Planned 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.
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, 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The ACO-SI Track welcomes submissions of original and unpublished work in all experimental and theoretical aspects of SI, including (but not limited to) the following areas: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
He is Professor of Parallel Computing and Complex Systems at the University of Leipzig, Germany. He received the Diploma degree in mathematics and the Dr. rer. nat. degree from the University of Hannover, Germany and the Professoral Habilitation degree from the University of Karlsruhe, Germany. He has worked at the University of Dortmund, Germany and the University of Hannover as a Visiting Professor of Computer Science. He was a Professor of Computer Science at the Catholic University of Eichstätt, Germany. His research interests include nature inspired algorithms, swarm intelligence, bioinformatics, and self-organised systems.
Xiaodong Li received his B.Sc. degree from Xidian University, Xi'an, China, and Ph.D. degree in information science from University of Otago, Dunedin, New Zealand, respectively. Currently, he is an Associate Professor at the School of Computer Science and Information Technology, RMIT University, Melbourne, Australia. His research interests include evolutionary computation, neural networks, complex systems, multiobjective optimization, and swarm intelligence. He serves as an Associate Editor of the IEEE Transactions on Evolutionary Computation, Swarm Intelligence (Springer), and International Journal of Swarm Intelligence Research. He is a founding member and currently a Vice-chair of IEEE CIS Task Force on Swarm Intelligence, and a Chair of IEEE CIS Task Force on Large Scale Global Optimization. He was the General Chair of SEAL'08, a Program Co-Chair AI'09, and a Program Co-Chair for IEEE CEC’2012. He is the recipient of 2013 ACM SIGEVO Impact Award.
AIS-BIO - Artificial Immune Systems and Biological and Medical Applications
This joint track solicits papers from two areas:
Artificial Immune Systems (AIS) is a diverse area of research that takes inspiration from the natural immune system 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. In turn, such models and simulations can act as a bridge between computer science and immunology, providing new insights for immunologists. Recent advances now also provide theoretical analysis into the performance and complexity of many of the common immune-inspired algorithms.
Biological and Biomedical Applications (BIO): The advent and ongoing development of evolutionary computation has made it possible to solve increasingly complex biological and biomedical 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 epitomise the kinds of problems to which evolutionary computation may be uniquely well suited to solving. The BIO aspect of the AIS-BIO track aims to explore the use of evolutionary computation techniques for biological and biomedical applications.
Within AIS, welcome submissions of original and unpublished work in all aspects of AIS, including (but not limited to) the following areas:
- Biological foundations of AIS
- Computational modelling and simulation of aspects of the immune system
- Applications of AIS algorithms to computational problems, e.g. in optimisation, classification, learning
- Application to real-world problems
- Novel algorithms and new approaches
- Benchmarking against other techniques
- Hybridisation with other techniques
- Empirical investigations into performance and complexity
- Theoretical aspects including:
- Algorithm performance
- Convergence analysis
- Mathematical modelling
Within BIO, welcome submissions of original and unpublished work in all evolutionary computation techniques (genetic algorithms, genetic programming, evolutionary strategies, evolutionary programming, particle swarm optimisation, ant colony optimisation, artificial immune systems, emovlutionary multi-objective optimisation, learning classifier systems, memetic computing, etc.) to biological and biomedical applications, including (but not limited to) the following areas:
- Biological data mining
- Biomarker detection
- Biomedical systems and drug design
- Dimensionality reduction, feature selection and construction
- 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
Prof. Hart received her PhD from the University of Edinburgh. She currently leads the Centre for Emergent Computing at Edinburgh Napier University where her research focuses on optimisation and continuous learning systems, with an emphasis applying methods from Artificial Immune Systems and HyperHeuristics. She has published extensively in the field of Artificial Immune Systems, with a particular interest in optimisation and self-organising systems such as swarm robotics. Her current interests relate to the development of optimisation algorithms that continuously learn through experience, and how collectives of algorithms can collaborate to form good problem solvers. She also has interests in more theoretical work relating to modelling the immune system to learn more about its computational properties. She is an Associate Editor of Evolutionary Computing, a member of the SIGEVO Executive Board and editor of the SIGEVO newsletter.
Mengjie Zhang is currently 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 evolutionary computation, particularly genetic programming, particle swarm optimisation and learning classifier systems with application areas of feature selection/construction and dimensionality reduction, computer vision and image processing, job shop scheduling, multi-objective optimisation, and classification with unbalanced and missing data. He is also interested in data mining, machine learning, and web information extraction. Prof Zhang has published over 350 research papers in refereed international journals and conferences in these areas.
He has been serving as an associated editor or editorial board member for seven international journals including IEEE Transactions on Evolutionary Computation, the Evolutionary Computation Journal (MIT Press) and Genetic Programming and Evolvable Machines (Springer), and as a reviewer of over 30 international journals. He has been a major chair for eight international conferences. He has also been serving as a steering committee member and a program committee member for over 80 international conferences including all major conferences in evolutionary computation. Since 2007, he has been listed as one of the top ten world genetic programming researchers by the GP bibliography (http://www.cs.bham.ac.uk/~wbl/biblio/gp-html/index.html).
He is the Tutorial Chair for GECCO 2014 and AIS-BIO Track Chair for GECCO 2016. Since 2012, he has been co-chairing IEEE CEC, SSCI, and EvoIASP/EvoApplications conferences (he has been involving major EC conferences such as GECCO, CEC, EvoStar, SEAL). Since 2014, he has been co-organising and co-chairing the special session on evolutionary feature selection and construction at IEEE CEC and SEAL.
Prof Zhang is the Chair of 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.
CO - Continuous Optimization (former ESEP)
The CO track is concerned with randomized search algorithms for optimization in continuous search spaces, thereby combining what was formerly the ESEP track with other tracks insofar they covered optimization in continuous search spaces, most notably the EDA track. The scope of the CO track explicitly includes, but is not limited to, stochastic methods like Evolution Strategies (ES), Evolutionary Programming (EP), continuous versions of Genetic Algorithms (GAs), Estimation-of-Distribution Algorithms (EDAs), Particle Swarm Optimization (PSO), Differential Evolution (DE), Markov Chain Monte Carlo (MCMC) and Cross-Entropy (CE) methods.
The CO 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 solving problems such as large-scale and budgeted optimization, handling of constraints, multi-modality, noise, uncertain and/or changing environments, and mixed-integer problems.
Youhei Akimoto 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.
Peter A. N. Bosman is a senior researcher in the Life Sciences research group at the Centrum Wiskunde & Informatica (CWI) (Centre for Mathematics and Computer Science) located in Amsterdam, the Netherlands. Peter was formerly affiliated with the Department of Information and Computing Sciences at Utrecht University, where also he obtained both his MSc and PhD degrees in Computer Science, more specifically on the design and application of estimation-of-distribution algorithms (EDAs). Peter is best known for his status of active researcher in the area of EDAs since its upcoming and has (co-)authored over 80 refereed publications, studying both algorithmic design aspects and real-world applications of evolutionary algorithms. At the GECCO conference, Peter has previously been track (co-)chair (EDA track, 2006, 2009), late-breaking-papers chair (2007), (co-)workshop organizer (OBUPM workshop, 2006; EvoDOP workshop, 2007; GreenGEC workshop, 2012-2014) and (co-)local chair (2013).
CS - Complex Systems (Artificial Life/Robotics/Evolvable Hardware/Generative and Developmental Systems)
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 models and generative and developmental systems (GDS); and the real-world complexity of physical systems, such as 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, developmental or generative processes, 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 robotics and evolvable hardware studies 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. 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.
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.
Risto Miikkulainen is a Professor of Computer Sciences at the University of Texas at Austin. He received an M.S. in Engineering from the Helsinki University of Technology, Finland, in 1986, and a Ph.D. in Computer Science from UCLA in 1990. His recent research focuses on methods for evolving neural networks and applying these methods to game playing, robotics, and intelligent control. He is an author of over 350 articles on neuroevolution, connectionist natural language processing, and the computational neuroscience of the visual cortex. He is an associate editor of IEEE Transactions on Computational Intelligence and AI in Games and Cognitive Systems Research, and a member of the Board of Governors of the International Neural Networks Society.
DETA - Digital Entertainment Technologies and Arts
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
He is an assistant professor at the Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB) of Politecnico di Milano, where he also received his Ph.D. in 2008.
His research interests include machine learning, evolutionary computation, and computational intelligence in games.
Since 2008, he has been organizing several scientific games-related competitions at major conferences including GECCO, CEC and CIG.
Julian Togelius is Associate Professor at the Center for Computer Games Research, IT University of Copenhagen, Denmark. He works on all aspects of computational intelligence and games and on selected topics in evolutionary computation and evolutionary reinforcement learning. His current main research directions involve search-based procedural content generation in games, game adaptation through player modelling, automatic game design, and fair and relevant benchmarking of game AI through competitions. He is a past chair of the IEEE CIS Technical Committee on Games, and an associate editor of IEEE Transactions on Computational Intelligence and Games. Togelius holds a BA from Lund University, an MSc from the University of Sussex, and a PhD from the University of Essex.
ECOM - Evolutionary Combinatorial Optimization and Metaheuristics
The ECOM track aims to provide a forum for the presentation and discussion of high-quality research on metaheuristics for combinatorial optimization problems. Challenging problems from a broad range of applications, including logistics, network design, bioinformatics, engineering and business have been tackled successfully with metaheuristic approaches. In many cases, the resulting algorithms represent the state-of-the-art for solving these problems. In addition to evolutionary algorithms, the class of metaheuristics includes prominent generic problem solving methods, such as tabu search, iterated local search, variable neighborhood search, memetic algorithms, simulated annealing, GRASP and ant colony optimization.
The ECOM track encourages original submissions on all aspects of evolutionary combinatorial optimization and metaheuristics, including, but not limited to:
- Applications of metaheuristics to combinatorial optimization problems
- 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 techniques (including exact methods)
- Constraint-handling techniques
- Hybrid methods, adaptive hybridization techniques and memetic computing
- Hyper-heuristics for combinatorial optimization problems
- Characteristics of problems and problem instances
Dr. López-Ibáñez is a lecturer in the Decision and Cognitive Sciences Research Centre at the Alliance Manchester Business School, University of Manchester, UK. 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 17 journal papers, 6 book chapters and 36 papers in peer-reviewed proceedings of international conferences 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 and the automatic configuration and design of stochastic optimization algorithms, for single and multi-objective problems. He is the lead developer and current maintainer of the irace software package for automatic algorithm configuration (http://iridia.ulb.ac.be/irace).
Holger H. Hoos is a Professor of Computer Science and a Faculty Associate at the Peter Wall Institute for Advanced Studies at the University of British Columbia (Canada). His main research interests span empirical algorithmics, artificial intelligence, bioinformatics and computer music. He is known for his work on the automated design of high-performance algorithms and on stochastic local search methods. Holger is a co-author of the book "Stochastic Local Search: Foundations and Applications", and his research has been published in numerous book chapters, journals, and at major conferences in artificial intelligence, operations research, molecular biology and computer music. Holger was elected a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) in 2015 and won two prestigious IJCAI/JAIR best paper prizes in 2009 and 2010. He is a past president of the Canadian Artificial Intelligence Association / Association pour l'intelligence artificielle au Canada (CAIAC) and Associate Editor of the Journal of Artificial Intelligence Research (JAIR). Recently, his group has helped UBC to produce better exam timetables, Actenum Inc. to increase production efficiency in the oil and gas industry, and IBM to improve their CPLEX optimisation software, which is used by 50% of the world's largest companies and thousands of universities.
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 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 obtained a Bsc (Hons) in Computer Science from Monash University in 2001, an MSc degree in Computer Science from the University of Erlangen-Nuremberg in 2003, and a PhD in Bioinformatics from the University of Manchester in 2006. From 2007 to 2011, she held an MRC Special Training Fellowship at the University of Manchester, and she is now a Lecturer in the Decision and Cognitive Sciences Group at the Manchester Business School. 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.
Faustino Gomez is CEO at NNAISENSE SA, and former senior researcher at the Swiss AI lab IDSIA where he worked from 2004 to 2014. He received a BA in Geography from Clark University in 1991, and a PhD in Computer Science from the University of Texas at Austin in 2003. His research has focused primarily on using artificial evolution to automatically design neural network solutions to reinforcement learning tasks where it is often too difficult to design effective controllers by conventional engineering methods. He is also interested in studying techniques for making evolved controllers robust so that they can successfully make the transition from simulation to the real world, and therefore actually be useful in industry.
EMO - Evolutionary Multiobjective Optimization
In many real-world applications one is faced with the problem that several objective functions have to be optimized simultaneously, leading to a multi-objective optimization problem (MOP). In the recent past, bio-inspired evolutionary methods specialized for generating trade-off solutions of MOPs — Evolutionary Multiobjective Optimization (EMO) algorithms — have caught the interest of many researchers, and have become an important and very active research field. Reasons for this include the applicability of these randomized set-oriented methods to a wide range of MOPs, including black-box optimization tasks. In particular, no differentiability assumptions are required, and problem characteristics such as nonlinearity, multimodality or stochasticity can be handled, as well. Furthermore, preference information provided by a Decision Maker can be used, to the extent to which it is available, to deliver a finite-size approximation to the solution set (the so-called Pareto-optimal set), or some part of it, in a single optimization run.
The special session on Evolutionary Multiobjective Optimization (EMO) 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):
- Theoretical foundations
- Constraint handling techniques
- Preference handling techniques
- Handling of continuous, combinatorial or mixed-integer problems
- Local search techniques
- Hybrid approaches
- Stopping criteria
- Parallel EMO models
- Performance evaluation
- Test functions and benchmark problems
- Algorithm selection approaches
- Many-objective optimization
- Large scale optimization
- EMO algorithm implementation
- Real-world applications
Carlos M. Fonseca is an Associate Professor at the Department of Informatics Engineering of the University of Coimbra, Portugal, and the Head of the Evolutionary and Complex Systems (ECOS) group of the Centre for Informatics and Systems of the University of Coimbra (CISUC). He graduated in Electronic and Telecommunications Engineering from the University of Aveiro, Portugal, in 1991, and obtained a Ph.D. in Automatic Control and Systems Engineering from the University of Sheffield, U.K., in 1996. His research has been devoted mainly to evolutionary computation and multi-objective optimization, and his current research interests include multiobjective selection under preference uncertainty, performance evaluation, evolutionary algorithm dynamics, and engineering design optimization. He was a General co-Chair of the International Conference on Evolutionary Multi-Criterion Optimization (EMO) in 2003, 2009 and 2013, and a Technical co-Chair of the IEEE Congress on Evolutionary Computation (CEC) in 2000 and 2005. He is also a member of the Evolutionary Multi-Criterion Optimization Steering Committee.
Heike Trautmann is Professor of Information Systems and Statistics at the University of Münster, Germany (https://www.wi.uni-muenster.de/department/groups/statistik/people/heike-trautmann). 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) optimization - in particular preference incorporation, performance assessment and stopping criteria - as well as algorithm selection and benchmarking concepts. She was involved in organizing several related special sessions already, e.g. 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 as well as the EMO track at GECCO 2015.
GA - Genetic Algorithms
The Genetic Algorithm (GA) track has always been a large and important track at GECCO. We invite submissions to the GA track that present original work on all aspects of genetic algorithms, including, but not limited to:
- Practical and theoretical aspects of GAs
- Design of new GA operators including representations, fitness functions, initialization, termination, selection, recombination, and mutation
- Design of new and improved GAs
- Comparisons with other methods (e.g., empirical performance analysis)
- Hybrid approaches (e.g., memetic algorithms)
- Design of tailored GAs for new application areas
- Handling uncertainty (e.g., dynamic and stochastic problems, robustness)
- Metamodeling and surrogate assisted evolution
- Interactive GAs
- Co-evolutionary algorithms
- Parameter tuning and control (including adaptation and meta-GAs)
- Constraint Handling
- Diversity control (e.g., fitness sharing and crowding, automatic speciation, spatial models such as island/diffusion)
- Bilevel and multi-level optimization
- Ensemble based genetic algorithms
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.
Alberto Moraglio 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 evolutionary computation 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 and for their rigorous theoretical analysis. He was co-chair of the Theory Track and the Genetic Programming Track in past editions of GECCO, co-chair of the European Conference on Genetic Programming, and has regular tutorials at GECCO and IEEE CEC.
Bill Punch is currently an Associate Professor at the Computer Science and Engineering Department of Michigan State University. He has been the director of the MSU High Performance Computing Center, co-directs the Genetic Algorithms Research and Application Group (GARAGe) and is on the a member of the NSF Science and Technology Center, BEACON, at MSU. He has been involved in evolutionary computation work for more than 20 years, especially in the areas of genetic algorithms, genetic programming, and their practical applications. He is the co-chair of the Genetic Algorithms Track for GECCO 2016.
GP - Genetic Programming
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
- Synthesis: Programs, Algorithms, Circuits, Systems
- 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).
Krzysztof Krawiec is an Associate Professor in the Institute of Computing Science at Poznan University of Technology, Poland. His primary research areas are genetic programming, semantic genetic programming, and coevolutionary algorithms, with applications in program synthesis, modeling, pattern recognition, and games. Dr. Krawiec co-chaired the European Conference on Genetic Programming in 2013 and 2014, GP track at GECCO'16, and is an associate editor of Genetic Programming and Evolvable Machines journal.
Zdeněk Vašíček is an Assistant Professor and member of Evolvable Hardware Group at Faculty of Information Technology, Brno University of Technology, Czech Republic. His primary research is focused on applications of evolutionary approaches (mainly genetic programming) in areas related to the optimization and synthesis of digital circuits. He (co) authored over 40 papers mainly on evolutionary circuit design and evolvable hardware in FPGAs. His work was awarded with Silver (2011) and Gold (2015) medal at HUMIES.
IGEC - Integrative Genetic and Evolutionary Computation
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, perhaps because they feel their work has relevance across many tracks. This is why GECCO 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
- Evolutionary game theory
- Novel nature-inspired paradigms and heuristics
- Metaheuristic search in dynamic and stochastic environments
- Metaheuristic search with expensive objective/constraint evaluations
- Statistical analysis techniques for Metaheuristics
- Hybrid and Evolutionary algorithm toolboxes
Thomas Jansen is Senior Lecturer at the Department of Computer Science at Aberystwyth University, Wales, UK (since January 2013). He studied Computer Science at the University of Dortmund, Germany, and received his diploma (1996, summa cum laude) and Ph.D. (2000, summa cum laude) there. From September 2001 to August 2002 he stayed as a Post-Doc at Kenneth De Jong's EClab at the George Mason University in Fairfax, VA. He was Junior professor for Computational Intelligence from September 2002 to February 2009 at the Technical University Dortmund. From March 2009 to December 2012 he was Stokes Lecturer at the Department of Computer Science at the University College Cork, Ireland. He has published 22 journal papers, 45 conference papers, contributed seven book chapters and authored one book on evolutionary algorithm theory. His research is centred around design and theoretical analysis of artificial immune systems, evolutionary algorithms and other randomised search heuristics. He is associate editor of Evolutionary Computation (MIT Press) and Artificial Intelligence (Elsevier), member of the steering committee of the Theory of Randomised Search Heuristics workshop series, co-track chair of the Genetic Algorithm track of GECCO 2013 and 2014, was program chair at PPSN 2008, co-organised FOGA 2009 and FOGA 2015, organised a workshop on Bridging Theory and Practice (PPSN 2008), two GECCO workshops on Evolutionary Computation Techniques for Constraint Handling (2010 and 2011) and Dagstuhl workshops on Theory of Evolutionary Computation (2004 and 2006) and on Artificial Immune Systems (2011).
Jim Smith is Professor of Interactive Artificial Intelligence at University of the West of England, from where he received his PhD. in 1998. He has been researching metaheuristics since 1994, publishing over 100 papers and authoring a best-selling text book on Evolutionary Computation. His research interests include; the theory and application of adaptive and self adaptive evolutionary and memetic systems; hybrid algorithms; and the use of interactive artificial intelligence in optimisation and machine learning.
He is a member of the editorial board of the journals including Evolutionary Computation, Applied Soft Computing and Memetic Computing. He was track chair for the Genetic Algorithms track of GECCO 2011, and was programme chair for Parallel Problem Solving from Nature (PPSN) in 2003 and 2014.
PES - Parallel Evolutionary Systems
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.
Simone Ludwig is currently an Associate Professor of Computer Science at North Dakota State University (NDSU), USA. Prior to joining NDSU, she worked at the University of Saskatchewan (Canada), Concordia University (Canada), Cardiff University (UK) and Brunel University (UK). She received her PhD degree and MSc degree with distinction from Brunel University (UK), in 2004 and 2000, respectively. Her research interests include swarm intelligence, evolutionary computation, parallelization of swarm intelligence and evolutionary computation approaches, and fuzzy reasoning. With regards to the PES track, she and her research team have parallelized PSO, GA, GSO, GP, and fuzzy-clustering algorithms using MapReduce.
JJ Merelo is professor at the university of Granada. He has been involved in evolutionary computation for 20 years and not missed a PPSN since 2000, including the organisation of PPSN 2002 in Granada. He's the author of Algorithm::Evolutionary, a Perl evolutionary computation library and has given tutorials in GECCO, PPSN and CEC conferences. He's also been plenary speaker in ECTA 2013 and IDC 2014.
RWA - Real World Applications
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.).
Boris Naujoks is a professor for Applied Mathematics at Cologne University of Applied Sciences (CUAS). He joint CUAs directly after he received his PhD from Dortmund Technical University in 2011. During his time in Dortmund, Boris worked as a research assistant in different projects and gained industrial experience working for different SMEs. Meanwhile, he enjoys the combination of teaching mathematics as well as computer science and exploring EC and CI techniques at the Campus Gummersbach of CUAS. He focused on multiobjective (evolutionary) optimization, in particular hypervolume based algorithms, and the (industrial) applicability of the explored methods.
Prof. Enrique Alba had his degree in engineering and PhD in Computer Science in 1992 and 1999, respectively, by the University of Málaga (Spain). He works as a Full Professor in this university with different teaching duties: data communications, distributed programing, software quality, and also evolutionary algorithms, bases for R+D+i and smart cities at graduate and master/doctoral programs. Prof. Alba leads an international team of researchers in the field of complex optimization/learning with applications in smart cities, bioinformatics, software engineering, telecoms, and others. In addition to the organization of international events (ACM GECCO, IEEE IPDPS-NIDISC, IEEE MSWiM, IEEE DS-RT, …) Prof. Alba has offered dozens postgraduate courses, multiple seminars in more than 30 international institutions, and has directed several research projects (7 with national funds, 5 in Europe, and numerous bilateral actions). Also, Prof. Alba has directed 7 projects for innovation and transference to the industry (OPTIMI, Tartessos, ACERINOX, ARELANCE, TUO, INDRA, AOP) and presently he also works as invited professor at INRIA, the Univ. of Luxembourg, and Univ. of Ostrava. He is editor in several international journals and book series of Springer-Verlag and Wiley, as well as he often reviews articles for more than 30 impact journals. He has published 87 articles in journals indexed by Thomson ISI, 17 articles in other journals, 40 papers in LNCS, and more than 250 refereed conferences. Besides that, Prof. Alba has published 11 books, 39 book chapters, and has merited 6 awards to his professional activities. Pr. Alba’s H index is 44, with more than 8900 cites to his work.
SBSE - Search-Based Software Engineering, including Self-* Search
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
- 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:
- Optimizing Functional and Non-Functional Software Properties (Genetic Improvement)
- Automated Software Design and Hyper-Heuristics
- Automatic Algorithm Selection and Configuration
- Test Data Generation
- Regression Testing Optimisation
- Software Maintenance, Program Repair, Refactoring and Transformation
- Enabling Self-configuring/Self-healing/Self-optimizing Systems
- Project Management and Organization
- Developing Dynamic Service-Oriented Systems
- Configuring Cloud-Based Architectures
- Requirements Engineering
- Software Security
- Creating Recommendation Systems to Support Software Development
- System and Software Integration
- Network Design and Monitoring
Gabriela Ochoa is a Senior 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 90 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 was chair of EvoCOP 2014, EvoCOP 2015, FOGA 2015, and id serving as program chair for PPSN 2016.
Simon is an assistant professor in the Software Engineering Research Lab at the Blekinge Institute of Technology in Sweden. His core research interests are search-based software engineering (SBSE), software testing, and the use of principled statistical methods for research in both these areas. Simon has been involved with the SBSE track at GECCO since 2007, and previously chaired the track in 2011. He has also served as both general and program co-chair for the International Symposium on Search-Based Software Engineering (SSBSE), and program co-chair of the International Workshop on Search-Based Software Testing (SBST).
THEORY - Theory
The theory track welcomes all papers that address theoretical issues in evolutionary computation (EC) and related areas. In particular, in addition to Genetic Algorithms, Evolutionary Strategies, Genetic Programming and other traditional EC areas, we also 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. 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 blackbox complexity
- single and multiobjective problems
- statistical approaches
- stochastic and dynamic environments.
Selected accepted papers from this track will be invited for a special issue in Algorithmica.
Dirk obtained his Diplom (Master's) degree in 2004 and his PhD in computer science in 2008 from the Technische Universitaet Dortmund, Germany, under the supervision of Prof. Ingo Wegener. He has held postdoc positions at the International Computer Science Institute in Berkeley, California, working in the Algorithms group led by Prof. Richard M. Karp and at the University of Birmingham, UK, working with Prof. Xin Yao. Since January 2012 he is a Lecturer at the University of Sheffield, UK.
His research focuses on the computational complexity of randomized search heuristics such as evolutionary algorithms and swarm intelligence algorithms like ant colony optimization and particle swarm optimization. He is an editorial board member of Evolutionary Computation and Natural Computing and receives funding from the EU's Future and Emerging Technologies scheme (SAGE project). He has more than 60 refereed publications in international journals and conferences, including 8 best paper awards at leading conferences, GECCO and PPSN. He has given 7 tutorials at ThRaSH, WCCI/CEC, GECCO, and PPSN.
Timo is a postdoctoral researcher at the Hasso Plattner Institute (HPI) in Potsdam, Germany. He finished his undergraduate in Computer Science at Kiel University, Germany in 2005 and his PhD in Computer Science at the University of Delaware in 2009. Timo worked as a researcher at the Max Planck Institute for Informatics and the Friedrich Schiller University in Jena before coming to the HPI.
While Timo’s PhD thesis was in the area of computability theory, he picked up an interest in the theory of evolutionary computation shortly after graduation and now works in both areas. Regarding evolutionary computation, Timo focuses on mathematical studies of the properties of randomized search heuristics, in particular runtime analysis and blackbox complexity.