Program Tracks

Three days of presentations of the latest high-quality results in 13 separate and independent program tracks specializing in various aspects of genetic and evolutionary computation.

ACO-SI - Ant Colony Optimization and Swarm Intelligence
  • Christine Solnon
  • Mardé Helbig
CS - Complex Systems (Artificial Life/Artificial Immune Systems/Generative and Developmental Systems/Evolutionary Robotics/Evolvable Hardware)
  • Stéphane Doncieux
  • Dennis G. Wilson
DETA - Digital Entertainment Technologies and Arts
  • Francisco Fernández de Vega
  • Vanessa Volz
ECOM - Evolutionary Combinatorial Optimization and Metaheuristics
  • Luis Paquete
  • Francisco Chicano
EML - Evolutionary Machine Learning
  • Bing Xue
  • Jaume Bacardit
EMO - Evolutionary Multiobjective Optimization
  • Sanaz Mostaghim
  • Jonathan Fieldsend
ENUM - Evolutionary Numerical Optimization
  • Oliver Schuetze
  • Dirk Arnold
GA - Genetic Algorithms
  • Gabriela Ochoa
  • Carlos Segura
GECH - General Evolutionary Computation and Hybrids
  • Carlos Cotta
  • Michael Emmerich
GP - Genetic Programming
  • Mengjie Zhang
  • Miguel Nicolau
RWA - Real World Applications
  • Robin Purshouse
  • Tapabrata Ray
SBSE - Search-Based Software Engineering
  • Justyna Petke
  • Fuyuki Ishikawa
THEORY - Theory
  • Johannes Lengler
  • Frank Neumann


ACO-SI - Ant Colony Optimization and Swarm Intelligence


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


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

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


Christine Solnon

Professor of Computer Science at INSA Lyon
Researcher at LIRIS (Computational Geometry and Constrained Optimization team)

Mardé Helbig

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.


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


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

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

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


Stéphane Doncieux

Stephane Doncieux is Professor in Computer Science at ISIR (Institute of Intelligent Systems and Robotics), Sorbonne University, CNRS, in Paris, France. Since January 2018, he is deputy director of the ISIR, a multidisciplinary robotics laboratory with researchers in mechatronics, signal processing computer science and neuroscience. Until that date, he was in
charge of the AMAC multidisciplinary research team (Architectures and Models of Adaptation and Cognition). He was coordinator of the DREAM FET H2020 project from 2015 to 2018 (http://robotsthatdream.eu/). His research is in cognitive robotics, with a focus on learning and adaptation with evolutionary algorithms.

Dennis G. Wilson

Dennis G. Wilson is an Associate 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.


DETA - Digital Entertainment Technologies and Arts


The intersection of culture, science and technology is attracting increasingly more public attention, with frequent exhibitions, competitions and industrial involvement worldwide.

The Digital Entertainment Technologies and Arts (DETA) track at GECCO, in its tenth edition in 2020, focusses on the key application fields of arts, music, and games from the perspective of evolutionary computation, biologically inspired techniques, and more generally computational intelligence.

We invite submissions describing original work involving the use of computational intelligence techniques in the creative arts, including design, games, and music. Works of a methodological, experimental, or
theoretical nature within the context of digital entertainment and its application in industry will also be considered.


Topics of interest include, but are not limited to:

  • Aesthetic measurement and control
    • Machine learning for predicting or controlling aesthetic preference
    • Aesthetic measures for sound, photos, textures and other content
    • User modeling
    • Stylistic recognition and classification
    • Content-based similarity or recommendation
  • Biologically-inspired creativity
    • Evolutionary arts and evolutionary algorithms for creative applications
    • Interactive evolutionary algorithms
    • Creative virtual ecosystems
    • Artificial creative agents
    • Definition or classification of creativity
  • Interactive environments and games
    • Virtual worlds
    • Reactive worlds and immersive environments
    • Procedural content generation
    • Game AI
    • Intelligent interactive narrative
    • Learning and adaptation in games
    • Search methods for games
    • Player experience measurement and optimization
  • Composition, synthesis, generative arts
    • Visual art, architecture and design
    • Creative writing
    • Cinema music composition and sound synthesis
    • Generative art
    • Synthesis of textures, images, animations
    • Non-realistic rendering
    • Generation or learning of environmental responses
  • Analysis of computational intelligence techniques for games, music and the arts
  • Innovative industrial / educational applications of evolutionary creativity


Francisco Fernández de Vega

Francisco Fernández de Vega is Computer Architecture Full Professor at the
University of Extremadura. He received his BS from the University of Seville 1993, MS from the University of Seville 1997, and Ph. D from the University of
Extremadura 2001 (best PhD award 2002). His research interests include
Parallel and Distributed Evolutionary Algorithms and their applications to
multiple aspects of art, music and design. He has published more than 250
referred papers in Conferences and Journals. He is co-chair of the Task Force on Creative Intelligence, IEEE Computational Intelligence Society. His work was been internationally awarded, including 2013 ACM GECCO Art, Design and Creativity Competition award and finalist at "Show Your World" international art competition, 2017 New York.

Vanessa Volz

Vanessa Volz is an AI researcher at modl.ai (Copenhagen, Denmark), with focus in computational intelligence in games. She received her PhD in 2019 from TU Dortmund University, Germany, for her work on surrogate-assisted evolutionary algorithms applied to game optimisation. She holds B.Sc. degrees in Information Systems and in Computer Science from WWU Münster, Germany. She received an M.Sc. with distinction in Advanced Computing: Machine Learning, Data Mining and High Performance Computing from University of Bristol, UK, in 2014. Her current research focus is on employing surrogate-assisted evolutionary algorithms to obtain balance and robustness in systems with interacting human and artificial agents, especially in the context of games.


ECOM - Evolutionary Combinatorial Optimization and Metaheuristics


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


The ECOM track encourages original submissions on 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.


Luis Paquete

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.

Francisco Chicano

Francisco Chicano is Associate Professor in the Department of Languages and Computing Sciences of the University of Malaga, Spain. He has a degree in Computer Science (2003) and Physics (2014) and a PhD in Computer Science (2007). His research interests and publications include the application of theoretical results to the design of new search algorithms and operators for combinatorial optimization and the application of metaheuristic algorithms to software engineering problems (Search-Based Software Engineering). He has served as Program Chair in the EvoCOP conference, as Track Chair in GECCO and is in the editorial board of Evolutionary Computation (MIT Press), Journal of Systems and Software, Engineering Applications of Artificial Intelligence and ACM Transactions on Evolutionary Learning and Optimization.


EML - Evolutionary Machine Learning


The Evolutionary Machine Learning (EML) track at GECCO covers advances in the theory and application of evolutionary computation methods to Machine Learning (ML) problems. Evolutionary methods can tackle many different tasks within the ML context, including problems related to supervised, unsupervised,
semi-supervised, and reinforcement learning, as well as emergent topics such as transfer learning and domain adaptation, deep learning, learning with a small number of examples, and learning with unbalanced data and missing data. The tasks range from classification, via clustering, regression, prediction to time series analysis and ML problems.

The global search performed by evolutionary methods frequently provides a valuable complement to the local search of non-evolutionary methods and combinations of the two often show particular promise in practice.

This track aims to encourage information exchange and discussion between researchers with an interest in this growing research area. We encourage submissions related to theoretical advances, the development of new (or modification of existing) algorithms, as well as application-focused papers.


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

  • Main EML paradigms and algorithms
    • Learning Classifier Systems (LCS) and evolutionary Rule-Based Systems
    • Genetic Programming (GP) when applied to machine learning tasks (as opposed to function optimisation)
    • Evolutionary ensembles, where evolution generates a set of learners which jointly solve problems
    • Evolutionary transfer learning and domain adaptation
    • Evolutionary deep learning and evolving deep structures
    • Evolving neural networks or neuroevolution (when applied to ML tasks)
    • Hyper-parameter tuning of machine learning (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 and differential evolution) based machine learning paradigms and algorithms.
  • Theoretical and methodological advances on EML
    • Identification and modelling of learning and scalability bounds
    • Evolutionary computation techniques for feature extraction, feature selection, and feature construction
    • Connections and combinations with machine learning theory (e.g. PAC theory and VC dimension)
    • Analysis of the evolved/learned models including visualisation
    • Generalisation and overfitting
    • Policy search and reinforcement learning when rooted in machine learning theory
    • Analysis and robustness in stochastic, noisy, or non-stationary environments
    • More effective and efficient algorithms
    • Addressing significant machine learning challenges such as representation, data sampling, scalability

  • Applications of EML
    • Data mining
    • Bioinformatics and life sciences
    • Computer vision, image processing and pattern recognition
    • Dynamic environments, time series and sequence learning
    • Cognitive systems and cognitive modelling
    • Artificial Life
    • Economic modelling
    • Cyber security


Bing Xue

Bing Xue received her PhD degree in 2014 at Victoria University of Wellington (VUW), New Zealand.  She is now working as an Associate Professor at VUW, and with the Evolutionary Computation Research Group at VUW, and her research focuses mainly on evolutionary computation, machine learning and
data mining, particularly, evolutionary computation for feature selection, feature construction, dimension reduction, symbolic regression, multi-objective optimisation, bioinformatics and big data. Bing is currently leading the strategic research direction on evolutionary feature selection and construction in Evolutionary Computation Research Group at VUW, and has been organising special sessions and issues on evolutionary computation for feature selection
and construction. She is also the Chair of IEEE CIS Task Force on Evolutionary Computation for Feature Selection and Construction, Chair 
of Data Mining and Big Data Analytics Technical Committee, Vice-Chair of Task Force on Evolutionary Deep Learning and Applications, IEEE CIS.
She has been serving as a Chair of Evolutionary Machine Learning Track at GECCO 2019, Chair of Women@GECCO 2018, guest editor, associated editor or editorial board member for international journals, and program chair, special session chair, symposium/special session organiser for a number of
international conferences, and as reviewer for top international journals and conferences in the field.

Jaume Bacardit

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.


EMO - Evolutionary Multiobjective Optimization


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


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

  • Handling of continuous, combinatorial or mixed-integer problems
  • Test problems and benchmarking
  • Selection mechanisms
  • Variation mechanisms
  • 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


Sanaz Mostaghim

Sanaz Mostaghim is a full Professor of Computer Science and the chair of
Computational Intelligence at Otto von Guericke University Magdeburg, Magdeburg, Germany. She received her Ph.D. degree in electrical engineering and computer science from Paderborn University, Germany and has worked as a postdoctoral fellow and lecturer at ETH Zurich, Switzerland (2004-2006) and Karlsruhe Institute of Technology, Germany (2006-2014). Her current research interests include evolutionary multiobjective optimization, swarm intelligence, and their applications in robotics, science, and industry. Sanaz is an active member in international communities. She is a Planning Group
Member (PGM) for Mathematics/Informatics/Engineering - Japanese-American-German Frontiers of Science, Alexander von Humboldt Foundation, an elected member of Administrative Committee (ADCOM) - IEEE Computational Intelligence Society (IEEE-CIS) since 2015. Sanaz has served an Associate Editor for IEEE Transactions on Evolutionary Computation, IEEE transactions on Cybernetics, Associate Editor for IEEE Transactions on System, Man and Cybernetics: Systems, and IEEE Transactions on
Emerging Topics in Computational Intelligence. Currently she is a member of the steering board of the IEEE Transactions on Games. She is also a program committee member for Genetic and Evolutionary Computation Conference (GECCO), Evolutionary Multi-Criterion Optimization (EMO), Congress on Evolutionary Computation (CEC) and other international conferences since 2005.

Jonathan Fieldsend

Jonathan Fieldsend is an Associate Professor in Computational Intelligence at the University of Exeter. He has a degree in Economics from Durham University, a Masters in Computational Intelligence from the University of Plymouth and a PhD in Computer Science from the University of Exeter. He has held postdoctoral positions as a Research Fellow (working on the interface of Bayesian modelling and optimisation) and as a Business Fellow (focusing on knowledge transfer to industry) prior to his appointment to an academic position at Exeter.

He has published widely on theoretical and applied aspects of evolutionary multi-objective optimisation, and also in the field of machine learning — and has ongoing interests on the interface between these areas. His theoretical work includes algorithm development and analysis, along with data structures required for efficient multi-objective optimisation and Pareto set maintenance. His applied work includes costly and uncertain industrial design problems, air traffic control safety systems, automating biological experiments and robust multi-objective routing.

He has previous been a workshop organiser at GECCO for VizGEC (Visualisation Methods in Genetic and Evolutionary Computation), SAEOpt (Surrogate-Assisted Evolutionary Optimisation) and EAPU (Evolutionary Algorithms for Problems with Uncertainty). He has been active within the evolutionary computation community as a reviewer and program committee member since 2003.


ENUM - Evolutionary Numerical Optimization


The ENUM track (Evolutionary NUMerical optimization) is concerned with randomized search algorithms and continuous search spaces. The scope of the ENUM track includes, but is not limited to, stochastic methods like Cross-Entropy (CE) methods, Differential Evolution (DE), continuous versions of Genetic Algorithms (GAs), Estimation-of-Distribution Algorithms (EDAs), Evolution Strategies (ES), Evolutionary Programming (EP), continuous Information Geometric Optimization (IGO), Markov Chain Monte Carlo methods (MCMC), and Particle Swarm Optimization (PSO).


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


Oliver Schuetze

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.

Dirk Arnold

Dirk Arnold is a Professor in the Faculty of Computer Science at Dalhousie University. His research interests span evolutionary computation, numerical optimization, and machine learning. He is an Associate Editor of Evolutionary Computation, a Member of the Editorial Board of Computational Optimization and Applications, and was General Chair of GECCO 2014.


GA - Genetic Algorithms


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

  • Practical, 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 control (e.g., fitness sharing and crowding, automatic speciation, spatial models such as island/diffusion)
  • Bilevel and multi-level optimization
  • Ensemble based genetic algorithms
  • Model-Based Genetic Algorithms

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


Gabriela Ochoa

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). 

Carlos Segura

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 700 citations and his
h-index is 17. 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. 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).


GECH - General Evolutionary Computation and Hybrids


General Evolutionary Computation and Hybrids is a new track that recognises that Evolutionary Algorithms are often used as part of a larger system, or together in synergy with other algorithms.
We welcome high quality papers on a range of topics that might not fit solely into any of the other track descriptions.


Areas of interest include the following - but the limit should be your creativity not ours!

  • Combining different ways of creating or improving solutions
    • such as co-evolution, neuro-evolution, memetic algorithms, and other hybrids.
  • Combining EAs with Machine Learning Algorithms that learn a model of the search space
    • such as surrogate-assisted optimisation of expensive fitness functions,
  • Combining EAs with learning algorithms that attempt to learn how to control or co-ordinate a range of algorithms
    • such as parameter tuning, parameter control, and self * approaches such as hyper-heuristics and self-adaptation,
  • Novel nature-inspired paradigms
  • Algorithms for Dynamic and stochastic environments
  • Statistical analysis techniques for EAs
  • Evolutionary algorithm toolboxes


Carlos Cotta

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.

Michael Emmerich

Michael Emmerich (Associate Professor) is leader the Multicriteria Optimization and Decision Analysis (MODA) research group at Leiden University. He currently works as Associate Professor at Leiden University, and as a guest researcher at the University of Jyväskylä, Finland. He was born in 1973 in Coesfeld (Germany) and received his doctorate in 2005 from Dortmun
d University (H.-P. Schwefel and P. Buchholz, promoters). He worked as researcher at ICD e.V. (Germany), IST Lisbon, University of the Algarve (Portugal), ACCESS Material Science e.V. (Germany), and the FOM/AMOLF institute on Fundamental Science of Matter (Netherlands). He is known for pioneering work on model-assisted and indicator-based multiobjective optimization, and has edited 5 books, and co-authored more than 160 publications in deterministic and stochastic optimization algorithms and their application in computational medicine, chemistry, and engineering design.


GP - Genetic Programming


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


Advances in genetic programming include but are not limited to:

  • Analysis: Information theory, Complexity, Run-time, Visualization, Fitness Landscape, Generalisation, Domain adaptation
  • Synthesis: Programs, Algorithms, Circuits, Systems
  • Applications: Classification, clustering, Control, Data mining, Big data analytics, Regression, Semi-supervised, Policy search, Prediction, Continuous and combinatorial Optimisation, Streaming data, Design, Inductive Programming, computer vision, feature selection and construction, 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, Debugging, 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


Mengjie Zhang

Mengjie Zhang is a Fellow of Royal Society of New Zealand, a Fellow of IEEE, 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, a member of the Faculty of Graduate Research Board at the University, Associate Dean (Research and Innovation) in the Faculty of Engineering, and Chair of the Research Committee of the Faculty of Engineering and 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, Natural Computing, and Engineering Applications of Artificial Intelligence, 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, 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 Feature Selection and Construction, 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.

Miguel Nicolau

Miguel is a Lecturer in Business Analytics, in the School of Business of University College Dublin, Ireland. His research interests revolve around Artificial Intelligence, Machine Learning, Evolutionary Computation, Business Analytics, Genetic Programming, and Real-World Applications. He is a senior member of the UCD's NCRA (Natural Computing Research & Applications) group.


RWA - Real World Applications


The Real-World Applications (RWA) track welcomes rigorous experimental, computational and/or applied advances in evolutionary computation (EC) in any discipline devoted to the study of real-world problems. The aim is to bring together contributions from the diverse application domains into a single event. The focus is on applications including but not limited to:

  • Papers that present novel developments of EC, grounded in real-world problems.
  • Papers that present new applications of EC to real-world problems.
  • Papers that analyse the features of real-world problems, as a basis for designing EC solutions.

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


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


Robin Purshouse

Robin Purshouse is a Reader (Associate Professor) in Decision Modelling and Optimization at the University of Sheffield. He received the MEng degree in Control Systems Engineering in 1999 and a PhD in Control Systems in 2004 for his research on evolutionary multi-objective optimization (both from the University of Sheffield). His research interests are in methods for design optimization, with a focus on decision support for real-world applications. He has successfully applied evolutionary algorithms to many-objective, robust and distributed optimization problems in projects with leading manufacturers of complex engineered products, such as Jaguar Land Rover and Ford Motor Company. Robin also has research interests in the modelling and simulation of complex social systems, with a focus on health behaviours. He presently leads the National Institutes of Health-funded CASCADE project, developing agent-based models that aim to explain alcohol use patterns in the US over the last 40 years.

Tapabrata Ray

Tapabrata Ray is a Professor with the School of Engineering and Information Technology at University of New South Wales, Canberra. He currently leads the efforts of the Multidisciplinary Design Optimization Group.


SBSE - Search-Based Software Engineering


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

We invite papers that address problems in the software engineering domain through the use of heuristic search techniques. We would also thus like to invite papers from the genetic improvement area where evolutionary computation has been used for the purpose of software improvement.

We particularly encourage papers demonstrating novel search strategies or the application of SBSE techniques to new problems in software engineering. Papers may also address the use of methods and techniques for improving the applicability and efficacy of search-based techniques when applied to software engineering problems. While empirical results are important, papers that do not contain strong empirical results - but instead present new sound approaches, concepts, or theory in the search-based software engineering area - are also very welcome.

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


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

  • Ant Colony Optimisation
  • Automatic Algorithm configuration and Parameter Tuning
  • Estimation of Distribution Algorithms
  • Evolutionary Computation
  • Genetic Programming
  • Hybrid and Memetic Algorithms
  • 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 Systems
  • Enabling Self-Configuring/Self-Healing/Self-Optimising Software Systems
  • Improving Software's properties, such as runtime or energy consumption, and other
  • Network Design and Monitoring
  • Optimising Functional and Non-Functional Software Properties (Genetic Improvement)
  • Predictive Modelling for Software Engineering Tasks
  • Project Management and Organisation
  • Testing including test data generation, regression test optimisation, test suite evolution
  • Requirements Engineering
  • Software Evolution and Maintenance
  • Program Repair
  • Refactoring and Transformation
  • Software Security
  • Software Transplantation
  • System and Software Integration
  • System and Software Verification


Justyna Petke

Justyna Petke is a Principal Research Fellow and a Proleptic Associate Professor at the Centre for Research on Evolution, Search and Testing (CREST) in University College London. Her main expertise lies in genetic improvement which uses automated search to find improved software versions. Her work on the subject was awarded a Silver (GECCO 2014) and a Gold Humie (GECCO 2016). Her various organisational roles include co-chairing five international Genetic Improvement Workshops and the SBSE track at GECCO 2019. She's also on the editorial board of the Genetic Programming and Evolvable Machines journal.

Fuyuki Ishikawa

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.


THEORY - Theory


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

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


Topics include (but are not limited to):

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

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


Johannes Lengler

Johannes Lengler is a senior researcher at ETH Zurich, Switzerland.

He received his PhD in mathematics at Saarland University, Germany, before he turned to theoretical computer science and computational neuroscience. The overarching theme of his research is to understand the dynamics of stochastic processes. His main research areas are generative network model for large social and technological networks, computational models for learning and cognitive processes in the brain, and theory of nature-inspired search heuristics. In the latter field, he has worked on runtime analysis, black-box complexity, and theoretical benchmarks for evolutionary and genetic algorithms.

Frank Neumann

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 "IEEE Transactions on Evolutionary Computation" (IEEE). 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.