum of Contemporary Art (Portugal) and the “Talk to me” exhibition of the Museum of Modern Art, NY (MoMA).
Vanessa Volz is a research associate at Queen Mary University London, UK, with focus in computational intelligence in games. She holds B.Sc. degrees in Information Systems and in Computer Science from WWU Münster, Germany. She received an M.Sc. with distinction in Advanced Computing: Machine Learning, Data Mining and High Performance Computing from University of Bristol, UK in 2014 after completing a BigData internship at Brown University, RI, USA. Her current research focus is on employing surrogate-assisted evolutionary algorithms to obtain balance and robustness in systems with interacting human and artificial agents, especially in the context of games.
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 all aspects of evolutionary combinatorial optimization and metaheuristics, including, but not limited to:
- Applications of metaheuristics to combinatorial optimization problems
- 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
Christian Blum currently holds the permanent post of a senior research scientist at the Artificial Intelligence Research Institute (IIIA) of the Spanish National Research Council (CSIC) in Bellaterra, Spain. He obtained the PhD in Applied Sciences from the Université de Bruxelles in 2004. Besides topics in swarm intelligence, his research interests are mainly focused on the hybridization of metaheuristics with other techniques for optimization. He has (co-)authored more than 150 publications in international journals, books, and peer-reviewed conference proceedings. Apart from acting as area editor for the journal Computers & Operations Research (responsible for metaheuristics), he is also associate editor for journals such as Theoretical Computer Science and Natural Computing. Moreover, he is a co-founder of the workshop series on Hybrid Metaheuristics.
Francisco Chicano is an Associate Professor in the Department of Languages and Computing Sciences of the University of Malaga, Spain. He studied Computer Science (2003) and PhD in Computer Science (2007) at University of Malaga, and Physics (2014) in the National Distance Education University. His research interests and publications include the landscapes theory of combinatorial optimization problems and the application of theoretical results to the design of new search algorithms and operators. He has served as Program Chair in the EvoCOP conference, as Track Chair in GECCO 2013 and 2015 and as Guest Editor in Special Issues of Evolutionary Computation (MIT), Journal of Systems and Software, Algorithmica and Journal of Heuristics.
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 emergent topics such as transfer learning and domain adaptation, deep learning, learning with a small number of examples, and learning with unbalanced data and missing data. The tasks range from classification, via clustering, regression, prediction to time series analysis and ML problems.
The global search performed by evolutionary methods frequently provides a valuable complement to the local search of non-evolutionary methods and combinations of the two often show particular promise in practice.
This track aims to encourage information exchange and discussion between researchers with an interest in this growing research area. 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:
- Main EML paradigms and algorithms
- Learning Classifier Systems (LCS) and evolutionary Rule-Based Systems
- Genetic Programming (GP) when applied to machine learning tasks (as opposed to function optimisation)
- Evolutionary ensembles, where evolution generates a set of learners which jointly solve problems
- Evolutionary transfer learning and domain adaptation
- Evolutionary deep learning and evolving deep structures
- Evolving neural networks or neuroevolution (when applied to ML tasks)
- Hyper-parameter tuning of machine learning with evolutionary methods
- Evolutionary learning with a small number of examples, unbalanced data or missing data values
- Other EC (e.g. particle swarm optimisation and differential evolution) based machine learning paradigms and algorithms.
- Theoretical and methodological advances on EML
- Identification and modelling of learning and scalability bounds
- Evolutionary computation techniques for feature extraction, feature selection, and feature construction
- Connections and combinations with machine learning theory (e.g. PAC theory and VC dimension)
- Analysis of the evolved/learned models including visualisation
- Generalisation and overfitting
- Policy search and reinforcement learning when rooted in machine learning theory
- Analysis and robustness in stochastic, noisy, or non-stationary environments
- More effective and efficient algorithms
- Addressing significant problems such as representation, data sampling, scalability, search mechanisms, multi-objective learning, fitness evaluation, niching and encapsulation, initialisation and termination
- Applications of EML
- 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
- Artificial Life
- Economic modelling
- Cyber security
Bing Xue received her PhD degree in 2014 at Victoria University of Wellington, New Zealand. Since May 2015, she has been working as a Lecturer at Victoria University of Wellington. She is with the Evolutionary Computation Research Group at VUW, and her research focuses mainly on evolutionary computation, machine learning and data mining, particularly, evolutionary computation for feature selection, feature construction, dimension reduction, symbolic regression, multi-objective optimisation, bioinformatics and big data. Bing is currently leading the strategic research direction on evolutionary feature selection and construction in Evolutionary Computation Research Group at VUW, and has been organsing special sessions and issues on evolutionary computation for feature selection and construction. She is also the Chair of IEEE CIS Task Force on Evolutionary Computation for Feature Selection and Construction. Bing is a committee member of Evolutionary Computation Technical Committee, and Emergent Technologies Technical Committee, IEEE CIS. She has been serving as a guest editor, associated editor or editorial board member for international journals, and program chair, special session chair, symposium/special session organiser for a number of international conferences, and as reviewer for top international journals and conferences in the field. She is also a Chair of Women@GECCO 2018.
Dr. Jean-Baptiste Mouret is a senior researcher ("Directeur de recherche") at Inria, the French research institute dedicated to computer science and mathematics. He is currently the principal investigator of an ERC grant (ResiBots – Robots with animal-like resilience, 2015-2020). From 2009 to 2015, he was an assistant professor ("maître de conférences") at the Pierre and Marie Curie University (Paris, France). Overall, J.-B. Mouret conducts researches that intertwine evolutionary algorithms, neuro-evolution, and machine learning to make robots more adaptive. His work was recently featured on the cover of Nature (Cully et al., 2015) and it received 3 GECCO best paper awards (2011, GDS track, 2017 & 2018 CS track), the "Distinguished Young Investigator in Artificial Life 2017" award, the French "La Recherche" award (2016), and the IEEE CEC "best student paper" award (2009).
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 ideal solution seldom 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 benchmarking
- Selection mechanisms
- Variation mechanisms
- Parallel and distributed models
- Stopping criteria
- Performance assessment
- 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
Arnaud Liefooghe is an Associate Professor (Maître de Conférences) at the University of Lille (France) since 2010. He is a member of the CRIStAL research center (UMR 9189, Univ Lille, CNRS, EC Lille) and of the Inria Lille - Nord Europe research center. He is also the co-director of the International Associated Laboratory MODŌ between Shinshu University (Japan) and the University of Lille (France). He received his PhD in computer science from the University of Lille (France) in 2009. In 2010, he was a post-doctoral researcher at the University of Coimbra (Portugal). His main research activities deal with the foundations, the design and the analysis of stochastic local search heuristic algorithms, with a particular interest in multiobjective optimization. He co-authored more than fifty scientific papers in international journals, book chapters and international conferences. He was awarded a best paper award at EvoCOP 2011 and at GECCO 2015. He co-organized two summer schools on EC, the ThRaSH 2012 workshop, a special issue on EMO at EJOR, and special sessions or workshops at EURO 2012, MCDM 2013, LION 2013, CEC 2015 and 2017, as well as GECCO 2017. He has been a program committee member of more than twenty international conferences such as CEC, EMO, EvoCOP, GECCO, PPSN, and a regular journal reviewer from more than ten international journals. Recently, he served as a program chair for EvoCOP 2018 and 2019, and as a proceedings chair for GECCO 2018.
Jonathan Fieldsend is an Associate Professor in Computational Intelligence at the University of Exeter. He has a degree in Economics from Durham University, a Masters in Computational Intelligence from the University of Plymouth and a PhD in Computer Science from the University of Exeter. He has held postdoctoral positions as a Research Fellow (working on the interface of Bayesian modelling and optimisation) and as a Business Fellow (focusing on knowledge transfer to industry) prior to his appointment to an academic position at Exeter.
He has published widely on theoretical and applied aspects of evolutionary multi-objective optimisation, and also in the field of machine learning — and has ongoing interests on the interface between these areas. His theoretical work includes algorithm development and analysis, along with data structures required for efficient multi-objective optimisation and Pareto set maintenance. His applied work includes costly and uncertain industrial design problems, air traffic control safety systems, automating biological experiments and robust multi-objective routing.
He has previous been a workshop organiser at GECCO for VizGEC (Visualisation Methods in Genetic and Evolutionary Computation), SAEOpt (Surrogate-Assisted Evolutionary Optimisation) and EAPU (Evolutionary Algorithms for Problems with Uncertainty). He has been active within the evolutionary computation community as a reviewer and program committee member since 2003.
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 like Cross-Entropy (CE) methods, Differential Evolution (DE), continuous versions of Genetic Algorithms (GAs), Estimation-of-Distribution Algorithms (EDAs), Evolution Strategies (ES), Evolutionary Programming (EP), continuous Information Geometric Optimization (IGO), Markov Chain Monte Carlo methods (MCMC), and Particle Swarm Optimization (PSO).
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.
Prof. Lozano graduated in Mathematics (1991) and Computer Science (1992) at the University of the Basque Country (UPV/EHU) (Spain). In 1998 he got his PhD degree from the University of the Basque Country UPV/EHU, where he was awarded with the extraordinary prize for the best thesis in engineering. He got an assistant professor position at the University of the Basque Country (UPV/EHU) in 1993 and became a full professor at the Department of Computer Science and Artificial Intelligence in 2008.
Since 2005 he leads the Intelligent Systems Group (ISG) based in the Computer Science School (UPV/EHU). His research areas are evolutionary computation, machine learning and probabilistic graphical models and its application in the solution of real problems in biomedicine, industry or finance. He has published 4 books, more tan 100 scientific ISI journal articles and about 150 contributions to national and international conferences. These publications have received more than 8600 citations. Prof. Lozano is associate editor of IEEE Trans. on Evolutionary Computation and IEEE Trans. on Neural Network and Learning Systems among other prestigious journals.
Dirk Arnold is a Professor in the Faculty of Computer Science at Dalhousie University. His research interests span evolutionary computation, numerical optimization, and machine learning. He is an Associate Editor of Evolutionary Computation, a Member of the Editorial Board of Computational Optimization and Applications, and was General Chair of GECCO 2014.
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 and theoretical aspects of GAs
- Design of new GA operators including representations, fitness functions, initialization, termination, selection, recombination, and mutation
- Design of new and improved GAs
- Comparisons with other methods (e.g., empirical performance analysis)
- 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 control (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.
Gabriela Ochoa is a Professor in Computing Science at the University of Stirling, Scotland. She received a PhD in Computer Science from the University of Sussex, UK. She worked in industry for five years before joining academia and has held faculty and research positions at the University Simon Bolivar, Venezuela and the University of Nottingham, UK. Her research interests lie in the foundations and application of evolutionary algorithms and heuristic search methods, with emphasis on autonomous search, hyper-heuristics, fitness landscape analysis and visualisation. She is associate editor of the Evolutionary Computation Journal and the IEEE Transactions on Evolutionary Computation; and is a member of the SIGEvo executive board. She has served as an organiser and/or program chair for several international events such as GECCO, PPSN, FOGA, and IEEE CEC and was the Editor-in-Chief for GECCO 2017.
Tian-Li Yu received the B.S. degree from the Dept. of Electrical Engineering of the National Taiwan University, and M.S. and Ph.D. in Computer Science of the University of Illinois at Urbana-Champaign. Currently, he is working as an associate professor in the National Taiwan University. He has been doing research in the field of Evolutional Computation for about 15 years. His main research interests are theoretical aspects concerning linkage learning in genetic algorithms as well as algorithm design/improvement.
GECH - General Evolutionary Computation and Hybrids
General Evolutionary Computation and Hybrids is a new track that recognises that Evolutionary Algorithms are often used as part of a larger system, or together in synergy with other algorithms.
We welcome high quality papers on a range of topics that might not fit solely into any of the other track descriptions.
Areas of interest include the following - but the limit should be your creativity not ours!
- Combining different ways of creating or improving solutions
- such as co-evolution, neuro-evolution, memetic algorithms, and other hybrids.
- Combining EAs with Machine Learning Algorithms that learn a model of the search space
- such as surrogate-assisted optimisation of expensive fitness functions,
- Combining EAs with learning algorithms that attempt to learn how to control or co-ordinate a range of algorithms
- such as parameter tuning, parameter control, and self * approaches such as hyper-heuristics and self-adaptation,
- Novel nature-inspired paradigms
- Algorithms for Dynamic and stochastic environments
- Statistical analysis techniques for EAs
- Evolutionary algorithm toolboxes
Holger H. Hoos is Professor of Machine Learning at Universiteit Leiden (the Netherlands) and Adjunct Professor of Computer Science at the University of British Columbia (Canada), where he also holds an appointment as Faculty Associate at the Peter Wall Institute for Advanced Studies. He is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) and past president of the Canadian Association for Artificial Intelligence / Association pour l'intelligence artificielle au Canada (CAIAC).
Holger's research interests span artificial intelligence, empirical algorithmics, bioinformatics and computer music. He is known for his work on machine learning and optimisation methods for the automated design of high-performance algorithms and for his work on stochastic local search. Based on a broad view of machine learning, he has developed - and vigorously pursues - the paradigm of programming by optimisation (PbO); he is also one of the originators of the concept of automated machine learning (AutoML). Holger has a penchant for work at the boundaries between computing science and other disciplines, and much of his work is inspired by real-world applications.
In 2018, together with Morten Irgens (Oslo Metropolitan University) and Philipp Slusallek (German Research Center for Artificial Intelligence), Holger launched CLAIRE, an initiative by the European AI community that seeks to strengthen European excellence in AI research and innovation (claire-ai.org). CLAIRE promotes excellence across all of AI, for all of Europe, with a human-centred focus and aims to achieve an impact similar to that of CERN. The initiative has attracted major media coverage in many European countries and garnered broad support by more than 1000 AI experts, more than one hundred fellows of various scientific AI associations, many editors of scientific AI journals, national AI societies, top AI institutes and key stakeholders in industry and other organisations (for details, see claire-ai.org).
Yaochu Jin received the B.Sc., M.Sc., and PhD degrees from Zhejiang University, China, in 1988, 1991, and 1996, respectively, and the Dr.-Ing. Degree from Ruhr University Bochum, Germany, in 2001. He is currently a Professor in Computational Intelligence and Head of the Nature Inspired Computing and Engineering (NICE) Group, Department of Computer Science, University of Surrey, UK. He was a Finland Distinguished Professor and Changjiang Distinguished Professor. His research interests include data-driven evolutionary optimization, Bayesian optimization, secure and interpretable machine learning, evolutionary multi-objective learning, evolutionary developmental systems, and neural plasticity.
He is the Editor-in-Chief of the IEEE Transactions on Cognitive and Developmental Systems and the Co-Editor-in-Chief of Complex & Intelligent Systems. He is also an Associate Editor of the IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, and IEEE Transactions on NanoBioscience. He is an Editorial Board Member of Evolutionary Computation. He is an Invited Plenary / Keynote Speaker at over 30 international conferences. He is the recipient of several awards including the 2014 and 2016 IEEE Computational Intelligence Magazine Outstanding Paper Award, and the 2017 IEEE Transactions on Evolutionary Computation Outstanding Paper Award. He is an IEEE Distinguished Lecturer.
Dr Jin is a Fellow of IEEE.
GP - Genetic Programming
Genetic Programming is an evolutionary computation technique that automatically generates solutions/programs to solve a given problem. Various representations have been used in GP, 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 the human to explicitly program the computer. The GP track invites original submissions on all aspects of the evolutionary generation of computer programs or other executable structures for specified tasks.
Advances in genetic programming include but are not limited to:
- Analysis: Information theory, Complexity, Run-time, Visualization, Fitness Landscape
- Synthesis: Programs, Algorithms, Circuits, Systems
- Applications: Classification, Control, Data mining, Regression, Semi-supervised, Policy search, Prediction, Optimisation, Streaming data, Design, Inductive Programming
- Environments: Static, Dynamic, Interactive, Uncertain
- Operators: Replacement, Selection, Variation
- Performance: Surrogate functions, Multi-objective, Coevolutionary, Human Competitive, Parameter Tuning
- Populations: Demes, Diversity, Niches
- Programs: Decomposition, Modularity, Semantics, Simplification, Software Improvement, Debugging
- Programming languages: Imperative, Declarative, Object-oriented, Functional
- Representations: Cartesian, Grammatical, Graphs, Linear, Rules, Trees
- Systems: Autonomous, Complex, Developmental, Gene regulation, Parallel, Self-organizing, Software
Ting Hu is an Assistant Professor at the Department of Computer Science, Memorial University in St. John's, Canada. She is interested in understanding the fundamental mechanisms of evolution, especially the properties of robustness and evolvability of evolutionary algorithms and novel applications of evolutionary algorithms in biomedical research.
Miguel is a Lecturer in Business Analytics, in the School of Business of University College Dublin, Ireland. His research interests revolve around Artificial Intelligence, Machine Learning, Evolutionary Computation, Business Analytics, Genetic Programming, and Real-World Applications. He is a senior member of the UCD's NCRA (Natural Computing Research & Applications) group.
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 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.
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.
The real-world applications track is open to all domains and all industries.
Robin Purshouse is a Reader (Associate Professor) in Decision Modelling and Optimization at the University of Sheffield. He received the MEng degree in Control Systems Engineering in 1999 and a PhD in Control Systems in 2004 for his research on evolutionary multi-objective optimization (both from the University of Sheffield). His research interests are in methods for design optimization, with a focus on decision support for real-world applications. He has successfully applied evolutionary algorithms to many-objective, robust and distributed optimization problems in projects with leading manufacturers of complex engineered products, such as Jaguar Land Rover and Ford Motor Company. Robin also has research interests in the modelling and simulation of complex social systems, with a focus on health behaviours. He presently leads the National Institutes of Health-funded CASCADE project, developing agent-based models that aim to explain alcohol use patterns in the US over the last 40 years.
Thomas is professor of computer science at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University, The Netherlands, where he is head of the Natural Computing and Optimization group.
In addition to his interest in the fundamental working principles of evolutionary algorithms, Thomas has ample experience in real-life applications of optimization and predictive analytics, through working with global companies such as BMW, Daimler, Honda Research Institute, KLM, Tata Steel.
In these projects, the focus is on Industry 4.0 applications of predictive analytics, automatic machine learning, and optimization, in particular for production process optimization applications, predictive maintenance, anomaly detection, and related areas.
Thomas has published two books on evolutionary algorithms: Evolutionary Algorithms in Theory and Practice (1996), and Contemporary Evolution Strategies (2013). He is co-editor of the Handbook of Evolutionary Computation (2000), and the Handbook of Natural Computing (2011). Thomas received the IEEE CIS Evolutionary Computation Pioneer Award in 2015.
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 meta-heuristic ideas, novel search strategies, approaches and findings.
We invite papers that address problems in the software engineering domain through the use of heuristic search techniques. We would also thus like to invite papers from the genetic improvement area where evolutionary computation has been used for the purpose of software improvement.
We particularly encourage papers demonstrating novel search strategies or the application of SBSE techniques to new problems in software engineering. Papers may also address the use of methods and techniques for improving the applicability and efficacy of search-based techniques when applied to software engineering problems. 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 approaches. Moreover poster-only papers presenting frameworks/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
- Automatic Algorithm configuration and Parameter Tuning
- Estimation of Distribution Algorithms
- Evolutionary Computation
- Genetic Programming
- Hybrid and Memetic Algorithms
- 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 Systems
- Enabling Self-Configuring/Self-Healing/Self-Optimising Software Systems
- Improving Software's properties, such as runtime or energy consumption, and other
- Network Design and Monitoring
- Optimising Functional and Non-Functional Software Properties (Genetic Improvement)
- Predictive Modelling for Software Engineering Tasks
- Project Management and Organisation
- Testing including test data generation, regression test optimisation, test suite evolution
- Requirements Engineering
- Software Evolution and Maintenance
- Program Repair
- Refactoring and Transformation
- Software Security
- Software Transplantation
- System and Software Integration
- System and Software Verification
Justyna Petke is a Principal Research Fellow and Proleptic Senior Lecturer (Associate Prof.) at the Centre for Research on Evolution, Search and Testing (CREST) in University College London. Her expertise lies in genetic improvement, which uses evolutionary computation in order to find improved software versions. Her work on the subject was awarded a Silver and a Gold 'Humie' at GECCO 2014 and GECCO 2016. She also organised four Genetic Improvement Workshops and is co-Chairing the SBSE track at GECCO 2019.
Giuliano Antoniol is professor of Software Engineering in the Department of Computer and Software Engineering of the Polytechnique Montréal where he directs the SOCCER laboratory. He worked in private companies, research institutions and universities. In 2005 he was awarded the Canada Research Chair Tier I in Software Change and Evolution. He has served in the program, organization and steering committees of numerous IEEE and ACM sponsored international conferences and workshops. His research interest include software traceability, traceability recovery and maintenance, software evolution, empirical software engineering, search based software engineering, and software testing.
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.
Johannes Lengler is a senior researcher at ETH Zurich, Switzerland.
He received his PhD in mathematics at Saarland University, Germany, before he turned to theoretical computer science and computational neuroscience. The overarching theme of his research is to understand the dynamics of stochastic processes. His main research areas are generative network model for large social and technological networks, computational models for learning and cognitive processes in the brain, and theory of nature-inspired search heuristics. In the latter field, he has worked on runtime analysis, black-box complexity, and theoretical benchmarks for evolutionary and genetic algorithms.
Per Kristian Lehre is a Senior Lecturer at the University of Birmingham, UK.
He received MSc and PhD degrees in Computer Science from the Norwegian University of Science and Technology (NTNU). After finishing his PhD in 2006, he held postdoctorial positions in the School of Computer Science at the University of Birmingham and at the Technical University of Denmark. From 2011, he was a Lecturer in the School of Computer Science at the University of Nottingham, until 2017, when he returned to Birmingham.
Dr Lehre's research interests are in theoretical aspects of nature-inspired search heuristics, in particular, runtime analysis of population-based evolutionary algorithms. His research has won several best paper awards, including at GECCO (2013, 2010, 2009, 2006), ICSTW (2008), and ISAAC (2014). He is editorial board member of Evolutionary Computation, and associate editor of IEEE Transactions on Evolutionary Computation. He was the coordinator of the successful 2M euro EU-funded project SAGE which brought together the theory of evolutionary computation and population genetics.