Loading...
 

Tracks

TitleTrack Chairs
ACO-SI - Ant Colony Optimization and Swarm Intelligence
  • Christopher Cleghorn
  • Chao-li Sun
CS - Complex Systems (Artificial Life/Artificial Immune Systems/Generative and Developmental Systems/Evolutionary Robotics/Evolvable Hardware)
  • Georgios Yannakakis
  • Nicolas Bredeche
ECOM - Evolutionary Combinatorial Optimization and Metaheuristics
  • Gabriela Ochoa
  • Bilel Derbel
EML - Evolutionary Machine Learning
  • Gisele L. Pappa
  • Sebastian Risi
EMO - Evolutionary Multiobjective Optimization
  • Laetitia Jourdan
  • Hiroyuki Sato
ENUM - Evolutionary Numerical Optimization
  • Petr Pošík
  • Ofer Shir
GA - Genetic Algorithms
  • Renato Tinós
  • John Woodward
GECH - General Evolutionary Computation and Hybrids
  • Malcolm Heywood
  • Elizabeth Wanner
GP - Genetic Programming
  • Leonardo Trujillo
  • Domagoj Jakobovic
NE - Neuroevolution
  • Risto Miikkulainen
  • Bing Xue
RWA - Real World Applications
  • Aneta Neumann
  • Richard Allmendinger
SBSE - Search-Based Software Engineering
  • Inmaculada Medina-Bulo
  • Slim Bechikh
THEORY - Theory
  • Andrew M. Sutton
  • Pietro Simone Oliveto

ACO-SI - Ant Colony Optimization and Swarm Intelligence

Description

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

Scope

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

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

Track Chairs

Christopher Cleghorn

University of Witwatersrand, 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 an Associate Professor in the School of Computer Science and Applied Mathematics, at the University of the Witwatersrand. His research interests include swarm intelligence, evolutionary computation, machine learning, and radio-astronomy with a strong focus of theoretical research. Prof Cleghorn annually serves as a reviewer for numerous international journals and conferences in domains ranging from swarm intelligence and neural networks to mathematical optimization.

Chao-li Sun

Taiyuan University of Science and Technology | webpage


CS - Complex Systems (Artificial Life/Artificial Immune Systems/Generative and Developmental Systems/Evolutionary Robotics/Evolvable Hardware)

Description

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

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

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

Track Chairs

Georgios Yannakakis

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.

Nicolas Bredeche

Sorbonne Université | webpage


ECOM - Evolutionary Combinatorial Optimization and Metaheuristics

Description

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

Scope

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

  • Representation techniques
  • Neighborhoods and efficient algorithms for searching them
  • Variation operators for stochastic search methods
  • Search space and landscape analysis
  • Comparisons between different techniques (including exact methods)
  • Constraint-handling techniques
  • 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.

Track Chairs

Gabriela Ochoa

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).

Bilel Derbel

University of Lille, France | webpage

Bilel Derbel is an associate Professor, with a research habilitation/accreditation, at the Department of Computer Science at the University of Lille, France. He is deputy team leader of the BONUS "Big Optimization aNd Ultra-Scale Computing" research group at Inria Lille Nord Europe and CRIStAL, CNRS. He is a co-founder member of the International Associated Lab (LIA-MODO) between Shinshu Univ., Japan, and Univ. Lille, France, on "Massive optimization and Computational Intelligence". His research topics are focused on the design and the analysis of combinatorial optimization algorithms and high-performance computing. His current interests are on the design of high-level evolutionary algorithms for single­ and multi­ objective optimization.


EML - Evolutionary Machine Learning

Description

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.

Scope

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

Track Chairs

Gisele L. Pappa

UFMG, Brazil | webpage

Gisele Pappa is an Associate Professor in the Computer Science Department at UFMG, Brazil. Her main research interests are the intersection of the areas of machine learning and evolutionary computation, with a special interest in genetic programming and its applications in classification and regression tasks. She has also been actively researching the use of EAs for automated machine learning, focusing on applications for health data and also fraud detection.

Sebastian Risi

IT University of Copenhagen | webpage


EMO - Evolutionary Multiobjective Optimization

Description

In many real-world applications, several objective functions have to be optimized simultaneously, leading to a multiobjective optimization problem (MOP) for which an 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.

Scope

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

  • Handling of continuous, combinatorial or mixed-integer problems
  • Test problems and benchmarking
  • Selection mechanisms
  • Variation mechanisms
  • Hybridization
  • 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
  • Visualization
  • Preference articulation
  • Interactive optimization
  • Many-objective optimization
  • Large-scale optimization
  • Expensive function evaluations
  • Constraint handling
  • Uncertainty handling
  • Real-world applications, where the results presented extend beyond the solving of the applied problem, bringing new and broader EMO insights

Track Chairs

Laetitia Jourdan

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 …).

Hiroyuki Sato

The University of Electro-Communications | webpage

Hiroyuki Sato received B.E. and M.E. degrees from Shinshu University, Japan, in 2003 and 2005, respectively. In 2009, he received Ph. D. degree from Shinshu University. He has worked at The University of Electro-Communications since 2009. He is currently an associate professor. He received best paper awards on the EMO track in GECCO 2011 and 2014, Transaction of the Japanese Society for Evolutionary Computation in 2012, 2015, and 2020. His research interests include evolutionary multi- and many-objective optimization, and its applications. He is a member of IEEE, ACM/SIGEVO.


ENUM - Evolutionary Numerical Optimization

Description

The ENUM track (Evolutionary NUMerical optimization) is concerned with randomized search algorithms and continuous search spaces. The scope of the ENUM track includes, but is not limited to, stochastic methods 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).

Scope

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

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

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

Track Chairs

Petr Pošík

Czech Technical University, Czech Republic | webpage

Petr Posik works as a lecturer at the Czech Technical University in Prague, where he also recieved his Ph.D. in Artificial Intelligence and Biocybernetics in 2007. From 2001 to 2004 he worked as a statistician, analyst and lecturer for StatSoft, Czech Republic. Since 2005 he works at the Department of Cybernetics, 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 serves as a reviewer for several journals and conferences in the evolutionary-computation field. Petr served as the student chair at GECCO 2014, as tutorials chair at GECCO 2017, and as the local chair at GECCO 2019.

Ofer Shir

Tel-Hai College and Migal Institute, Israel | webpage

Ofer Shir is an Associate Professor of Computer Science. He currently serves as the Head of the Computer Science Department in Tel-Hai College, and as a Principal Investigator at Migal-Galilee Research Institute – both located in the Upper Galilee, Israel. Ofer Shir holds a BSc in Physics and Computer Science from the Hebrew University of Jerusalem, Israel (conferred 2003), and both MSc and PhD in Computer Science from Leiden University, The Netherlands (conferred 2004, 2008; PhD advisers: Thomas Bäck and Marc Vrakking). Upon his graduation, he completed a two-years term as a Postdoctoral Research Associate at Princeton University, USA (2008-2010), hosted by Prof. Herschel Rabitz in the Department of Chemistry – where he specialized in computational aspects of experimental quantum systems. He then joined IBM-Research as a Research Staff Member (2010-2013), which constituted his second postdoctoral term, and where he gained real-world experience in convex and combinatorial optimization as well as in decision analytics. His current topics of interest include Statistical Learning in Theory and in Practice, Experimental Optimization, Theory of Randomized Search Heuristics, Scientific Informatics, Natural Computing, Computational Intelligence in Physical Sciences, Quantum Control and Quantum Machine Learning.


GA - Genetic Algorithms

Description

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

  • Practical, methodological and foundational aspects of GAs
  • Design of new GA operators including representations, fitness functions, initialization, termination, parent selection, replacement strategies, recombination, and mutation
  • Design of new and improved GAs
  • Fitness landscape analysis
  • Comparisons with other methods (e.g., empirical performance analysis)
  • Design of 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.

Track Chairs

Renato Tinós

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.

 

John Woodward

Queen Mary University of London, UK | webpage

John R. Woodward is a lecturer at the Queen Mary University of London. Formerly he was a lecturer at the University of Stirling, within the CHORDS group (http://chords.cs.stir.ac.uk/) and was employed on the DAASE project (http://daase.cs.ucl.ac.uk/). Before that he was a lecturer for four years at the University of Nottingham. He holds a BSc in Theoretical Physics, an MSc in Cognitive Science and a PhD in Computer Science, all from the University of Birmingham. His research interests include Automated Software Engineering, particularly Search Based Software Engineering, Artificial Intelligence/Machine Learning and in particular Genetic Programming. He has over 50 publications in Computer Science, Operations Research and Engineering which include both theoretical and empirical contributions, and given over 50 talks at International Conferences and as an invited speaker at Universities. He has worked in industrial, military, educational and academic settings, and been employed by EDS, CERN and RAF and three UK Universities.


GECH - General Evolutionary Computation and Hybrids

Description

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.

Scope

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

Track Chairs

Malcolm Heywood

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.

Elizabeth Wanner

Centro Federal de Educação Tecnológica de Minas Gerais | webpage

Elizabeth Wanner is an Associate Professor at Centro Federal de Educação Tecnológica de Minas Gerais, Brazil


GP - Genetic Programming

Description

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.

Scope

Advances in genetic programming include but are not limited to:

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

Track Chairs

Leonardo Trujillo

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.

Domagoj Jakobovic

University of Zagreb, Faculty of electrical engineering and computing | webpage

Domagoj Jakobovic received his PhD degree in 2005 at the Faculty of Electrical Engineering and Computing, University of Zagreb, on the subject of generating scheduling heuristics with genetic programming. He is currently full professor at the Department of Electronics, Microlelectronics, Computer and Intelligent Systems. His research interests include evolutionary algorithms, optimization methods and parallel algorithms. Most notable contributions are in the area of machine supported scheduling, optimization problems in cryptography, parallelization and improvement of evolutionary algorithms. He has published more than 100 papers, lead several research projects and served as a reviewer for many international journals and conferences. He has supervised seven doctoral theses and more than 170 bachelor and master theses.


NE - Neuroevolution

Description

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

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

Scope

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

  • Neuroevolution algorithms involving:
    • Any EC method, e.g. genetic algorithms, evolutionary strategy, and genetic programming, particle swarm optimisation, 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 search
    • Optimisation of network hyperparameters, activation and loss functions, learning dynamics, data augmentation, and initialisation
    • Novel candidate representations
    • Novel search mechanisms
    • Novel fitness functions
    • Surrogate assisted Neuroevolution
    • Methods for improving efficiency
    • Methods for improving regularisation
    • Multi-objective Neuroevolution
    • Neuroevolution for reinforcement learning, supervised learning, unsupervised learning
    • Neuroevolution for transfer learning, one-short learning, few-short learning, multitask learning
    • Parallelised and distributed realisations of Neuroevolution
    • Combinations of Neuroevolution and other neural learning algorithms
    • Interpretable/explainable model learning
  • Applications of Neuroevolution:
    • Computer vision, image processing and pattern recognition
    • Text mining, natural language processing
    • Speech recognition
    • Machine translation
    • Medical and biological problems
    • Evolutionary robotics
    • Artificial life
    • Time series analysis
    • Cyber security
    • Scheduling and combinatorial optimization
    • Healthcare
    • Finance, fraud detection and business
    • Social media data analysis
    • Game playing
    • Visualisation

Track Chairs

Risto Miikkulainen

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 Associate VP of Evolutionary AI at Cognizant. He received an M.S. in Engineering from Helsinki University of Technology (now Aalto University) in 1986, and a Ph.D. in Computer Science from UCLA in 1990. His current research focuses on methods and applications of neuroevolution, as well as neural network models of natural language processing and vision; he is an author of over 450 articles in these research areas. At Cognizant, and previously as CTO of Sentient Technologies, he is scaling up these approaches to real-world problems. Risto is an IEEE Fellow, recipient of the IEEE CIS EC Pioneer Award, INNS Gabor Award, ISAL Outstanding Paper of the Decade Award, as well as 10 Best-Paper Awards at GECCO.

Bing Xue

Victoria University of Wellington, New Zealand | webpage

Bing Xue is currently a Professor of Artificial Intelligence and Program Director of Science in the 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, data mining, 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 CIS Task Force on Transfer Learning & Transfer Optimization, and Vice-Chair of IEEE Task Force on Evolutionary Feature Selection and Construction and of IEEE CIS Task Force on Evolutionary Deep Learning and Applications. She was the Chair of IEEE Computational Intelligence Society (CIS) Data Mining and Big Data Analytics Technical Committee. Prof Xue is the organizer of the special session on Evolutionary Feature Selection and Construction in IEEE Congress on Evolutionary Computation (CEC) 2015, 2016, 2017, 2018, 2019, 2020, and 2021. Prof Xue has been a chair for a number of international conferences including the Chair of Women@GECCO 2018, a co-Chair of the Evolutionary Machine Learning Track for GECCO 2019 and 2020, a co-Chair of the first Neuroevolution Track for GECCO 2021. She is the Lead Chair of IEEE Symposium on Computational Intelligence in Feature Analysis, Selection, and Learning in Image and Pattern Recognition (FASLIP) at SSCI 2016, 2017, 2018, 2019 and 2020, a Program Co-Chair of the 7th International Conference on Soft Computing and Pattern Recognition (SoCPaR2015), a Program Chair of the 31st Australasian Joint Conference on Artificial Intelligence (AI 2018), Finance Chair of 2019 IEEE Congress on Evolutionary Computation, a Workshop Chair of 2021 IEEE International Conference on Data Mining (ICDM), a Conference Activities Chair of 2021 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2021), a General Co-Chair of the 35th International Conference on Image and Vision Computing New Zealand (IVCNZ 2020), a Tutorial Co-Chair of 2022 IEEE World Congress on Computational Intelligence (IEEE WCCI 2022), and a Publication Co-Chair of Proceedings of the 25th European Conference on Genetic Programming (EuroGP 2022). She is an Editor of the IEEE Computational Intelligence Society Newsletter. She is an Associate Editor or Member of the Editorial Board for over ten international journals, including IEEE Transactions on Evolutionary Computation, IEEE Computational Intelligence Magazine, IEEE Transactions on Artificial Intelligence, IEEE Transactions on Emerging Topics in Computational Intelligence, and ACM Transactions on Evolutionary Learning and Optimization.


RWA - Real World Applications

Description

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

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


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

Scope

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

Track Chairs

Aneta Neumann

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, Australia. She has recently participated in artistic exhibition SALA 2016-17 and 2020 in Adelaide. She presented invited talks at UCL London, Goldsmiths, University of London, University of Nottingham, University of Sheffield, Hasso Plattner Institut University of Potsdam, Sorbonne University, University of Melbourne, University of Sydney in 2016-19. She received the Lorentz Center Grant 2020, Optimization Meets Machine Learning, Leiden, The Netherlands, the ACM Women scholarship, sponsored by Google, Microsoft, and Oracle, the Hans-Juergen and Marianna Ohff Research Grant in 2018, and the Best Paper Nomination at GECCO 2019 in the track “Genetic Algorithms”. She is a co-designer and co-lecturer for the EdX Big Data Fundamentals course in the Big Data MicroMasters® program and Online Pearson Education, Working with Big Data. Her main research interest focuses on bio-inspired computation, particularly dynamic and stochastic optimization, evolutionary diversity optimization and digital art. She serves as the co-chair of the Real-World Applications track at GECCO 2021and GECCO 2022.

Richard Allmendinger

The University of Manchester, UK | webpage

Richard is Senior Lecturer in Data Science and the Business Engagement Lead of Alliance Manchester Business School, The University of Manchester, and Fellow of The Alan Turing Institute, the UK's national institute for data science and artificial intelligence. 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 Member of the Editorial Board of several international journals, Vice-Chair of the IEEE CIS Bioinformatics and Bioengineering Technical Committee, Co-Founder of the IEEE CIS Task Force on Optimization Methods in Bioinformatics and Bioengineering, and contributes regularly to conference organisation and special issues as guest editors.


SBSE - Search-Based Software Engineering

Description

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

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

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

Scope

As an indication of the wide scope of the field, search techniques include, but are not limited to:

  • Ant Colony Optimisation
  • Automatically configured and Tuned Algorithms
  • Estimation of Distribution Algorithms
  • Evolutionary Computation
  • Genetic Programming
  • Hybrid Algorithms and Neuroevolution
  • Hyper-heuristics
  • Iterated Local Search
  • Particle Swarm Optimisation
  • Simulated Annealing
  • Tabu Search
  • Variable Neighbourhood Search

The software engineering tasks to which they are applied are drawn from throughout the engineering lifecycle and include, but are not limited to:

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

Track Chairs

Inmaculada Medina-Bulo

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.

Slim Bechikh

University of Carthage, FSEG-Nabeul, Tunisia | webpage

Slim Bechikh is an Associate Professor, having a Research Habilitation, at the department of computer science of the University of Carthage, FSEG-Nabeul, Tunisia. He received his PhD in Computer Science with Business in 2013 from the University of Tunis, ISG-Tunis, Tunisia. He is also a Research Director within the SMART laboratory at ISG-Tunis. He published over 80 papers in peer-reviewed international journals and conferences. His current research interests include evolutionary optimization, SBSE, machine learning, and business analytics. Dr. Bechikh was a recipient of the Best Paper Award of the ACM SAC-2010 in Switzerland. He supervised the Tunisian best national Doctoral thesis in ICT for the year 2019, which earned a presidential prize in scientific research and technology. He was promoted in August 2021 to the grade of IEEE Senior Member. He is Associate Editor for IEEE Transactions on Evolutionary Computation and Swarm and Evolutionary Computation. He serves as reviewer for sixty international journals and four conferences in computational intelligence and its applications.


THEORY - Theory

Description

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

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

Scope

Topics include (but are not limited to):

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


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

Track Chairs

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.

 

Pietro Simone Oliveto

Department of Computer Science, University of Sheffield | webpage

Pietro S. Oliveto is Head of the Algorithms Group at the University of Sheffield, UK. He received the Laurea degree and PhD degree in computer science respectively from the University of Catania, Italy in 2005 and from the University of Birmingham, UK in 2009. He has been EPSRC PhD+ Fellow (2009-2010) and EPSRC Postdoctoral Fellow (2010-2013) at Birmingham and Vice-Chancellor's Fellow (2013-2016) and EPSRC Early Career Fellow at Sheffield.

His main research interest is the performance analysis of bio-inspired computation techniques including evolutionary algorithms, genetic programming, artificial immune systems, hyper-heuristics and algorithm configuration. He has guest-edited journal special issues of Computer Science and Technology, Evolutionary Computation, Theoretical Computer Science and IEEE Transactions on Evolutionary Computation. He has co-Chaired the the IEEE symposium on Foundations of Computational Intelligence (FOCI) from 2015 to 2021 and has been co-program Chair of the ACM Conference on Foundations of Genetic Algorithms (FOGA 2021). He is part of the Steering Committee of the annual workshop on Theory of Randomized Search Heuristics (ThRaSH), Leader of the Benchmarking Working Group of the COST Action ImAppNIO, member of the EPSRC Peer Review College and Associate Editor of IEEE Transactions on Evolutionary Computation.