Three days of presentations of the latest high-quality results in 13 separate and independent program tracks specializing in various aspects of genetic and evolutionary computation.
ACO-SI - Ant Colony Optimization and Swarm Intelligence
Description
Swarm Intelligence (SI) is the collective problem-solving behavior of groups of animals or artificial agents that results from the local interactions of the individuals with each other and with their environment. SI systems rely on certain key principles such as decentralization, stigmergy, 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.
Scope
The ACO-SI Track welcomes submissions of original and unpublished work in all experimental and theoretical aspects of SI, including (but not limited to) the following areas:
- Biological foundations
- Modeling and analysis of new approaches
- Hybrid schemes with other algorithms
- Multi-swarm and self-adaptive approaches
- Constraint-handling and penalty function approaches
- Combinations with local search techniques
- 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
Biographies
Roderich Gross
Roderich Gross is a Senior Lecturer in the Department of Automatic Control and Systems Engineering at the University of Sheffield. He received a Ph.D. degree in engineering science in 2007 from Université libre de Bruxelles in 2007, and was a JSPS Fellow (Tokyo Institute of Technology) and a Marie Curie Fellow (EPFL & Unilever). He has authored more than 70 publications in robotics and artificial intelligence. He has made contributions to the coordination of swarm and reconfigurable robots, and invented a machine learning method called Turing Learning. Dr Gross serves as the General Chair of DARS 2016, Editor of IROS 2015-17, and as an Associate Editor of Swarm Intelligence, IEEE Robotics and Automation Letters, and IEEE Computational Intelligence Magazine.
Andries Engelbrecht
Andries Engelbrecht received the Masters and PhD degrees in Computer Science from the University of Stellenbosch, South Africa, in 1994 and 1999 respectively. He is Professor in Computer Science at the University of Pretoria, and serves as Head of the department. He holds the position of South African Research Chair in Artificial Intelligence, and leads the Computational Intelligence Research Group. His research interests include swarm intelligence, evolutionary computation, neural networks, artificial immune systems, and the application of these paradigms to data mining, games, bioinformatics, finance, and difficult optimization problems. He has published over 300 papers in these fields and is author of two books, Computational Intelligence: An Introduction and Fundamentals of Computational Swarm Intelligence.
CS - Complex Systems (Artificial Life/Artificial Immune Systems/Generative and Developmental Systems/Evolutionary Robotics/Evolvable Hardware)
Description
This track invites all papers addressing the challenges of scaling evolution up to real-life complexity. This includes both the real-life complexity of biological systems, such as artificial life, artificial immune systems, and generative and developmental systems (GDS); and the real-world complexity of physical systems, such as evolutionary robotics and evolvable hardware.
Artificial life, Artificial Immune Systems, and Generative and Developmental Systems all take inspiration from studying living systems. In each field, there are generally two main complementary goals: to better understand living systems and to use this understanding to build artificial systems with properties similar to those of living systems, such as behavior, adaptability, learning, developmental or generative processes, evolvability, active perception, communication, self-organization and cognition. The track welcomes both theoretical and application-oriented studies in the above fields. The track also welcomes models of problem-solving through (social) agent interaction, emergence of collective phenomena and models of the dynamics of ecological interactions in an evolutionary context.
Evolutionary Robotics and Evolvable Hardware study the evolution of controllers, morphologies, sensors, and communication protocols that can be used to build systems that provide robust, adaptive and scalable solutions to the complexities introduced by working in real-world, physical environments. The track welcomes contributions addressing problems from control to morphology, from single robot to collective adaptive systems. Approaches to incorporating human users into the evolutionary search process are also welcome. Contributions are expected to deal explicitly with Evolutionary Computation, with experiments either in simulation or with real robots.
Biographies
Emma Hart
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. From January 2017, she will become Editor-in-Chief of Evolutionary Computing, She is also a member of the SIGEVO Executive Board and editor of the SIGEVO newsletter.
Sebastian Risi
Sebastian Risi is an Associate Professor at the IT University of Copenhagen where he is part of the Center for Computer Games Research and the Robotics, Evolution and Art Lab (REAL). His interests include computational intelligence in games, neuroevolution, evolutionary robotics and human computation. Risi completed his PhD in computer science from the University of Central Florida. He has won several best paper awards at GECCO, EvoMusArt, IJCNN, and the Continual Learning Workshop at NIPS for his work on adaptive systems, the HyperNEAT algorithm for evolving complex artificial neural networks, and music generation.
DETA - Digital Entertainment Technologies and Arts
Description
The intersection of culture, science and technology is attracting increasingly more public attention, with frequent exhibitions, competitions and industrial involvement worldwide.
The Digital Entertainment Technologies and Arts (DETA) track at GECCO, in its seventh edition in 2017, focusses on the key application fields of arts, music, and games from the perspective of evolutionary computation, biologically inspired techniques, and more generally computational intelligence.
We invite submissions describing original work involving the use of computational intelligence techniques in the creative arts, including design, games, and music. Works of a methodological, experimental, or theoretical nature will be considered.
Scope
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
Biographies
Ekart Aniko
Aniko Ekart is currently Head of Computer Science at Aston University, Birmingham, United Kingdom. She holds a PhD in Informatics from Eötvös Loránd University, Budapest, Hungary. Her research interests include the theory and application of evolutionary computation and genetic programming in particular. She has experience in real-world applications of a variety of computational intelligence and data mining methods, including visual art, logistics (engineering) and vascular health (medicine). She has been working on various European Union funded research projects, including Advanced predictive analysis based decision support engine for logistics (ADVANCE), Actions for Excellence in Smart Cyber-Physical Systems applications through exploitation of Big Data in the context of Production Control and Logistics (EXCELL) and INdividual Vascular SignaTure: A new machine learning tool to aid personalised management of risk for cardiovascular disease (INVeST).
Julian Togelius
Julian Togelius is an Associate Professor in the Department of Computer Science and Engineering at New York University, and co-director of the NYU Game Innovation Lab. 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 an author of a recent textbook on Procedural Content Generation in Games and an upcoming textbook on Artificial 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
Description
The ECOM track aims to provide a forum for the presentation and discussion of high-quality research on metaheuristics for combinatorial optimization problems. Challenging problems from a broad range of applications, including logistics, network design, bioinformatics, engineering and business have been tackled successfully with metaheuristic approaches. In many cases, the resulting algorithms represent the state-of-the-art for solving these problems. In addition to evolutionary algorithms, the class of metaheuristics includes prominent generic problem solving methods, such as tabu search, iterated local search, variable neighborhood search, memetic algorithms, simulated annealing, GRASP and ant colony optimization.
Scope
The ECOM track encourages original submissions on 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
Biographies
Sébastien Verel
Sébastien Verel is a professor in Computer Science at the Université du Littoral Côte d'Opale, Calais, France, and previously at the University of Nice Sophia-Antipolis, France, from 2006 to 2013. He received a PhD in computer science from the University of Nice Sophia-Antipolis, France, in 2005. His PhD work was related to fitness landscape analysis in combinatorial optimization. He was an invited researcher in DOLPHIN Team at INRIA Lille Nord Europe, France from 2009 to 2011. His research interests are in the theory of evolutionary computation, multiobjective optimization, adaptive search, and complex systems. A large part of his research is related to fitness landscape analysis. He co-authored of a number of scientific papers in international journals, book chapters, book on complex systems, and international conferences. He is also involved in the co-organization EC summer schools, workshops, a special issue on EMO at EJOR, as well as special sessions in indifferent international conferences.
Christian Blum
Christian Blum currently holds the permanent post of a senior research scientist at the Artificial Intelligence Research Institute (IIIA) of the Spanish National Research Council (CSIC) in Bellaterra, Spain. He obtained the PhD in Applied Sciences from the Université de Bruxelles in 2004. Besides topics in swarm intelligence, his research interests are mainly focused on the hybridization of metaheuristics with other techniques for optimization. He has (co-)authored more than 150 publications in international journals, books, and peer-reviewed conference proceedings. Apart from acting as area editor for the journal Computers & Operations Research (responsible for metaheuristics), he is also associate editor for journals such as Theoretical Computer Science and Natural Computing. Moreover, he is a co-founder of the workshop series on Hybrid Metaheuristics.
EML - Evolutionary Machine Learning
Description
The Evolutionary Machine Learning (EML) track at GECCO covers advances in the theory and application of evolutionary computation methods to Machine Learning (ML) problems. Evolutionary methods can tackle many different tasks within the ML context, including problems related to supervised, unsupervised, semi-supervised, and reinforcement learning, as well as emergent topics such as transfer learning and domain adaptation, deep learning, learning with a small number of examples, and learning with unbalanced data and missing data. The tasks range from classification, via clustering, regression, prediction to time series analysis.
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.
Scope
More concretely, topics of interest include but are not limited to:
- Main EML paradigms and algorithms
- Learning Classifier Systems (LCS) and evolutionary Rule-Based Systems
- Genetic Programming (GP) when applied to machine learning tasks (as opposed to function optimisation)
- Evolutionary ensembles, where evolution generates a set of learners which jointly solve problems
- Evolutionary transfer learning and domain adaptation
- Evolutionary deep learning and evolving deep structures
- Evolving neural networks or neuroevolution (when applied to ML tasks)
- Hyper-parameter tuning with evolutionary methods
- Evolutionary learning with a small number of examples, unbalanced data or missing data values
- Other genetic based or evolutionary machine learning paradigms and algorithms
- Theoretical and methodological advances
- Theoretical analysis of mechanisms and systems
- Identification and modelling of learning and scalability bounds
- Evolutionary computation techniques for Feature extraction, feature selection, and feature construction
- Connections and combinations with machine learning theory (e.g. PAC theory and VC dimension)
- Analysis of the evolved/learned models including visualisation
- Generalisation and overfitting
- Analysis and robustness in stochastic, noisy, or non-stationary environments
- More Effective and efficient algorithms
- Addressing significant problems such as representation, data sampling, scalability, search mechanisms, multi-objective learning, fitness evaluation, niching and encapsulation, initialisation and termination
- Applications of EML
- Data mining
- Bioinformatics and life sciences
- Computer vision, image processing and pattern recognition
- Dynamic environments, time series and sequence learning
- Robotics, engineering, hardware/software design, and control
- Cognitive systems and cognitive modelling
- Artificial Life
- Economic modelling
- Cyber security
- Platforms such as GPU
- Other kinds of real-world ML applications
Biographies
Will Browne
Will Browne received a BEng Mechanical Engineering, Honours degree from the University of Bath, UK in 1993, MSc in Energy (1994) and EngD (Engineering Doctorate scheme, 1999) University of Wales, Cardiff. After eight years lecturing in the Department of Cybernetics, University of Reading, UK, he was appointed to School of Engineering and Computer Science, Victoria University of Wellington, NZ in 2008. Associate Professor Browne's main area of research is Applied Cognitive Systems. This includes Learning Classifier Systems, Cognitive Robotics, and Modern Heuristics for industrial application. Blue skies research includes analogues of emotions, abstraction, memories, dissonance and machine consciousness. He is an Associate Editor for Neural Computing and Applications, and Applied Soft Computing. He has published over 100 academic papers, including in IEEE Transactions on Evolutionary Computation on scalable learning and two best paper awards in ACM Genetic and Evolutionary Computation Conference.
Yusuke Nojima
Yusuke Nojima received the B.S. and M.S. Degrees in mechanical engineering from Osaka Institute of Technology, Osaka, Japan, in 1999 and 2001, respectively, and the Ph.D. degree in system function science from Kobe University, Hyogo, Japan, in 2004. Since 2004, he has been with Osaka Prefecture University, Osaka, Japan, where he was a Research Associate and is currently an Associate Professor in Department of Computer Science and Intelligent Systems. His research interests include evolutionary machine learning, evolutionary fuzzy systems, and evolutionary multiobjective optimization. He was a guest editor for several special issues in international journals. He is currently a task force chair on Evolutionary Fuzzy Systems in Fuzzy Systems Technical Committee of IEEE Computational Intelligence Society. He is an associate editor of IEEE Computational Intelligence Magazine.
EMO - Evolutionary Multiobjective Optimization
Description
In many real-world applications, several objective functions have to be optimized simultaneously, leading to a multi-objective optimization problem (MOP) for which an ideal solution seldom exists. Rather, MOPs typically admit multiple compromise solutions representing different trade-offs among the objectives. Due to their applicability to a wide range of MOPs, including black-box optimization problems, evolutionary algorithms for multiobjective optimization have given rise to an important and very active research area, known as Evolutionary Multiobjective Optimization (EMO). No continuity or differentiability assumptions are required by EMO algorithms, and problem characteristics such as nonlinearity, multimodality and stochasticity can be handled as well. Furthermore, preference information provided by a decision maker can be used to deliver a finite-size approximation to the solution set (the so-called Pareto-optimal set) in a single optimization run.
Scope
The Evolutionary Multiobjective Optimization Track is intended to bring together researchers working in this and related areas to discuss all aspects of EMO development and deployment, including (but not limited to):
- Theoretical foundations
- Preference articulation
- Constraint handling
- Handling of continuous, combinatorial or mixed-integer problems
- Stopping criteria
- Hybridization
- Performance evaluation
- Test functions and benchmarking
- Algorithm selection and configuration
- Visualization
- Interactive optimization
- Uncertainty handling
- Many-objective optimization
- Large-scale optimization
- Expensive function evaluations
- Parallel models
- Implementation aspects
- Real-world applications
Biographies
Tea Tušar
Tea Tušar is a research fellow at the Department of Intelligent Systems of the Jožef Stefan Institute in Ljubljana, Slovenia. She received the BSc degree in Applied Mathematics and the MSc degree in Computer and Information Science from the University of Ljubljana. She was awarded the PhD degree in Information and Communication Technologies by the Jožef Stefan International Postgraduate School for her work on visualizing solution sets in multiobjective optimization. She has recently completed a one-year postdoctoral fellowship at Inria Lille in France where she worked on benchmarking multiobjective optimizers. Her research interests include evolutionary algorithms for singleobjective and multiobjective optimization with emphasis on visualizing and benchmarking their results and applying them to real-world problems.
She was involved in the organization of a number of workshops at previous GECCOs (BBOB, VizGEC, Women@GECCO and Student Workshop), she proposed and organized the Job Market at GECCO 2017 and held a tutorial on Visualization in Multiobjective Optimization at GECCO 2016.
Qingfu Zhang
Qingfu Zhang is a Professor at the Department of Computer Science, City University of Hong Kong. His main research interests include evolutionary computation, optimization, neural networks, data analysis, and their applications. He is currently leading the Metaheuristic Optimization Research (MOP) Group in City University of Hong Kong. Professor Zhang is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the IEEE Transactions Cybernetics. He was awarded the 2010 IEEE Transactions on Evolutionary Computation Outstanding Paper Award. He is on the list of the Thomson Reuters 2016 and 2017 highly cited researchers in computer science. He is an IEEE fellow.
ENUM - Evolutionary Numerical Optimization
Description
The ENUM track (Evolutionary NUMerical optimization) is concerned with randomized search algorithms and continuous search spaces. The track is replacing the CO track, which combined the ESEP track with tracks covering optimization in continuous search spaces, most notably the EDA track. The scope of the ENUM track includes, but is not limited to, stochastic methods like Cross-Entropy (CE) methods, Differential Evolution (DE), continuous versions of Genetic Algorithms (GAs), Estimation-of-Distribution Algorithms (EDAs), Evolution Strategies (ES), Evolutionary Programming (EP), Markov Chain Monte Carlo methods (MCMC), and Particle Swarm Optimization (PSO).
Scope
The ENUM track invites submissions that present original work regarding theoretical analysis, algorithmic design, and experimental validation of algorithms for optimization in continuous domains, including work on large-scale and budgeted optimization, handling of constraints, multi-modality, noise, uncertain and/or changing environments, and mixed-integer problems. Work that advances experimental methodology and benchmarking, problem and search space analysis is also encouraged.
Biographies
Nikolaus Hansen
Nikolaus Hansen is a research scientist at INRIA, France. Educated in medicine and mathematics, he received a Ph.D. in civil engineering in 1998 from the Technical University Berlin under Ingo Rechenberg. Before he joined INRIA, he has been working in evolutionary computation, genomics and computational science at the Technical University Berlin, the InGene Institute of Genetic Medicine and the ETH Zurich. His main research interests are learning and adaptation in evolutionary computation and the development of algorithms applicable in practice. His best-known contribution to the field of evolutionary computation is the so-called Covariance Matrix Adaptation (CMA).
Jose Antonio Lozano
Prof. Lozano graduated in Mathematics (1991) and Computer Science (1992) at the University of the Basque Country (UPV/EHU) (Spain). In 1998 he got his PhD degree from the University of the Basque Country UPV/EHU, where he was awarded with the extraordinary prize for the best thesis in engineering. He got an assistant professor position at the University of the Basque Country (UPV/EHU) in 1993 and became a full professor at the Department of Computer Science and Artificial Intelligence in 2008.
Since 2005 he leads the Intelligent Systems Group (ISG) based in the Computer Science School (UPV/EHU). His research areas are evolutionary computation, machine learning and probabilistic graphical models and its application in the solution of real problems in biomedicine, industry or finance. He has published 4 books, more tan 100 scientific ISI journal articles and about 150 contributions to national and international conferences. These publications have received more than 8600 citations. Prof. Lozano is associate editor of IEEE Trans. on Evolutionary Computation and IEEE Trans. on Neural Network and Learning Systems among other prestigious journals.
GA - Genetic Algorithms
Description
The Genetic Algorithm (GA) track has always been a large and important track at GECCO. We invite submissions to the GA track that present original work on all aspects of genetic algorithms, including, but not limited to:
- Practical 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
- Model-Based Genetic Algorithms
As a large and diverse track, the GA track will be an excellent opportunity to present and discuss your research/application with a wide variety of experts and participants of GECCO.
Biographies
Dirk Thierens
Dirk Thierens is affiliated with the Department of Information and Computing Sciences at Utrecht University, the Netherlands, where he is teaching courses on Evolutionary Computation and on Computational Intelligence. He has been involved in genetic algorithm research since 1990. His current research interests are mainly focused on the design and application of model learning techniques to improve evolutionary search. Dirk is (has been) a member of the Editorial Board of the journals Evolutionary Computation, Evolutionary Intelligence, IEEE Transactions on Evolutionary Computation, and a member of the program committee of the major international conferences on evolutionary computation. He was elected member of the SIGEVO ACM board and contributed to the organization of several GECCO conferences: workshop co-chair (2003, 2004), track (co-)chair (2004, 2006, 2014), and Editor-in-Chief (2007).
Tian-Li Yu
Tian-Li Yu received the B.S. degree from the Dept. of Electrical Engineering of the National Taiwan University, and M.S. and Ph.D. in Computer Science of the University of Illinois at Urbana-Champaign. Currently, he is working as an associate professor in the National Taiwan University. He has been doing research in the field of Evolutional Computation for about 15 years. His main research interests are theoretical aspects concerning linkage learning in genetic algorithms as well as algorithm design/improvement.
GECH - General Evolutionary Computation and Hybrids
Description
General Evolutionary Computation and Hybrids is a new track that recognises that Evolutionary Algorithms are often used as part of a larger system, or together in synergy with other algorithms.
We welcome high quality papers on a range of topics that might not fit solely into any of the other track descriptions.
Scope
Areas of interest include the following - but the limit should be your creativity not ours!
- Combining different ways of creating or improving solutions
- such as co-evolution, neuro-evolution, memetic algorithms, and other hybrids.
- Combining EAs with Machine Learning Algorithms that learn a model of the search space
- such as surrogate-assisted optimisation of expensive fitness functions,
- Combining EAs with learning algorithms that attempt to learn how to control or co-ordinate a range of algorithms
- such as parameter tuning, parameter control, and self * approaches such as hyper-heuristics and self-adaptation,
- Novel nature-inspired paradigms
- Algorithms for Dynamic and stochastic environments
- Statistical analysis techniques for EAs
- Evolutionary algorithm toolboxes
Biographies
Jürgen Branke
Jürgen Branke is Professor of Operational Research and Systems at Warwick Business School, University of Warwick, UK. He has been an active researcher in the field of Evolutionary Computation for over 20 years, has published over 160 papers in peer-reviewed journals and conferences, resulting in an H-Index of 48 (Google Scholar). His main research interests include optimization under uncertainty, simulation-based optimization and multi-objective optimization. Jürgen is Area Editor for the Journal of Heuristics, and Associate Editor for the Evolutionary Computation Journal, IEEE Transactions on Evolutionary Computation, and the Journal on Multi-Criteria Decision Analysis.
Yaochu Jin
Yaochu Jin received the B.Sc., M.Sc., and Ph.D. degrees from Zhejiang University, China, in 1988, 1991, and 1996, respectively, and the Dr.-Ing. Degree from Ruhr University Bochum, Germany, in 2001. He is currently a Professor in Computational Intelligence and Head of the Nature Inspired Computing and Engineering (NICE) Group, Department of Computer Science, University of Surrey, UK. His research interests include computational intelligence, machine learning and computational neuroscience, in particular data-driven surrogate-assisted evolutionary optimization, multi-objective machine learning, evolutionary developmental systems, neural plasticity with applications to complex systems design, image processing, swarm robotics and bioinformatics.
He is the Editor-in-Chief of the IEEE Transactions on Cognitive and Developmental Systems, Co-Editor-in-Chief of Complex & Intelligent Systems, an Associate Editor of BioSystems, the IEEE Transactions on Cybernetics, IEEE Transactions on NanoBioscience and Soft Computing. He is also an Editorial Board Member of Evolutionary Computation. He is an Invited Plenary / Keynote Speaker at over 25 international conferences. He was the General Chair of IEEE CIBCB 2012 and IEEE SSCI 2016. He was the Vice President for Technical Activities and is an IEEE Distinguished Lecturer of the IEEE Computational Intelligence Society. He is the recipient of the 2014 and 2016 IEEE Computational Intelligence Magazine Outstanding Paper Award, the 2017 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, and the Best Paper Award of 2010 CIBCB, and the Best Student Paper Award of FOCI 2007 and IEEE CEC 2017.
Dr Jin is a Fellow of IEEE and Fellow of BCS.
GP - Genetic Programming
Description
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.
Scope
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
Keywords
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, classification, 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).
Biographies
Sara Silva
Sara Silva obtained a BSc and MSc in Informatics at the University of Lisbon, and a PhD (2008) in Informatics Engineering at the University of Coimbra, Portugal. Her main research interests are bio-inspired machine learning methods for data mining, like neural networks, genetic algorithms, and particularly genetic programming, which she has applied in several interdisciplinary projects ranging from remote sensing and forest science to epidemiology and medical informatics.
Sara Silva has around 60 peer-reviewed scientific publications, 10 of which distinguished with nominations and international awards. She is a member of the editorial board of the Genetic Programming and Evolvable Machines journal, has been program chair on several international conferences on Evolutionary Computation, and Editor-in-Chief of GECCO in 2015. She is the creator and developer of GPLAB - A Genetic Programming Toolbox for MATLAB.
Hitoshi Iba
Hitoshi Iba graduated from the Dept of Science of University of Tokyo in 1985 and received a Ph.D. degree from the Dept. of Engineering of University of Tokyo in 1990. Since then, he had been working in ETL (ElectroTechnical Lab). He joined Department of Electronic Engineering at the University of Tokyo in April, 1998. He is currently a Professor at the Department of Information and Communication Engineering, Graduate School of Information Science and Technology at the University of Tokyo. He is an associate editor of IEEE tr. on EC and Journal of Genetic Programming and Evolvable Machines (GPEM). His research interest includes: Evolutionary Computation, Genetic Programming, Bio-informatics, Foundation of Artificial Intelligence, Machine Learning, Robotics, and Vision.
RWA - Real World Applications
Description
Real-world applications (RWA) were originally the driving force for the development of evolutionary optimisation techniques; way before any theories were developed, EC was already applied successfully in the real world. In this spirit, 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 contributions from the diverse fields encountered in Engineering and Sciences, including Social Sciences and Economics, into a single event. The focus is on applications including but not limited to:
- Papers that describe advances in the field of EC for implementation purposes.
- Papers that present rigorous comparisons across techniques in real world applications.
- Papers that present novel uses of EC in the real world.
- 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. Papers covering multiple disciplines are welcome; we encourage the authors of such papers to write and present them in a way that allows researchers from other fields to grasp the main results, techniques, and their potential applications.
Scope
The real-world applications track is open to all domains and all industries.
Biographies
Anna I Esparcia-Alcázar
Anna I Esparcia-Alcázar holds a degree in Electrical Engineering from the Universitat Politècnica de València (UPV), Spain, and a PhD from the University of Glasgow, UK. She is a researcher at the PROS Centre of the UPV and an associate lecturer at the Control Department of the same university. She has ample experience both in industry and academia. For the past 10 years she has been actively involved in the organization of the two main conferences in the field of Evolutionary Computation, evostar and GECCO. She is Senior Member of the IEEE and Member of the ACM and was elect member of the Executive Committee of SIGEVO in the period 2009-2015.
In 2015 she was awarded the evo* Award for Outstanding Contribution to Evolutionary Computation in Europe.
Thomas Bartz-Beielstein
- Academic Background: Ph.D. (Dr. rer. nat.), TU Dortmund University, 2005, Computer Science.
- Professional Experience: Shareholder, Bartz & Bartz GmbH, Germany, 2014 – Present; Speaker, Research Center Computational Intelligence plus, Germany, 2012 – Present; Professor, Applied Mathematics, TH Köln, Germany, 2006 – Present.
- Professional Interest: Computational Intelligence; Simulation; Optimization; Statistical Analysis; Applied Mathematics.
- ACM Activities: Organizer of the GECCO Industrial Challenge, SIGEVO, 2011 – Present; Event Chair, Evolutionary Computation in Practice Track, SIGEVO, 2008 – Present; Tutorials Evolutionary Computation in Practice, SIGEVO, 2005 – 2013; GECCO Program Committee Member, Session Chair, SIGEVO, 2004 – Present.
- Membership and Offices in Related Organizations: Program Chair, International Conference Parallel Problem Solving from Nature, Jozef Stefan Institute, Slovenia, 2014; Program Chair, International Workshop on Hybrid Metaheuristics, TU Dortmund University, 2006; Member, Special Interest Group Computational Intelligence, VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik, 2008 – Present.
- Awards Received: Innovation Partner, State of North Rhine-Westphalia, Germany, 2013; One of the top 20 researchers in applied science by the Ministry of Innovation, Science and Research of the State of North Rhine-Westphalia, 2017.
SBSE - Search-Based Software Engineering
Description
Search-Based Software Engineering (SBSE) is the application of search algorithms and strategies to the solution of software engineering problems. Evolutionary computation is a foundation of SBSE, and since 2002 the SBSE track at GECCO has provided the unique opportunity to present SBSE research in the widest context of the evolutionary computation community. Last but not least, participating to the SBSE track and, more generally, to GECCO allow to be informed by advances in evolutionary computation, new cutting edge meta-heuristic ideas, novel search strategies, approaches and findings.
We invite papers that address problems in the software engineering domain through the use of heuristic search techniques. We particularly encourage papers demonstrating novel search strategies or the application of SBSE techniques to new problems in software engineering. Papers may also address the use of methods and techniques for improving the applicability and efficacy of search-based techniques when applied to software engineering problems. While empirical results are important, papers that do not contain strong empirical results - but instead present new sound approaches, concepts, or theory - are also very welcome. Moreover, this year for the first time we encourage the submission of both full papers and poster-only papers describing negative results as well as industrial reports on the practical use of search-based approaches. Moreover poster-only papers presenting frameworks/tools for search-based software engineering are also welcomed.
Scope
As an indication of the wide scope of the field, search techniques include, but are not limited to:
- Ant Colony Optimization
- Automatic Algorithm configuration and Parameter Tuning
- Estimation of Distribution Algorithms
- Evolutionary Computation
- Hybrid and Memetic Algorithms
- Hyper-heuristics
- Iterated Local Search
- Particle Swarm Optimization
- Simulated Annealing
- Tabu Search
- Variable Neighbourhood Search
The software engineering tasks to which they are applied are drawn from throughout the engineering lifecycle and include, but are not limited to:
- Automated Software Design and Hyper-Heuristics
- Automatic Algorithm Selection and Configuration
- Configuring Cloud-Based Architectures
- Creating Recommendation Systems to Support Life Cycle (Software Requirement, Design, Development, Evolution and Maintenance, etc.)
- Developing Dynamic Service-Oriented Systems
- Enabling Self-Configuring/Self-Healing/Self-Optimizing Systems
- Network Design and Monitoring
- Optimizing Functional and Non-Functional Software Properties (Genetic Improvement)
- Predictive Modelling for Software Engineering Tasks
- Project Management and Organization
- Regression Test Optimization
- Requirements Engineering
- Software Evolution
- Maintenance
- Program Repair
- Refactoring and Transformation
- Software Security
- System and Software Integration
- Test Data Generation
Special Section in the Information and Software Technology Journal for the Best Papers
The authors of best selected papers accepted in the SBSE track will be invited to submit an extended version of their paper for a special section in the Elsevier Journal of Information and Software Technology (IST).
Biographies
Federica Sarro
Federica is an Associate Professor at the Department of Computer Science, University College London, UK.
Her main research areas are Empirical Software Engineering and Search Based Software Engineering (SBSE), with a particular interest in predictive modelling for software engineering, software project management, and software quality. She has published over 60 scholarly papers and served more than 40 program committees over the last 6 years. She has also won four international awards for her research in these areas, including the HUMIES-GECCO award in 2016 for her recent work on “Multi-objective Effort Estimation”. She has been member of the Program Committee of the SBSE track at GECCO and of the International Symposium on Search-Based Software Engineering (SSBSE) since 2013. In 2015 she has been elected as member of the Steering Committee for SSBSE and she has also been Program Chair of SSBSE 2016 and of the SBSE track at GECCO 2017. Federica is on the editorial board of the GPEM Journal and Associate Editor of the IET Software Journal.
Giuliano Antoniol
Giuliano Antoniol is professor of Software Engineering in the Department of Computer and Software Engineering of the Polytechnique Montréal where he directs the SOCCER laboratory. He worked in private companies, research institutions and universities. In 2005 he was awarded the Canada Research Chair Tier I in Software Change and Evolution. He has served in the program, organization and steering committees of numerous IEEE and ACM sponsored international conferences and workshops. His research interest include software traceability, traceability recovery and maintenance, software evolution, empirical software engineering, search based software engineering, and software testing.
THEORY - Theory
Description
The theory track welcomes all papers performing theoretical analyses or concerning theoretical aspects in evolutionary computation and related areas. Results can be proven with mathematical rigor or obtained via a thorough experimental investigation.
In addition to traditional areas in evolutionary computation like Genetic and Evolutionary Algorithms, Evolutionary Strategies, and Genetic Programming we also highly welcome theoretical papers in Artificial Life, Ant Colony Optimization, Swarm Intelligence, Estimation of Distribution Algorithms, Generative and Developmental Systems, Evolutionary Machine Learning, Search Based Software Engineering, Population Genetics, and more.
Selected accepted papers from this track will be invited for a special issue in Algorithmica.
Scope
Topics include (but are not limited to):
* analytical methods like drift analysis, fitness levels, Markov chains, large deviation bounds,
* dynamic and static parameter choices,
* fitness landscapes and problem difficulty,
* population dynamics,
* problem representation,
* runtime analysis, black-box complexity, and alternative performance measures,
* single- and multi-objective problems,
* statistical approaches,
* stochastic and dynamic environments,
* variation and selection operators.
Biographies
Anne Auger
Anne Auger is a permanent researcher at the French National Institute for Research in Computer Science and Control (INRIA). She received her diploma (2001) and PhD (2004) in mathematics from the Paris VI University. Before to join INRIA, she worked for two years (2004-2006) at ETH in Zurich. Her main research interest is stochastic continuous optimization including theoretical aspects and algorithm designs. She is a member of ACM-SIGECO executive committee and of the editorial board of Evolutionary Computation. She has been organizing the biannual Dagstuhl seminar "Theory of Evolutionary Algorithms" in 2008 and 2010 and served as track chair for the theory and ES track in 2011, 2013 and 2014. Together with Benjamin Doerr, she is editor of the book "Theory of Randomized Search Heuristics".
Per Kristian Lehre
Per Kristian Lehre is a Senior Lecturer at the University of Birmingham, UK.
He received MSc and PhD degrees in Computer Science from the Norwegian University of Science and Technology (NTNU). After finishing his PhD in 2006, he held postdoctorial positions in the School of Computer Science at the University of Birmingham and at the Technical University of Denmark. From 2011, he was a Lecturer in the School of Computer Science at the University of Nottingham, until 2017, when he returned to Birmingham.
Dr Lehre's research interests are in theoretical aspects of nature-inspired search heuristics, in particular, runtime analysis of population-based evolutionary algorithms. His research has won several best paper awards, including at GECCO (2013, 2010, 2009, 2006), ICSTW (2008), and ISAAC (2014). He is editorial board member of Evolutionary Computation, and associate editor of IEEE Transactions on Evolutionary Computation. He was the coordinator of the successful 2M euro EU-funded project SAGE which brought together the theory of evolutionary computation and population genetics.