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.
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The ACO-SI Track welcomes submissions of original and unpublished work in all experimental and theoretical aspects of SI, including (but not limited to) the following areas: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
Griffith University, Australia | webpage
Mardé Helbig is a Senior Lecturer at the School of ICT at Griffith University in Australia. Her research focuses on solving dynamic multi-objective optimization (DMOO) problems using computational intelligence algorithms. She is a sub-committee member of the IEEE CIS Young Professionals and IEEE Women in CI, and a member of the IEEE CIS Emerging Technologies Technical Committee. She has organised special sessions and presented tutorials and keynotes on DMOO at various conferences. She is an executive committee member of the South African Young Academy of Science and has received the 2018/2019 TW Kambule-NSTF: Emerging Researcher award.
University of Pretoria, South Africa | webpage
Christopher Cleghorn received his Masters and PhD degrees in Computer Science from the University of Pretoria, South Africa, in 2013 and 2017 respectively. He is Senior Lecturer in Computer Science at the University of Pretoria. His research interests include swarm intelligence, evolutionary computation, machine learning, and radio astronomy with a strong focus of theoretical research. Dr Cleghorn annually serves as a reviewer for numerous international journals and conferences in domains ranging from swarm intelligence and neural networks to mathematical optimization.
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.
Dennis G. Wilson
ISAE-SUPAERO, University of Toulouse, France | webpage
Dennis G. Wilson is an Assistant Professor of AI and Data Science at ISAE-SUPAERO in Toulouse, France. He obtained his PhD at the Institut de Recherche en Informatique de Toulouse (IRIT) on the evolution of design principles for artificial neural networks. Prior to that, he worked in the Anyscale Learning For All group in CSAIL, MIT, applying evolutionary strategies and developmental models to the problem of wind farm layout optimization. His current research focuses on genetic programming, neural networks, and the evolution of learning.
University of Malta, Malta | webpage
Georgios Yannakakis a Professor and Director of the Institute of Digital Games, University of Malta (UM), the co-founder of modl.ai and an Associate Professor at the Technical University of Crete. He received the PhD degree in Informatics from the University of Edinburgh in 2006. Between 2007 and 2012 he was an Associate Professor at the Center for Computer Games Research at the IT University of Copenhagen. He does research at the crossroads of artificial intelligence, computational creativity, affective computing, advanced game technology, and human-computer interaction. He pursues research concepts such as user experience modelling and procedural content generation for the design of personalized interactive systems for entertainment, education, training and health. Prof. Yannakakis has published over 260 journal and conference papers in the aforementioned fields. His research has been supported by numerous national and European grants (including a Marie Skłodowska-Curie Fellowship) and has appeared in Science Magazine and New Scientist among other venues. He has been involved in a number of journal editorial boards and is currently an Associate Editor of the IEEE Transactions on Games and the IEEE Transactions on Evolutionary Computation, and was an Associate Editor of the IEEE Transactions on Affective Computing between 2009 and 2017. He has been the General Chair of key conferences in the area of game artificial intelligence (IEEE CIG 2010) and games research (FDG 2013, FDG 2020). He is the co-author of the Artificial Intelligence and Games textbook and the co-organiser of the Artificial Intelligence and Games summer school series.
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.
University of Coimbra, Portugal | webpage
Luís Paquete is Associate Professor at the Department of Informatics Engineering, University of Coimbra, Portugal, since 2007. He received his Ph.D. in Computer Science from T.U. Darmstadt, Germany, in 2005 and a M.S. in Systems Engineering and Computer Science from the University of Algarve, Portugal, in 2001. His research interest is mainly focused on exact and heuristic solution methods for multiobjective combinatorial optimization problems. He is in editorial board of Operations Research Perspectives and Area Editor at ACM Transactions on Evolutionary Learning and Optimization.
University of Stirling, UK | webpage
Gabriela Ochoa is a Professor in Computing Science at the University of Stirling, Scotland, where she leads the Data Science and Intelligent Systems (DAIS) research group. She received BSc and MSc degrees in Computer Science from University Simon Bolivar, Venezuela and a PhD from University of Sussex, UK. She worked in industry for 5 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 optimisation methods, with emphasis on autonomous search, hyper-heuristics, fitness landscape analysis, visualisation and applications to logistics, transportation, healthcare, and software engineering. She has published over 110 scholarly papers (H-index 31) and serves various program committees. She was associate editor of the IEEE Transactions on Evolutionary Computation, is currently for the Evolutionary Computation Journal, and is a member of the editorial board for Genetic Programming and Evolvable Machines. She has served as organiser for various Evolutionary Computation events and served as the Editor-in-Chief for the Genetic and Evolutionary Computation Conference (GECCO) 2017. She is a member of the executive boards of the ACM interest group on Evolutionary Computation (SIGEVO), and the leading European Event on bio-inspired computing (EvoSTAR).
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.
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:
- 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 learning
- Evolutionary representation learning, transfer learning and domain adaptation
- 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
- Machine Learning-assisted evolutionary optimisation algorithms.
- Theoretical and methodological advances on EML
- 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:
- 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
Newcastle University, UK | webpage
Jaume Bacardit has receiveda BEng, MEng in Computer Engineering and a
PhD in Computer Science from Ramon Llull University, Spain in 1998, 2000 and 2004, respectively. He is currently Reader in Machine Learning at Newcastle University in the UK. Bacardit’s research interests include the development of machine learning methods for large-scale problems, the design of techniques to extract knowledge and improve the interpretability of machine learning algorithms and the application of these methods to a broad range of problems, mostly in biomedical domains.
Université Laval, Canada | webpage
Christian Gagné is a professor at the Electrical Engineering and Computer Engineering Department of Université Laval since 2008. He is the director of the Institute Intelligence and Data (IID) of Université Laval. He holds a Canada-CIFAR Artificial Intelligence Chair and is an associate member to Mila. He is also a member of the Computer Vision and Systems Laboratory (CVSL), a component of the Robotics, Vision and Machine Intelligence Research Centre (CeRVIM), and the Big Data Research Centre (BDRC) of Université Laval. He is also participating to the REPARTI and UNIQUE strategic clusters of the FRQNT, the VITAM FRQS center and the International Observatory on the Societal Impacts of AI (OBVIA).
He completed a PhD in Electrical Engineering (Université Laval) in 2005 and then had a postdoctoral stay jointly at INRIA Saclay (France) and the University of Lausanne (Switzerland) in 2005-2006. He worked as research associate in the industry between 2006 and 2008. He is a member of executive board the ACM Special Interest Group on Evolutionary Computation (SIGEVO) since 2017.
His research interests are on the development of methods for machine learning and stochastic optimization. In particular, he is interested by deep neural networks, representation learning and transfer, meta-learning and multitask learning. He is also interested by optimization approaches based on probabilistic models and evolutionary algorithms for black-box optimization and automatic programming, among others. A significant share of his research work is on the practical use of these techniques in domains such as computer vision, microscopy, health, energy and transportation.
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
Otto von Guericke University Magdeburg, Germany | webpage
Sanaz Mostaghim is a full professor of computer science and head of SwarmLab at the Otto von Guericke University Magdeburg, Germany. She holds a PhD degree (2004) in electrical engineering from the University of Paderborn, Germany. Sanaz has worked as a postdoctoral fellow at ETH Zurich in Switzerland (2004-2006) and as a lecturer at Karlsruhe Institute of Technology (KIT), Germany (2006-2013), where she received her habilitation degree in applied computer science in 2012. Her research interests are in the area of evolutionary multi-objective optimization and decision-making, swarm intelligence, and their applications in robotics and science. She is the deputy chair of the executive board of Informatics Germany, member of the advisory board on digitalization at the ministry of digitalization and economy in state Saxony-Anhalt and the head of the RoboCup team of the University of Magdeburg. Sanaz is an active member of IEEE Computational Intelligence Society, is a member of the administration committee and will serve as the vice-president for member activities from 2021. She is associate editor of IEEE Transactions on Artificial Intelligence, IEEE Transactions on Evolutionary Computation and member of the editorial boards of several international journals.
Université de Lille, France | webpage
Laetitia Jourdan is a full Professor in Computer Science at University of Lille /CRIStAL. Her areas of research are modeling datamining tasks as combinatorial optimization problems, solving methods based on metaheuristics, incorporate learning in metaheuristics and multiobjective optimization. She holds a PhD in combinatorial optimization from the University of Lille 1 (France). From 2004 to 2005, she was a research associate at University of Exeter (UK). She is (co)author of more than 100 papers published in international journals, book chapters, and conference proceedings.She organized several international conferences (LION 2015, MIC 2015, etc) and is reviewer editor for Frontier in Big Data. She has served as member of the programme committee of the major conferences of her research domain (GECCO, CEC, PPSN …).
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.
CINVESTAV-IPN, Mexico | webpage
Oliver Schütze received a PhD in Mathematics from the University of
Paderborn, Germany. He is Professor at the Cinvestav-IPN in Mexico City (Mexico). His research interests focus on numerical and evolutionary optimization. He has co-authored more than 140 publications including 1 monograph and 10 edited books. He has won several international awards including the IEEE Computational Intelligence Society (CIS) Outstanding Paper Awards for the best papers of the IEEE Transactions on Evolutionary Computation Journal in the years 2010 and 2012 (bestowed in 2013 and 2015, respectively). He is Editor-in-Chief of the journal Mathematical and Computational Applications.
Czech Technical University, Czechia | webpage
Petr Pošík received his Diploma degree in Technical Cybernetics in 2001 and Ph.D. in Artificial Intelligence and Biocybernetics in 2007, both from the Czech Technical University in Prague, Czech Republic. From 2001 to 2004 he also worked as statistician, analyst and lecturer for StatSoft, Czech Republic. Since 2005 he works as a researcher in the Intelligent Data Analysis Lab, Department of Cybernetics at the Czech Technical University. Being on the boundary of optimization, statistics and machine learning, his research interests are aimed at improving the characteristics of evolutionary algorithms with techniques of statistical machine learning. He also serves as a reviewer for several journals and conferences in the evolutionary-computation field.
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.
CIMAT, Spain | webpage
Carlos Segura received the M.S. degree in computer science from the Universidad de La Laguna, in 2009 and the Ph.D. degree in computer science from the Universidad de La Laguna, in 2012. He has authored and co-authored over 75 technical papers and book chapters, including more than 25 journal papers. His publications currently report over 850 citations and his h-index is 18. Currently, he serves in the editorial board of several international conferences. His main research interests are: design of evolutionary algorithms, diversity management and problem solving paradigms. In the field of design of evolutionary algorithms, he has been involved in the design of optimizers that currently hold the best-known solutions for several optimization problems, such as the Frequency Assignment Problem and the Job-Shop Scheduling Problem. Additionally, he has participated in several international optimization competitions, including the Extended version of Google Hash Code 2020, a competition with more than 100,000 participants, where his team got the winning position.
Dr. Segura is a Member of the IEEE and a member of the ACM. He is currently an Associate Researcher of the Computer Science area at the Center for Research in Mathematics (CIMAT).
University of São Paulo, Brazil | webpage
Renato Tinós is Associate Professor at the Department of Computing and Mathematics of University of São Paulo (USP) at Ribeirão Preto, Brazil. He graduated in Electrical Engineering from State University of São Paulo (UNESP), Brazil, in 1994, and received the M.Sc. and Ph.D. in Electrical Engineering from USP at São Carlos, Brazil, in 1999 and 2003, respectively. He is member of the editorial board of the Journal of the Brazilian Computer Society (Springer). He has published more than 100 papers on topics such as Evolutionary Algorithms, Robotics, and Artificial Neural Networks. His main research interests are on efficient recombination operators for optimization and in application of Evolutionary Algorithms and Artificial Neural Networks in Medicine.
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
Universidad de Málaga, Spain | webpage
Carlos Cotta received his Ph.D. in Computer Science from the University of Málaga, Spain, in 1998. He is currently a Professor at the Computer Science Department from the University of Málaga. His research interests are focused on the confluence of complex systems and evolutionary and memetic computing, with applications on combinatorial optimization in general and bioinformatics and videogames in particular.
He has co-edited books on memetic algorithms and combinatorial optimization, and has published more than 200 papers on these topics. He has been involved in the scientific organization of different events centered on bio-inspired algorithms, evolutionary combinatorial optimization, and complex systems.
Dalhousie University, Canada | webpage
Malcolm Heywood is a Professor of Computer Science at Dalhousie University, Canada. He has a particular interest in scaling up the tasks that evolutionary computation can potentially be applied to. His current research is attempting to coevolve behaviours capable of demonstrating general game AI and multi-task learning under video game environments. Dr. Heywood is a member of the editorial board for Genetic Programming and Evolvable Machines (Springer). He was a track co-chair for the GECCO GP track in 2014 and a co-chair for European Conference on Genetic Programming in 2015 and 2016. He received the Humies Silver medial with Stephen Kelly in 2018 for evolving human (and deep learning) competitive solutions to the Arcade Learning Environment at a fraction of the computational cost.
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, 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
Victoria University of Wellington, New Zealand | webpage
Mengjie Zhang is a Fellow of Royal Society of New Zealand, a Fellow of IEEE, IEEE Distinguished Lecturer, Professor of Computer Science at Victoria University of Wellington where he heads the interdisciplinary Evolutionary Computation Research Group. He is a member of the University Academic Board, a member of the University Postgraduate Scholarships Committee, Associate Dean (Research and Innovation) in the Faculty of Engineering, and Chair of the Research Committees for the Faculty of Engineering and the School of Engineering and Computer Science.
His research is mainly focused on artificial intelligence (AI), machine learning and big data, particularly in evolutionary computation and learning (using genetic programming, particle swarm optimisation and learning classifier systems), feature selection/construction and big dimensionality reduction, computer vision and image processing, job shop scheduling and resource allocation, multi-objective optimisation, classification with unbalanced data and missing data, and evolutionary deep learning and transfer learning. Prof Zhang has published over 500 research papers in refereed international journals and conferences in these areas. He has been serving as an associated editor or editorial board member for over ten international journals including IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, IEEE Transactions on Emergent Topics in Computational Intelligence, ACM Transactions on Evolutionary Learning and Optimisation, the Evolutionary Computation Journal (MIT Press), Genetic Programming and Evolvable Machines (Springer), Applied Soft Computing, and Natural Computing, and as a reviewer of over 30 international journals. He has been involving major AI and EC conferences such as GECCO, IEEE CEC, EvoStar, IJCAI, AAAI, PRICAI, PAKDD, AusAI, IEEE SSCI and SEAL as a Chair. He has also been serving as a steering committee member and a program committee member for over 100 international conferences. Since 2007, he has been listed as one of the top ten (currently No. 4) world genetic programming researchers by the GP bibliography (http://www.cs.bham.ac.uk/~wbl/biblio/gp-html/index.html www.cs.bham.ac.uk).
Prof Zhang is the (immediate) past Chair of the IEEE CIS Intelligent Systems Applications, the IEEE CIS Emergent Technologies Technical Committee and the IEEE CIS Evolutionary Computation Technical Committee, a vice-chair of the IEEE CIS Task Force on Evolutionary Deep Learning and Applications, a vice-chair of the IEEE CIS Task Force on Evolutionary Computer Vision and Image Processing, and the founding chair of the IEEE Computational Intelligence Chapter in New Zealand.
Instituto Tecnológico de Tijuana | webpage
Dr. Leonardo Trujillo is a Professor at the Tecnológico Nacional de México/Instituto Tecnológico de Tijuana (ITT), working at the Department of Electrical and Electronic Engineering, and the Engineering Sciences Graduate Program. Dr. Trujillo received an Electronic Engineering degree and a Masters in Computer Science from ITT, as well as a doctorate in Computer Science from CICESE research center in Mexico. He is involved in interdisciplinary research in the fields of evolutionary computation, computer vision, machine learning and pattern recognition. His research focuses on Genetic Programming (GP) and developing new learning and search strategies based on this paradigm. Dr. Trujillo has been the PI of several national and international research grants, receiving several distinctions from the mexican science council (CONACYT). His work has been published in over 60 journal papers, 60 conference papers, 18 book chapters, and he has edited 4 books on EC and GP. He is on the Editorial Board of the journals GPEM (Springer) and MCA (MDPI), regularly serves as a reviewer for highly respected journals in AI, EC and ML, is series co-chair of the NEO Workshop series, and has organized, been track chair or served as PC member of various prestigious conferences, including GECCO, EuroGP, PPSN, CEC, GPTP, CVPR and ECCV.
Neuroevolution is a machine learning approach that applies evolutionary computation (EC) for 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, and differential evolution
- 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
- 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
- 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
The University of Texas at Austin and Cognizant Technology Solutions, USA | webpage
Risto Miikkulainen is a Professor of Computer Science at the University of Texas at Austin and a AVP of Evolutionary AI at Cognizant Technology Solutions. He received an M.S. in Engineering from the Helsinki University of Technology, Finland, in 1986, and a Ph.D. in Computer Science from UCLA in 1990. His research focuses on methods and applications of neuroevolution, as well as neural network models of natural language processing and self-organization of the visual cortex; he is an author of over 450 articles in these research areas. He is an IEEE Fellow, recipient of the 2020 IEEE CIS EC Pioneer Award, Gabor Award of the INNS, and Outstanding Paper of the Decade Award of the ISAL.
Victoria University of Wellington, New Zealand | webpage
Bing Xue received her PhD degree in 2014 at Victoria University of Wellington (VUW), New Zealand. She is currently an Associate Professor and Program Director of Science in School of Engineering and Computer Science at VUW. She has over 200 papers published in fully refereed international journals and conferences and her research focuses mainly on evolutionary computation, machine learning, classification, symbolic regression, feature selection, evolving deep neural networks, image analysis, transfer learning, multi-objective machine learning. Dr Xue is currently the Chair of IEEE Computational Intelligence Society (CIS) Data Mining and Big Data Analytics Technical Committee, and Vice-Chair of IEEE Task Force on Evolutionary Feature Selection and Construction, Vice-Chair of IEEE CIS Task Force on Transfer Learning & Transfer Optimization, and of IEEE CIS Task Force on Evolutionary Deep Learning and Applications. She is also served as associate editor of several international journals, such as IEEE Computational Intelligence Magazine and IEEE Transactions of Evolutionary Computation.
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.
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 postdoctoral researcher at the School of Computer Science, The University of Adelaide in Australia within the Premier's Research and Industry Fund, Research Consortium. She presented invited talks at UCL London, Goldsmiths, University of London, the University of Nottingham, the University of Sheffield, Hasso Plattner Institut University Potsdam, Sorbonne University, University of Melbourne, University of Sydney. Aneta is a co-designer and co-lecturer for the EdX Big Data Fundamentals course in the Big Data MicroMasters® program and Online Pearson Master of Data Science, Working with Big Data. She received an ACM Women scholarship, sponsored by Google, Microsoft, and Oracle, a Hans-Juergen and Marianna Ohff Research Grant in 2018, and the Best Paper Nomination at GECCO 2019 in the track “Genetic Algorithms”.
Her main research interest focuses on bio-inspired computation, particularly dynamic and stochastic optimisation, evolutionary diversity optimisation, submodular functions, and optimisation under uncertainty in practice. Moreover, her work contributes to understanding the fundamental link between bio-inspired computation, machine learning, and computational creativity. She investigates evolutionary image transition and animation in the area of Artificial Intelligence and examines how to develop designs and applications of artificial intelligent methods based on complex agent-based models. Aneta has given tutorials on Evolutionary Computation for Digital Art at GECCO 2018-20 (https://www.researchgate.net/publication/342762835_Evolutionary_Computation_for_Digital_Art), and tutorial on Evolutionary Diversity Optimisation at PPSN 2020.
The University of Manchester, UK | webpage
Richard is Senior Lecturer in Data Science at The University of Manchester, and the Business Engagement Lead of Alliance Manchester Business School. Richard has a background in Business Engineering (Diplom, Karlsruhe Institute of Technology, Germany + Royal Melbourne Institute of Technology, Australia), Computer Science (PhD, The University of Manchester, UK), and Biochemical Engineering (Research Associate, University College London, UK).
Richard's research interests are in the field of data and decision science and in particular in the development and application of optimization, learning and analytics techniques to real-world problems arising in areas such as management, engineering, healthcare, sports, music, and forensics. Richard is known for his work on non-standard expensive optimization problems comprising, for example, heterogeneous objectives, ephemeral resource constraints, changing variables, and lethal optimization environments. Much of his research has been funded by grants from various UK funding bodies (e.g. Innovate UK, EPSRC, ESRC, ISCF) and industrial partners.
Richard is a Co-Founder of the IEEE CIS Task Force on Optimization Methods in Bioinformatics and Bioengineering, a Member of the IEEE CIS Bioinformatics and Bioengineering Technical Committee, regularly contributing to conference/workshop organization and editorial duties of well-known journals in the field of Optimization and Machine Learning.
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
National Institute of Informatics, Japan | webpage
Fuyuki Ishikawa is Associate Professor at Information Systems Architecture Science Research Division, and also Deputy Director at GRACE Center, in National Institute of Informatics, Japan. His research interests are in software engineering for dependability, including optimization-driven design, analysis, and testing techniques for service-based systems, clouds, automotive systems, and AI systems.
University of Cádiz, Spain | webpage
Inmaculada Medina-Bulo is associate professor in the Department of Computer Science and Engineering of the University of Cádiz (Spain). She pursues an international projection with strong links to other groups in Spain, Germany, and UK. She publishes in top international venues and contribute with reviewing and conference organization. She has led several PhD Thesis, projects and excellence networks too, developed software tools, participated in specialized consulting and data analysis contracts, and her current research interests focus on software testing, search based software engineering, SOA 2.0, CEP, big data, IoT, and decision making.
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.
University of Adelaide, Australia | webpage
Frank Neumann received his diploma and Ph.D. from the Christian-Albrechts-University of Kiel in 2002 and 2006, respectively. He is a professor and leader of the Optimisation and Logistics Group at the School of Computer Science, The University of Adelaide, Australia. Frank has been the general chair of the ACM GECCO 2016. With Kenneth De Jong he organised ACM FOGA 2013 in Adelaide and together with Carsten Witt he has written the textbook "Bioinspired Computation in Combinatorial Optimization - Algorithms and Their Computational Complexity" published by Springer. He is an Associate Editor of the journals "Evolutionary Computation" (MIT Press) and ACM Transactions on Evolutionary Learning and Optimization. In his work, he considers algorithmic approaches in particular for combinatorial and multi-objective optimization problems and focuses on theoretical aspects of evolutionary computation as well as high impact applications in the areas of renewable energy, logistics, and mining.
Andrew M. Sutton
University of Minnesota Duluth, USA | webpage
Andrew M. Sutton is an Assistant Professor in the Department of Computer Science at the University of Minnesota Duluth. He has held postdoctoral research fellowships at the University of Adelaide, Australia, Friedrich-Schiller-Universität Jena, and the Hasso Plattner Institute in Germany. His research interests are in the mathematical analysis of randomized search heuristics for combinatorial optimization and studying models of natural processes using an algorithmic approach.