Organizers and Tracks:

GECCO Organizers

General Chair:
Anna I Esparcia-Alcázar

Sara Silva

Proceedings Chair:
Juan Luis Jiménez-Laredo
Local Chair:
J. Ignacio (Iñaki) Hidalgo
Publicity Chair:
A. Şima Etaner Uyar
Tutorials Chair:
Anabela Simões
Students Chair:
Katya Rodríguez-Vázquez
Workshops Chair:
Gisele Pappa

Evolutionary Computation in Practice:
Thomas Bartz-Beielstein

Jörn Mehnen
Business Committee:
Jürgen Branke
Pier-Luca Lanzi

Competitions Chair:
Mike Preuss
Social Media Chair:
Pablo García-Sánchez
Local Arrangements:
Luis Hernández-Yáñez
Late Breaking Abstracts chair

Dirk Sudholt

Program Tracks
Three days of presentations of the latest high-quality results in more than 15 separate and independent program tracks specializing in various aspects of genetic and evolutionary computation.

Planned Program Tracks:

Ant Colony Optimization and Swarm Intelligence (ACO-SI)
more info:
- Sanaz Mostaghim -
- Manuel López-Ibáñez -
Artificial Immune Systems and Artificial Chemistries (AIS-AChem)
more info:
- Jon Timmis -
- Christine Zarges-
Artificial Life / Robotics / Evolvable Hardware (ALIFE)
more info:
- Terry Soule -
- Luis Correia -
Biological and Biomedical Applications (BIO)
more info:
- Ryan Urbanowicz -
- Mario Giacobini -
Continuous Optimization - Evolution Strategies and Evolutionary Programming (CO)
more info:
- Tobias Glasmachers  -
- Youhei Akimoto  -
Digital Entertainment Technologies and Arts (DETA)
more info:
- Francisco Fernández -
- Amy K. Hoover -
Evolutionary Combinatorial Optimization and Metaheuristics (ECOM)
more info:
- Carlos Cotta -
- Francisco B. Pereira -
Estimation of Distribution Algorithms (EDA)
more info:
- Pedro Larrañaga -
- Marta Soto  -
Evolutionary Machine Learning (EML)
more info:
- Julia Handl -
- Jan Koutník -
Evolutionary Multiobjective Optimization (EMO)
more info:
- António Gaspar-Cunha -
- Heike Trautmann -
Genetic Algorithms (GA)
more info:
- Oliver Schuetze -
- Ernesto Costa -
Generative and Developmental Systems (GDS)
more info:
- J B Mouret -
- Sebastian Risi -
Genetic Programming (GP)
more info:
- Alberto Moraglio -
- Krzysztof Krawiec -
Hot Off the Press
more info:
Integrative Genetic and Evolutionary Computation (IGEC)
more info:
- Julian. F. Miller -
- Paweł Widera -
Parallel Evolutionary Systems (PES)
more info:
- Stefano Cagnoni -
- JJ Merelo -
Real World Applications (RWA)
more info:
- Emma Hart -
- Leonardo Trujillo -

Search-Based Software Engineering and Self-* Search (SBSE-SS)
more info:

- Gabriela Ochoa -
- Marouane Kessentini -
Theory (THEORY)
more info:
- Carola Doerr -
- Francisco Chicano -

Ant Colony Optimization and Swarm Intelligence (ACO-SI)


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, and self-organization. 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. 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
  • Benchmarking and new empirical results
  • Parallel/distributed implementations and applications
  • Large-scale applications
  • Applications to multi-objective, dynamic, and noisy problems
  • Applications to continuous and discrete search spaces
  • Software and high-performance implementations
  • Theoretical and experimental research in swarm robotics


Sanaz Mostaghim
She is a professor of computer science at Otto von Guericke University of Magdeburg, Germany. She received her habilitation, the highest German academic degree, in applied computer science from Karlsruhe Institute of technology (KIT) in 2012, where she worked as a senior lecturer. Sanaz has received a PhD degree in electrical engineering from the University of Paderborn in Germany and worked as a post-doctoral fellow at the Swiss Federal Institute of Technology (ETH) Zurich in Switzerland. Her research interests are evolutionary multi-objective algorithms, swarm intelligence and their applications in science and industry. She is an active member in several international societies such as IEEE and ACM. Currently she is the chair of two task forces about “evolutionary multi-objective optimization” and “optimization methods in bioinformatics and bioengineering” at IEEE Computational Intelligence Society. Since 2012, she is serving as associate editor for IEEE transactions on evolutionary computation and IEEE transactions on cybernetics.


Manuel López-Ibáñez is a postdoctoral researcher (Chargé de recherche) of the Belgian F.R.S.-FNRS working at the IRIDIA laboratory of Université libre de Bruxelles, Belgium. IRIDIA is one of the leading laboratories in Ant Colony Optimization and Swarm Robotics. He received the M.S. degree in computer science from the University of Granada, Granada, Spain, in 2004, and the Ph.D. degree from Edinburgh Napier University, U.K., in 2009. He has published on diverse areas such as evolutionary algorithms, ant colony optimization, multi-objective optimization, pump scheduling and various combinatorial optimization problems. His current research interests are experimental analysis, automatic configuration and automatic design of stochastic optimization algorithms, for single and multi-objective optimization. He is the main author of the multi-objective ant colony optimization (MOACO) framework ( for the automatic design of MOACO algorithms.


Artificial Immune Systems and Artificial Chemistries (AIS-AChem)


Artificial Immune Systems (AIS) is a diverse area of research that takes inspiration from the natural immune system and to develop algorithms that can be applied in a wide range of applications, including learning, optimisation and classification. Many of these algorithms are built on solid theoretical foundations, taking inspiration and understanding from mathematical models and computational simulation of aspects of the immune systems. The area of Artificial Chemistries (AChem) is concerned with developing new tools and insights into molecular based systems, so attempting to bridge the gap between chemical, biological and computational systems, or algorithms to develop novel dynamical systems and control systems. This track will highlight the latest advances in these areas, and act as a catalyst for potential interaction between these two areas.


The AIS/AChem track welcomes submissions of original and unpublished work in all aspects of AIS/AChem, including (but not limited to) the following areas:

  • Computational modelling and simulation of aspects of the immune system or chemical system
  • Applications of AIS/AChem algorithms to computational problems
  • Application to real-world problems
  • Novel algorithms or simulations of AIS/AChem systems
  • Hybridisation with other techniques
  • Empirical investigations into performance and complexity
  • Theoretical aspects including:
    • Algorithm performance
    • Convergence analysis
    • Mathematical modelling


Jon Timmis
Jon received his degree and PhD from the University of Wales, Aberystwyth. He currently is Professor of Intelligent and Adaptive Systems at the University of York in the Department of Electronics, where he is Director of York Robotics Laboratory and co-Director of York Computational Immunology Laboratory. Jon is an interdisciplinary researcher, having spent many years working in the area of immunology, developing a number of computational systems inspired by the immune system, applied to detection, diagnosis and recovery and computational models of the immune system response. He has published extensively in the area of AIS, both on applied and theoretical aspects.


Christine Zarges
Christine received her degree and PhD from the TU Dortmund, Germany, in 2007 and 2011, respectively. Afterwards, she held a postdoctoral research position at the University of Warwick, UK. She currently is a Birmingham Fellow and Lecturer in the School of Computer Science at the University of Birmingham, UK. Her PhD topic was "Theoretical Foundations of Artificial Immune Systems" and her current research focuses on the theoretical analysis of all kinds of randomised search heuristics. She is also interested in computational and theoretical aspects of immunology. She has given tutorials on "Artificial Immune Systems for Optimisation" at previous GECCOs and was co-chair of the AIS track at GECCO 2014. She is member of the editorial board of Evolutionary Computation (MIT Press) and co-organiser of FOGA 2015.


Artificial Life / Robotics / Evolvable Hardware (ALIFE)


This track promotes evolutionary computation and bio-inspired heuristics as methods to address engineering problems and scientific questions in a wide range of areas, including (but are not limited to): artificial life, evolutionary robotics, and evolvable hardware.

Artificial life studies artificial systems (software, hardware, or chemical) with properties similar to those of living systems. There are 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, evolvability, active perception, communication, self-organization and cognition. This 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 computation techniques can be particularly useful for many of the problems in robotics. The evolution of controllers, morphologies, sensors, and communication protocols can be used to build systems that provide robust, adaptive and scalable solutions to difficult problems in robotics. This 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.

The term evolvable hardware denotes both self-evolving hardware, and the exploitation of evolutionary techniques for creating hardware. While the first task sounds ambitious, the second is routinely applied by industries. Contributions in this area are expected to show either real or potential applications.


Terry Soule
Dr. Soule is a Professor of Computer Science at the University of Idaho, a member of the Institute for Biology and Evolutionary Studies (IBEST), and of the BEACON Science and Technology Center for the study of evolution in action.  He is an associate faculty member of the Neuroscience and of the Bioinformatics and Computational Biology programs at the University of Idaho.  He served as the editor-in-chief for GECCO in 2012 and is a member of the ACM Special Interest Group for Genetic and Evolutionary Computation (SOGEVO) executive board.   He is the author of the textbook “A Project Based Introduction to C++” published by KendallHunt.  His research interests include evolutionary robotics and cooperative co-evolution. 


Luís M. P. Correia
Luís ( is associate professor
with habilitation at the Department of Informatics of the Faculty of Sciences of University of Lisboa (DI-FCUL) in Portugal. From 2004 he has lead theLaboratory of Agent Modelling (LabMAg) research unit. Currently he is head of department at DI-FCUL. His research interests are in the area of artificial life, autonomous robots and self-organisation in multi-agent
systems. Besides lecturing in the three cycles of informatics he has also
appointments in the cognitive science and in the complexity sciences


Biological and Biomedical Applications (BIO)


The advent and ongoing development of genetic and evolutionary computation has made it possible to solve increasingly complex problems.  Tasks that were previously intractable due to dimensionality, noise, or representation complexity, are becoming accessible.  In particular, the fields of biological and biomedical sciences are rife with modeling and data mining challenges that epitomize the kinds of problems to which genetic and evolutionary computation may be uniquely well suited to solving.  The aim of this GECCO track is to explore the use of genetic and evolutionary computation within biological and biomedical sciences.

Submissions are welcome in the following and related areas:

·  Bioinformatics
·  Biological data mining
·  Biomedical systems and drug design
·  Biotechnology
·  Ecological networks and models
·  Genome and metagenome analysis
·  High throughput sequencing
·  Modeling and simulation of biological systems
·  Protein structure and function
·  Regulatory, expression, and metabolic networks
·  Systems biology
·  Visualization and imaging of biological systems


Ryan Urbanowicz
Dr. Ryan Urbanowicz is a post-doctoral research associate at Dartmouth College where he received his Ph.D in Genetics in 2012.  He also holds a B.Eng. and M.Eng in Biological and Agricultural Engineering from Cornell University.  His current research focuses on the development of machine learning strategies for feature selection, modeling, classification, and data mining in studies of common complex human disease.  Specifically, he is interested in developing strategies that can detect, model, and characterize phenomena that hinder these tasks such as genetic heterogeneity and epistasis.  The majority of his research explores the improvement, adaptation, and application of learning classifier system algorithms to address these complex tasks.  To date, he has received two best paper awards in this area, one at GECCO 2010 in the Bioinformatics and Computational Biology track and another at the Translational Bioinformatics Conference (TBC) in 2012.


Mario Giacobini
Mario obtained a degree in Mathematics at the University of Torino (IT) in 1998, and after a PhD a post-doc in Computer Science at the University of Lausanne (CH) he came back to Torino as Assistant Professor at the Department of Veterinary Sciences. His research interests focus on Artificial Life, both as bio-inspired computational techniques (mainly Evolutionary Algorithms and their application to Bioinformatics) and as modeling of biological phenomena (ranging from Epidemiology to Molecular Biology) mainly using concepts and instruments of Network Science. Part of Mario Giacobini's research activity is carried out at the Molecular Biotechnolgy Center where he is PI of the Computational Biology Unit. He also collaborates with the Applied Research Group on Computational Complex Systems of the Department of Computer Science.


Continuous Optimization (CO)
-former ESEP-


The CO (former ESEP) track is concerned with black-box search paradigms for optimization in continuous search spaces. It aims to cover but is not limited to stochastic methods like evolution strategies (ES) and Evolutionary Programming (EP), as well as the continuous versions of genetic algorithm (GA), estimation of distribution algorithms (EDA), particle swarm optimization (PSO), differential evolution (DE), and more generally stochastic methods for continuous optimization, such as Markov Chain Monte Carlo (MCMC) and cross-entropy methods.

The track invites submissions that present original work on stochastic optimization in continuous domains. We encourage papers focusing on theoretical analysis as well as applications to real-world problems and benchmark function suites. We welcome further development, improvement and analysis of optimization algorithms, as well as work on problems such as large-scale and budgeted optimization, handling of constraints, multi-modality, noise, uncertain and/or changing environments, and mixed-integer problems.


Tobias Glasmachers
He is a junior professor at Ruhr-University Bochum, Germany. He received his diploma (2004) and his doctorate degree (2008) from the Mathematics department of the Ruhr-University. Then he joined the swiss AI lab IDSIA for two years as a post-doc and then returned to Bochum in 2012. His research is located in the areas of supervised machine learning and optimization in continous spaces, in particular with evolution strategies. His work on randomized search heuristics is focused on natural evolution strategies (NES) as well as on multi-objective optimization.


Youhei Akimoto
He is an assistant professor at Shinshu University, Japan. He received his diploma (2007) in computer science and his master degree (2008) and PhD (2011) in computational intelligence and systems science from Tokyo Institute of Technology, Japan. Since 2010, he was also a research fellow of Japan Society for the Promotion of Science for one year. Afterwords, He joined TAO group at INRIA, France, for two years as a post-doc. He started working at Shinshu University in 2013. His research interests include design principle and theoretical analysis of stochastic search heuristics in continuous domain, in particular, the Covariance Matrix Adaptation Evolution Strategy.


Digital Entertainment Technologies and Arts (DETA)


Arts, music, and games are key application fields for evolutionary computation, computational intelligence, and biologically inspired techniques. The digital entertainment technologies and arts (DETA) track focuses on these areas. We invite submissions describing original work involving the use of computation in the creative arts, including design, games, and music. Works of a methodological, experimental, or theoretical nature will be considered. However, in all accepted work there must be some connection to evolutionary computation or other forms of computational intelligence or biologically inspired algorithms.
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
    • Non-realistic rendering, animations
    • Content-based similarity or recommendation
    • User modeling
  • 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
    • Generation or learning of environmental responses
    • Stylistic recognition and classification
  • Analysis of computational intelligence techniques for games, music and the arts


Amy K. Hoover
Amy K. Hoover is a currently a postdoctoral researcher at the Institute of Digital Games, University of Malta. Supported by a National Science Foundation Graduate Research Fellowship, she earned her Ph.D. in computer science at the University of Central Florida (2014). She has won best paper awards at GECCO and EvoMUSART for her work in evolutionary inspired music generation, and has co-chaired the GECCO Art, Design, and Creativity competition since 2012.

Francisco Fernández
He is Associate 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 and design. He's been guest editor with Soft Computing, Parallel Computing, Journal of Parallel and Distributed, Natural Computing and edited the books Parallel and Distributed Computational Intelligence and Parallel Architectures and Bioinspired Algorithms, with Springer. He has published more than 200 referred papers in Conferences and Journals. He is co-chair of the Task Force on Creative Intelligence, IEEE Computational Intelligence Society. His work was recently awarded with the 2013 ACM GECCO Art, Design and Creativity Competition.


Evolutionary Combinatorial Optimization and Metaheuristics (ECOM)


The aim of this track is to provide a forum for the presentation and discussion of high quality research on metaheuristics for combinatorial optimization problems. Plenty of hard problems in a huge variety of areas, including logistics, network design, bioinformatics, engineering, business, etc., have been tackled successfully with metaheuristic approaches. For many problems the resulting algorithms are considered to be state-of-the-art.
Apart from evolutionary algorithms, the class of metaheuristics includes prominent members such as tabu search, iterated local search, variable neighborhood search, memetic algorithms, simulated annealing, GRASP, ant colony optimization and others. 

The ECOM track encourages original submissions on all aspects of evolutionary combinatorial optimization and metaheuristics, including, but not limited to:

  • Applications of metaheuristics to combinatorial optimization problems
  • Theoretical developments in combinatorial optimization and metaheuristics
  • Representation techniques
  • Neighborhoods and efficient algorithms for searching them
  • Variation operators for stochastic search methods
  • Search space and landscape analysis
  • Comparisons between different (also exact) techniques
  • Constraint-handling techniques
  • Hybrid methods, adaptive hybridization techniques and Memetic Computing Methodologies
  • Hyper-heuristics for combinatorial optimization problems
  • Insight into problem characteristics of problem classes

Evolutionary algorithms, local search, variable neighborhood search, iterated local search, tabu search, simulated annealing, very large scale neighborhood search, ant colony optimization, particle swarm optimization, scatter search, path relinking, GRASP, search space and landscape analysis, representations, variation operators, hybridization, hyper-heuristics, matheuristic, memetic algorithm, vehicle routing, cutting and packing, scheduling, timetabling, bioinformatics, transport optimization, routing, network design, graph problems, string problems. 


Carlos Cotta
He 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 150 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.

Francisco B. Pereira
He received his Ph.D. in Computer Science from the University of Coimbra, Portugal, in 2002. He is currently a Professor at the Informatics Engineering Department from the Polytechnic Institute of Coimbra, and a researcher at the Evolutionary and Complex Systems Group from the Centre for Informatics and Systems of the University of Coimbra, Portugal. His research interests are focused on the development and application of bio-inspired algorithms to combinatorial and numerical optimization problems.
He edited one book on bio-inspired approaches to the vehicle routing problem and published over 60 peer-reviewed scientific papers in journals and conference proceedings, including three best paper awards.  He co-organized several special sessions and workshops on topics related to hybrid algorithms, evolutionary optimization, and artificial life.


Estimation of Distribution Algorithms (EDA)


Estimation of distribution algorithms (EDAs) are based on the explicit use of probability distributions. They replace traditional variation operators of evolutionary algorithms, such as mutation and crossover, by building a probabilistic model of promising solutions and sampling the built model to generate new candidate solutions. Using probabilistic models for exploration in evolutionary algorithms enables the use of advanced methods of machine learning and statistics for automated identification and exploitation of problem regularities for broad classes of problems. In addition they provide natural ways to introduce problem information into the search by means of the probabilistic model and also to get information about the problem that is being optimized. EDAs provide a robust and scalable solution to many important classes of optimization problems with only little problem specific knowledge.
The aim of the track is to attract the latest high quality research on EDAs in particular, and the use of explicit probabilistic and graphical models in evolutionary algorithms in general. We encourage the submission of original and previously unpublished work especially in the following areas:

  • Advances in the theoretical foundations of EDAs.
  • Position papers on EDA-related topics.
  • Reviews of specific EDA-related aspects.
  • Statistical and machine learning modeling in evolutionary algorithms.
  • EDAs for dynamic, multiobjective or noisy problems and
  • Interactive and self-adaptive EDAs.
  • EDAs in practical decision making.
  • Links between EDAs and other model-based search.
  • Large scale EDAs.
  • EDAs based on new algorithms for learning probabilistic models from data.
  • Probabilistic models as fitness surrogates in evolutionary algorithms.
  • Interfaces between EDA and Ant Colony Optimization, Evolution Strategies, Cross-Entropy Method or other related methods.
  • Comparisons of EDAs and other metaheuristics, evolutionary algorithms, more traditional optimization methods of operations research or hybrids thereof.
  • Hybridizing EDAs with other metaheuristics.
  • Novel applications for EDAs.

The above list of topics is not exhaustive; if you think that your work does not fit the above categories but the work should belong to the EDA track, please contact the track chairs to discuss this issue.


Pedro Larrañaga
He is Full Professor in Computer Science and Artificial Intelligence at the Technical University of Madrid (UPM) where he leads the Computational Intelligence Group. His research interests are in the fields of Bayesian networks, estimation of distribution algorithms, multi-label classification, regularization, and data streams, with applications in bioinformatics, biomedicine and neuroscience. He has supervised more than 20 PhD students, published more than 100 papers in international journals and participated in more than 30 projects in collaboration with the industry. He has also participated in more than 50 projects granted by public institutions, the most recent one the Human Brain Project, selected as one the two European Flagships for the period 2013-2023. He has been Expert Manager of the Computer Technology area, Deputy Directorate of research projects, of the Spanish Ministry of Science and Innovation (2007-2010). He is Fellow of the European Artificial Intelligence Society (ECCAI) since 2012. He has received the Spanish National Award in Computer Science in 2013.


Marta Soto
She received her PhD degree in Mathematics and Artificial Intelligence at the Institute of Cybernetics, Mathematics and Physics (ICIMAF), Havana, Cuba, in 2001. Currently, she is the head of the Department of Interdisciplinary Mathematics at ICIMAF. Dr. Soto's primary research interests are focused on Computational Intelligence and Evolutionary Optimization with particular emphasis in the application of copula theory in Estimation of Distribution Algorithms. Machine learning, data analysis using the R programming language and Bioinformatics applications are also part of her current research activities.


Evolutionary Machine Learning (EML)


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 unsupervised, semi-supervised and supervised, as well as reinforcement learning. 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:

  • Evolutionary methods designed to address subproblems of ML e.g. feature selection and construction
  • Math-heuristics for ML problems
  • Learning Classifier Systems
  • Hyper-parameter tuning with evolutionary methods
  • Genetic Programming (GP) when applied to machine learning tasks (as opposed to function optimisation)
  • Evolutionary ensembles, in which evolution generates a set of learners which jointly solve problems
  • Evolving neural networks or Neuroevolution when applied to ML tasksapplied in the following areas:
    • Data mining
    • Dynamic environments, time series and sequence learning
    • Bioinformatics and life sciences
    • Robotics, engineering, hardware/software design, and control
    • Cognitive systems and cognitive modeling
    • Artificial Life
    • Economic modelling
    • Network security
    • Other kinds of real-world ML applications


Julia Handl
She holds a BSc (Hons) in computer Science from Monash University, an MSc in Computer Science from the University of Erlangen-Nuremberg and a PhD from the University of Manchester. Her PhD work explored the use of multiobjective optimization in unsupervised and semi-supervised classification. She has developed multiobjective algorithms for clustering and feature selection tasks in these settings, and her work has highlighted some of the theoretical and empirical advantages of this approach. In 2011 she was appointed as a Lecturer in Decision Sciences at the University of Manchester. Her research is interdisciplinary and includes the development and application of optimization and machine learning methods to a variety of problems in computational biology, business and finance.

Jan Koutník
He received his Ph.D. in computer science from the Czech Technical University in Prague in 2008. He works as machine learning researcher at The Swiss AI Lab IDSIA. His research is mainly focused on artificial neural networks, recurrent neural networks, evolutionary algorithms and deep-learning applied to reinforcement learning, control problems, image classification, handwriting and speech recognition.



Evolutionary Multiobjective Optimization (EMO)


Multiobjective optimization problems (MOPs) are the vector equivalent of standard optimization problems, with a vector objective function mapping each candidate solution to several (two or more) objective values. As MOPs are a very natural generalization of standard optimization, they describe a wide variety of problems, and have numerous applications in practice. Typically in a MOP, the objectives are at least partially in conflict so that no single solution is optimal in all objectives, and instead optimal (Pareto optimal) tradeoffs are sought.
The EMO track at GECCO aims at bringing together both experts and newcomers working in this area to discuss novel aspects of EMO theory and methodology. Topics may include, but are not limited to:

  • New developments for existing classes of EMO algorithms: e.g. aggregation- based, dominance-based, indicator-based
  • Methods to handle problems with more than three objectives
  • Methods to handle problems featuring uncertainty in decision-space and/or objective-space
  • Methods to handle expensive objective function evaluations
  • Theoretical analysis of EMO algorithms
  • Techniques to maintain diversity in an EMO context
  • New developments in collaborative and parallel EMO approaches: e.g. algorithm portfolios, divide-and-conquer methods, use of parallel and distributed hardware
  • New approaches to solving different multi-criterion problem classes: e.g. network topology problems, assignment problems, multidisciplinary optimization problems
  • New developments in related paradigms, e.g. ant colony optimization, particle swarm optimization, differential evolution, artificial immune systems, estimation of distribution algorithms, variable neighbourhood search, iterated local search, simulated annealing, Tabu search
  • New paradigms for population-based multi-criterion optimization
  • New benchmark problems, performance indicators, and methods for empirical analysis of EMO algorithms
  • Hybrid methodologies.
  • Parallelization of EMO techniques
  • Local search in an EMO context (e.g., memetic algorithms)
  • Multiobjective combinatorial optimization
  • Incorporation of preferences into EMO algorithms
  • Dynamic multiobjective optimization using EMO algorithms
  • Special representations and operators for EMO algorithms
  • Software architectures for development of EMO algorithms
  • Learning and intelligent mechanisms for EMO
  • Multi-level optimization using EMO algorithms
  • Multi-criteria decision making and EMO techniques
  • Multiobjectivization studies
  • Set-based Multicriteria Decision Making (MCDM) approaches
  • Real-world applications in engineering design, financial applications, bioinformatics and health, computer science, scientific computation, production, manufacturing and logistics, etc


Antonio Gaspar-Cunha
He ( received the PhD degree in Optimization and Modeling of Single Screw Extrusion from the University of Minho, Portugal, in 2000. He is currently an Auxiliary Professor of Polymer Processing at the University of Minho. The main areas of scientific activity are modeling of polymer extrusion based processes and multi-objective Multidisciplinary Design and optimization systems (MO-MDO). The research interests include: robustness analysis, decision making in multi-objective environment and feature selection using multi-objective optimization algorithms. He is editor of 4 books, author (co-)author of circa of 17 book chapters, 44 papers published in international refereed journals, and more than 120 papers published in proceedings of international conferences.

Heike Trautmann
She is Professor of Information Systems and Statistics at the University of Münster, Germany. She graduated in Statistics and, after working in a consulting company for two years, received her PhD and habilitation in Statistics / Multiobjective Optimization.
Her current research activities are focused on multiobjective (evolutionary) optimisation - in particular preference incorporation, performance assessment, process chains and stopping criteria - as well as algorithm selection and benchmarking concepts. She was involved in organizing the special session "Designing Evolutionary Processes" at the Congress on Evolutionary Computation (CEC) in 2010, the "Joint Workshop on Automated Selection and Tuning of Algorithms" at Parallel Problem Solving from Nature (PPSN) in 2012 as well as the track "Multiobjective Optimization" at the Evolve Conferences in 2012 and 2014. Furthermore, she organized the 1st Workshop on COnfiguration and SElection of ALgorithms (COSEAL) in Münster in 2014.


Genetic Algorithms (GA)


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:

    1. Practical and theoretical aspects of GAs
    2. Design of new GA operators including representations, fitness functions, initialization, termination, selection, recombination, and mutation
    3. Design of new and improved GAs
    4. Comparisons with other methods (e.g., empirical performance analysis)
    5. Hybrid approaches (e.g., memetic algorithms)
    6. Design of tailored GAs for new application areas
    7. Handling uncertainty (e.g., dynamic and stochastic problems, robustness)
    8. Metamodeling and surrogate assisted evolution
    9. Interactive GAs
    10. Co-evolutionary algorithms
    11. Parameter tuning and control (including adaptation and meta-GAs)
    12. Constraint Handling
    13. Diversity control (e.g., fitness sharing and crowding, automatic speciation, spatial models such as island/diffusion)
    14. Bilevel and multi-level optimization
    15. Ensemble 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.


Oliver Schuetze
He is currently Professor at the Cinvestav-IPN in Mexico City (Mexico). He received his diploma in Mathematics in 1999 from the University of Bayreuth (Germany) and his PhD from the University of Paderborn (Germany) in 2004. Afterwards, he held post-doc positions at the Institute for Industrial Mathematics in Paderborn (Germany), the INRIA Futurs (now INRIA Lille - Nord Europe) in Lille (France), and the Cinvestav-IPN in Mexico City (Mexico) where he got a position as Professor in 2008.

His research interests focus on numerical and evolutionary optimization where he addresses both scalar and multi-objective optimization problems. He has co-edited 5 books and is co-author of more than 80 papers published in books, journals, and conference proceedings. He has co-organized several scientific events. For instance, he is a co-founder of the SON (Set Oriented Numerics) and founder of the NEO (Numerical and Evolutionary Optimization) workshop series. During his career he received several prices and awards. Two of the papers he co-authored received the GECCO best paper award. Further, he is co-author of two papers that won the IEEE CIS Outstanding Paper Award (for the IEEE TEC papers of 2010 and 2012).


Ernesto Costa
He is Full Professor at the Department of Informatics Engineering of the University of Coimbra, where he concluded its B.Sc. in 1976. He received a 3rd Cycle Thesis in Computing Science from the University Pierre et Marie Curie (Paris, France) in 1981 and got a Ph.D. in Electronic Engineering (area of Computing Science) from the University of Coimbra (Coimbra, Portugal) in 1985.

His main research interests are in the areas of Evolutionary Computation,Artificial Life, Complex Systems, Machine Learning, Cognition and Computational Biology. He was co-founder of the Centre for Informatics and Systems of the University of Coimbra (CISUC), and its President between May 1998 and June 2000. He was also the founder of the Artificial Intelligence Group which he led until 2003, when he founded the new Evolutionary and Complex Systems group within CISUC.

 He participated in several projects, got several best paper awards. He  was the recipient of the 2009 EvoStar Award for Outstanding Contributions to the Field of Evolutionary Computation. He   organized several international scientific events and had published over 150 papers in books, journals and proceedings of conferences. Since December 2012 is a member of the General Council of the University of Coimbra, a governing board of the university. 


Generative and Developmental Systems (GDS)


As artificial systems continue to grow in size and complexity, the engineering traditions of rigid top-down design are reaching the limits of their applicability. In contrast, biological evolution is responsible for an apparently unbounded complexity and diversity of living organisms. The Generative and Developmental Systems (GDS) track seeks to unlock the full potential of in silico evolution as a design methodology that can scale up to systems of great complexity and meet our specifications with minimal manual programming effort. Major themes are genotype-phenotype maps, interactions between developmental processes and evolution, alternatives to the classic fitness function to drive the selection process, and success metrics that go beyond task-based benchmarks (e.g., generating/measuring complexity, evolvability, regularity, etc.).

The GDS track invites all papers addressing the challenges of scaling up evolution to life-like complexity, including, but not limited to, the areas of:

  1. artificial development, artificial embryogeny
  2. neural development, neuro-evolution
  3. evo-devo robotics, morphogenetic robotics
  4. evolution of evolvability
  5. gene regulatory networks
  6. grammar-based systems, generative systems, rewriting systems
  7. indirect mappings, compact encodings, novel representations
  8. morphogenetic engineering
  9. diversity preservation, novelty search
  10. competitive co-evolution (arms races)
  11. measures of evolved complexity (theoretical or practical)
  12. open-ended evolution


JB Mouret
He is currently an assistant professor in computer sciences and robotics at the university Pierre and Marie Curie (UPMC, in Paris, France), in which he is a member of the Institute for Intelligent Systems and Robotics (ISIR, CNRS UMR 7222). He holds an engineering degree in CS from EPITA (2004), a master degree in artificial Intelligence from the UPMC (2005), and a PhD from UPMC (2008). He won the best paper award of the GDS track in 2011 and received several best paper awards and nominations in other major evolutionary computation conferences. He is the recipient of an ERC grant (Resibots, 2015-2020) and of a French "young researcher" grant (Creadapt, 2012-2015). His main contributions to the GDS topic deal with the relationship between generative encoding and plastic neural-networks, multi-objective selective pressures for behavioral diversity, and the evolution of modular networks.

Sebastian Risi
He is an assistant professor at the IT University of Copenhagen where he co-directs the Robotics, Evolution and Art Lab (REAL). His interests include neuroevolution, evolutionary robotics and design automation. Risi has a PhD in computer science from the University of Central  Florida. He has won several best paper awards at GECCO and IJCNN for his work on adaptive systems and the HyperNEAT algorithm for evolving complex artificial neural networks. He’s also a co-founder of FinchBeak, a company that creates casual and educational social games enabled by GDS technology.


Genetic Programming (GP)


In genetic programming, evolutionary computation is to search for an algorithm or executable structure that solves 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
  • Applications: Classification, Control, Data mining, Regression, Semi-supervised, Policy search, Prediction, Streaming data, Design, Inductive Programming
  • Environments: Static, Dynamic, Interactive, Uncertain
  • Operators: Replacement, Selection, Variation
  • Performance: Surrogate functions, Multi-objective, Coevolutionary
  • Populations: Demes, Diversity, Niches
  • Programs: Decomposition, Modularity, Semantics, Simplification
  • Programming languages: Imperative, Declarative, Object-oriented, Functional
  • Representations: Cartesian, Grammatical, Graphs, Linear, Rules, Trees
  • Systems: Autonomous, Complex, Developmental, Gene regulation, Parallel, Self-organizing, Software

Genetic programming (GP), data mining, learning, complex systems, performance evaluation, control, grammatical evolution (GE), fitness, training set, test suite, selection operators, theoretical analysis, fitness landscapes, visualisation, regression, graphs, rules, software improvement, representation, information theory, tree GP, complex, optimisation, evolvability, machine learning, feature construction and selection, applications, variation operators (crossover, mutation, etc.), hyperheuristics and automatic algorithm creation, parameter tuning, prediction, applications, symbolic expression, linear GP, knowledge engineering, environment, decision making, uncertain environments, nonlinear models, unique applications, streaming data, human competitive, dynamic environments, parallel implementations, Cartesian genetic programming (CGP), GP in high performance computing (parallel, cloud, grid, cluster, GPU).


Alberto Moraglio
He is a Lecturer in Computer Science in the College of Engineering, Mathematics and Physical Sciences at the University of Exeter, UK. He has been active in bio-inspired computing and genetic programming research for the last 10 years with a substantial publication record in the area.  He is the founder of the Geometric Theory of Evolutionary Algorithms, which unifies Evolutionary Algorithms across representations and has been used for the principled design of new successful search algorithms, including a new form of Genetic Programming based on semantics, and for their rigorous theoretical analysis. He was co-chair of the European Conference on Genetic Programming 2012 and 2013, and has regular tutorials at GECCO and IEEE CEC. He is a member of the editorial board for Genetic Programming and Evolvable Machines (Springer).

Krzysztof Krawiec
He is an Associate Professor in the Laboratory of Intelligent Decision Support Systems at Poznan University of Technology, Poznań, Poland. Dr. Krawiec co-­chaired the European Conference on Genetic Programming in 2013 and 2014 and is an associate editor of Genetic Programming and Evolvable Machines journal. His primary research areas are genetic programming and coevolutionary algorithms, with applications in program synthesis, modeling, image analysis, and games. His work in the area of GP includes semantic GP, design of effective search operators, discovery of semantic modularity of programs, and exploitation of program execution traces for improving search performance (best paper award in GP track at GECCO’14). Within the coevolutionary algorithms, he studied problem decomposition using cooperative coevolution (with applications in machine learning and pattern recognition), learning game strategies for Othello, Go, and other games using competitive coevolutionary algorithms, and discovery of underlying objectives in test-based problems. 


Hot Off the Press


The HOP (Hot Off the Press) track offers authors of recent papers the opportunity to present their work to the GECCO community. We invite researchers to submit summaries of their own work recently published in top-tier conferences and journals. Contributions are selected based on quality and estimated interest to the GECCO community.

Acceptance criteria:

  • The HOP paper must be of interest to the GECCO community. Significant applications as well as theoretical papers are welcome.
  • The HOP paper must not have been published in its final form earlier than 2014.
  • The HOP paper must have been accepted for publication in a well-respected journal or a well-respected international conference.
  • The core of the HOP paper must not have been presented at a GECCO conference before.


The abstract of the talk will be published in the conference program booklet.


The submission must contain an abstract of the paper plus a short explanation (less than a page) in which the authors briefly explain the relevance of the HOP paper to the GECCO community. For the review, the full HOP paper must be made available in some form on request.
Submissions are made through the usual submission site of GECCO 2015, at, selecting category "Hot Off the Press" as submission form.
Feel free to contact the general chair or the editor in chief for questions regarding the HOP track.


Integrative Genetic and Evolutionary Computation (IGEC)


GECCO has traditionally been a collection of mini-conferences, so authors have to choose a particular track to submit to. While this works fine for the majority of papers, some authors struggled to choose a track. This is why GECCO has introduced the track on "Integrative Genetic and Evolutionary Computation (IGEC)".


This track welcomes all papers that the authors feel do not fit into a particular track or that cross multiple tracks. Topics include but are not limited to:

  • Research on combining multiple evolutionary algorithms, such as Genetic Algorithms, Evolutionary Programming, Evolution Strategies and others
  • Classification of evolutionary algorithms
  • Memetic Computing and Hybrid algorithms in general
  • Coevolution
  • Evolutionary game theory
  • Novel nature-inspired paradigms
  • Dynamic and stochastic environments
  • Evolutionary algorithms with expensive objective/constraint evaluations
  • Statistical analysis techniques for EAs
  • Evolutionary algorithm toolboxes


Julian. F. Miller
He has a BSc in Physics (Lond), a PhD in Nonlinear Mathematics (City) and a PGCLTHE (Bham) in Teaching.  He is a Reader in the Department of Electronics at the University of York. He has chaired or co-chaired fifteen international workshops, conferences and conference tracks in Genetic Programming (GP), Evolvable Hardware. He is a former associate editor of IEEE Transactions on Evolutionary Computation and an associate editor of the Journal of Genetic Programming and Evolvable Machines and Natural Computing. He is on the editorial board of the journals: Evolutionary Computation, International Journal of Unconventional Computing and Journal of Natural Computing Research.  He has publications in genetic programming, evolutionary computation, quantum computing, artificial life, evolvable hardware, computational development, and nonlinear mathematics. He is a highly cited author with over 5,400 citations and over 210 publications in related areas.  He has given ten tutorials on genetic programming and evolvable hardware at leading conferences in evolutionary computation. He received the prestigious EvoStar award in 2011 for outstanding contribution to the field of evolutionary computation. He is the inventor of a highly cited method of genetic programming known as Cartesian Genetic Programming and edited the first book on the subject in 2011.

Paweł Widera
He is a postdoctoral researcher at the School of Computing Science at Newcastle University, UK. He holds a B.Sc. and M.Sc. in Computing Science from Poznań University of Technology, Poland and earned his Ph.D. in Computing Science from the University of Nottingham, UK. His work on genetic programming based design of energy functions for protein structure prediction has won a 1st prize (gold medal) at the 7th HUMIES awards for human-competitive results
produced by genetic end evolutionary computation held at GECCO 2010. His research interests include evolutionary computing, optimisation, complex systems and interactive data visualisation.


Parallel Evolutionary Systems (PES)


Parallel or distributed computing systems have gone a long way from specialized big-scale computer systems to have a place in our desktop and even our pocket, with smartphones boasting several cores which can, in fact, run concurrent and parallel systems. They have also moved from being permanent, physical and synchronized systems to ad hoc, temporal and virtual (cloud) and asynchronous and finally from something available to just a few they have become nowadays ubiquitous.
Adaptation of evolutionary algorithms of any kind to these environments presents unique challenges from many points of views: from the purely theoretical that studies the influence of different types of communication among populations, to the practical that intends to predict the performance of the parallel system or apply it to a particular problem.
This track in GECCO aims at fostering the cross-fertilization of knowledge between evolutionary algorithms, or metaheuristics in general, and parallel, distributed and concurrent computing. Working in two domains of research can be hard, but the cross-fertilization might be fruitful. Knowledge about parallel computing helps in creating parallel algorithms for clouds, multi-core or GPU architectures. However, this also implies the need for a careful definition of proper benchmarks, software tools, and metrics to measure the behavior of algorithms in a meaningful way. In concrete, a conceptual separation between physical parallelism and decentralized algorithms (whether implemented in parallel or not) is needed to better analyze the resulting algorithms.
This track is expected to collect contributions, from the theory through the implementation, to the application of techniques born from the crossover with metaheuristics of the traditional research fields in parallel computing. Articles are solicited, that describe significant and methodologically well-founded contributions to problem solving, aimed at maximizing both efficiency and accuracy.
This track includes (but is not limited to) topics concerning the design, implementation, and application of parallel evolutionary algorithms, as well as metaheuristics in general: ACO, PSO, VNS, SS, SA, EDAs, TS, ES, GP, GRASP, etc. As an indication, contributions are welcomed in the following areas:

  • Parallel/distributed/concurrent evolutionary, memetic, multiobjective, dynamic algorithms and  metaheuristics.
  • Parallel/distributed/concurrent (PDC) computing models.
  • Hardware realizations of these models
  • PDC realizations: cloud, P2P, browser-based, socket-based, mobile.
  • Algorithms and tools for helping in designing new parallel algorithms
  • PDC software frameworks/libraries
  • PDC test benchmarks
  • Performance evaluation.
  • Theory of PDC evolutionary algorithms and metaheuristics.
  • Real-world applications.


Stefano Cagnoni
He graduated in Electronic Engineering at the University of Florence in 1988 where he has been a PhD student and a post-doc until 1997. In 1994 he was a visiting scientist at the Whitaker College Biomedical Imaging and Computation Laboratory at the Massachusetts Institute of Technology. Since 1997 he has been with the University of Parma, where he has been Associate Professor since 2004.

Recent research grants regard: co-management of a project funded by Italian Railway Network Society (RFI) aimed at developing an automatic inspection system for train pantographs; a "Marie Curie Initial Training Network" grant, for a four-year research training project in Medical Imaging using Bio-Inspired and Soft Computing; a grant from "Compagnia di S. Paolo" on "Bioinformatic and experimental dissection of the signalling pathways underlying dendritic spine function".

He has been Editor-in-chief of the "Journal of Artificial Evolution and Applications" from 2007 to 2010. Member of the Editorial Board of the journals "Evolutionary Computation" and "Genetic Programming and Evolvable Machines" . Since 1999, he has been chair of EvoIASP, an event dedicated to evolutionary computation for image analysis and signal processing, now a track of the EvoApplications conference. Since 2005, he has co-chaired the MedGEC workshop at GECCO. Co-editor of special issues of journals dedicated to Evolutionary Computation for Image Analysis and Signal Processing. Reviewer for several international journals and is member of the program committees of several conferences.

He has been awarded the "Evostar 2009 Award", in recognition of the most outstanding contribution to Evolutionary Computation.


JJ Merelo
He is professor at the university of Granada, where he studied and finished his PhD. Besides professoring, he dables in writing novels in time he manages to free. He is a supporter of free software and open science and his main interests are distributed evolutionary computation and computational intelligence in games.



Real World Applications (RWA)


The 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 a rich and diverse set of fields, such as: Engineering and Technological Sciences, Mathematical Sciences, Numerical and Computational Sciences, Physical Sciences, Cosmological Sciences, Environmental Sciences, Geophysical Sciences, Oceanographic Sciences, Chemical Sciences, Biological Sciences, Atmospheric Sciences, Aerospace Sciences, Social Sciences and Economics; into a single event where the major interest is on applications including but not limited to:

  • Papers that describe advances in the field of EC for implementation purposes, including scalability for solution quality, scalability for algorithm complexity, and implementation in industrial packages like Matlab, Mathematica, and R.
  • Papers that describe EC systems that use modern computing paradigms in real-world applications, such as distributed and parallel computing (cloud, Mapreduce / Hadoop, grid, GPGPU, etc.), ubiquitous computing or cyber-physical systems.
  • Papers that present rigorous comparisons across techniques in a real-world application.
  • Papers that present new applications of EC to real-world problems.

All contributions should be original research papers demonstrating the relevance and applicability of EC within a real-world problem. The papers with a multidisciplinary interaction are welcomed, and it is desirable that those papers are presented and written in a way that other researchers can grasp the main results, techniques, and their potential applications. In summary, the real-world applications track is open to all domains including all industries (e.g. automobile, bio-tech, chemistry, defense, finance, oil and gas, telecommunications, etc.) and functional areas including all functions of relevance to real-world problems (e.g. logistics, scheduling, business, management, timetabling, design, data mining, process control, predictive modeling, etc.) as well as more technical and scientific disciplines (e.g. pattern recognition, computer vision, robotics, image processing, control, electrical and electronics, mechanics, etc.).


Emma Hart
Prof. Hart received her PhD from the University of Edinburgh in the field of Artificial Immune Systems in 2002. She has been active in the field of Evolutionary Computing and Optimisation for over 15 years, and was one of the early pioneers in the field of Hyper-Heuristics in the early 2000s. She has worked in a number of application domains, including scheduling, packing and routing, where her work has often been informed by interactions with companies. She currently leads the Centre for Visualisation, Algorithms and Evolving Systems at Edinburgh Napier University: the Centre encourages consultancy and innovation work in addition to conducting leading research in the field. She is an Associate Editor of Evolutionary Computation, and has authored over 100 publications. Her current interests lie in optimisation methods that continuously learn and improve as a result of being exposed to increasing numbers of problems.

Leonardo Trujillo
He is a research professor at the Instituto Tecnológico de Tijuana (ITT) in Mexico, his primary research areas are evolutionary computation and genetic programming, computer vision and pattern recognition. Dr. Trujillo has an engineering degree in Electronics and a masters in Computer Science from ITT, and a doctorate in Computer Science from the CICESE research center in Mexico. He is a level 1 member of the National System of Researchers from the National Science Council (CONACYT) in Mexico, publishing over a dozen journal papers and over 40 conference papers, while receiving several best paper awards from the leading conferences in the field. Currently, he is developing three collaborative research projects, respectively they focus on developing new GP-based methodologies for pattern recognition, analyzing and classifying EEG signals, and optimizing the planning of emergency response systems, with funding from CONACYT, the National System of Technological Institutes in Mexico, and the European Commission.


Search-Based Software Engineering and Self-* Search (SBSE-SS)


Search-Based Software Engineering (SBSE) is the application of search algorithms to the solution of software engineering tasks. We invite papers that address problems in the software engineering domain through the use of heuristic search techniques. We particularly encourage papers demonstrating novel search strategies or the application of SBSE techniques to new problems in software engineering.
Self-* search techniques incorporate ideas from adaptation and machine learning. The goal is to reduce the role of the human expert in the process of designing search algorithms, and to produce more generally applicable and robust methods. This will contribute to the long-standing challenge of self-adaptive software systems.

While empirical results are important, papers that do not contain results - but instead present new approaches, concepts, or theory - are also very welcome.
As an indication of the wide scope of the field, search techniques include, but are not limited to:

  • Evolutionary Computation
  • Ant Colony Optimization
  • Particle Swarm Optimization
  • Estimation of Distribution Algorithms
  • Simulated Annealing
  • Tabu Search
  • Iterated Local Search
  • Variable Neighbourhood Search
  • Hybrid and Memetic Algorithms
  • Hyper-heuristics
  • Adaptive Operator Selection
  • Adaptive Memetic Algorithms
  • Adaptive and Self-adaptive Parameter Control
  • Automatic Algorithm configuration and Parameter Tuning
  • Reactive search and Intelligent Optimization

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

  • Project Management and Organization
  • Requirements Engineering
  • Developing Dynamic Service-Oriented Systems
  • Configuring Cloud-Based Architectures
  • Automatic Algorithm Selection and Configuration
  • Enabling Self-configuring/Self-healing/Self-optimizing Systems
  • Creating Recommendation Systems to Support Software Development
  • Software Security
  • System and Software Integration
  • Test Data Generation
  • Regression Testing Optimization
  • Network Design and Monitoring
  • Software Maintenance, Program Repair, Refactoring and Transformation
  • Automated Software Design and Hyper-Heuristics


Gabriela Ochoa
Gabriela is a Lecturer in Computing Science at the University of Stirling, Scotland. She holds a PhD in Computing Science and Artificial Intelligence from the University of Sussex, UK. Her research interests lie in the foundations and application of evolutionary algorithms and heuristic search methods, with emphasis on autonomous (self-*) search, hyper-heuristics, fitness landscape analysis, and applications to combinatorial optimisation, healthcare, and software engineering. She has published over 80 scholarly papers and serves various program committees. She is associate editor of Evolutionary Computation (MIT Press), was involved in founding the Self-* Search track in 2011, and served  as the tutorial chair at  GECCO in 2012, 2013. She proposed the first Cross-domain Heuristic Search Challenge (CHeSC 2011)  and is involved in organising  EvoCOP 2014, 2015, and FOGA 2015.


Marouane Kessentini
He is a tenure-track assistant professor at University of Michigan. He is the founder of the research group Search-based Software Engineering@Michigan. He holds a Ph.D. in Computer Science, University of Montreal (Canada), 2011. His research interests include the application of artificial intelligence techniques to software engineering, search-based software engineering, refactoring, software quality, model-driven engineering, and re-engineering. He has published around 50 papers in conferences, workshops, books, and journals including three best paper awards. He has served as program committee and organization member in several major conferences in software engineering and evolutionary algorithms. He was the co-chair of SBSE track at GECCO2014 and the founder of the North American Search Based Software Engineering Symposium (NasBASE2015).


Theory (THEORY)


The theory track welcomes all papers that address theoretical issues in the whole of evolutionary computation and allied sciences. So, in addition to Genetic Algorithms, Evolutionary Strategies, Genetic Programming and other traditional EC areas, we also very much welcome theory papers in Artificial Life, Ant Colony Optimization, Swarm Intelligence, Estimation of Distribution Algorithms, Generative and Developmental Systems, Evolutionary Machine Learning, Search Based Software Engineering, and more. The theory track considers submissions performing theoretical analyses or concerning theoretical aspects in the areas described above. Results can be proven with mathematical rigor or obtained via a thorough experimental investigation.

Topics include (but are not limited to):

  • analysis methods like drift analysis, fitness levels, Markov chains, ...
  • fitness landscapes and problem difficulty
  • population dynamics
  • representations and variation operators
  • runtime analysis and black-box complexity
  • self-adaptation
  • single- and multi-objective problems
  • statistical approaches
  • stochastic and dynamic environments.

Up-to-date information on the track is available at


Carola Doerr
She is a CNRS researcher at the Université Pierre et Marie Curie (Paris 6). She studied mathematics at Kiel University (Germany, Diploma in 2007) andcomputer science at the Max Planck Institute for Informatics and Saarland University (Germany, PhD in 2011). From Dec. 2007 to Nov. 2009, Carola Doerr has worked as a business consultant for McKinsey & Company, mainly in the area of network optimization, where she has used randomized search heuristics to compute more efficient network layouts and schedules. Before joining the CNRS she was a post-doc at the Université Diderot (Paris 7) and the Max Planck Institute for Informatics.

Carola Doerr's main research interest is in the theory of randomized algorithms, both in the design of efficient algorithms as well as in finding a suitable complexity theory for randomized search heuristics. Most of her papers are on black-box complexities, a theory-guided approach to explore the limitations of heuristic search algorithms. She has contributed to the field of evolutionary computation also through results on the runtime analysis of evolutionary algorithms and drift analysis, as well as through the development of search heuristics for solving geometric discrepancy problems.

Francisco Chicano
He is an associate professor in the Department of Languages and Computing Sciences of the University of Malaga, Spain. He studied Computer Science (2003) and PhD in Computer Science (2007) at University of Malaga, and Physics (2014) in the National Distance Education University. His research interests and publications include the landscapes theory of combinatorial optimization problems and the application of theoretical results to the design of new search algorithms and operators. He has served as Program Chair in the EvoCOP conference, as Track Chair in GECCO 2013 and as Guest Editor in Special Issues of Evolutionary Computation (MIT) and Journal of Systems and Software.
He is the author of more than 70 refereed publications, has 3 best paper awards and has served on more than 30 program committees.





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