Planned Program Tracks
Optimization and Swarm Intelligence (ACO-SI)
Swarm Intelligence (SI) deals with natural and artificial systems
composed of a number of individuals that generate collective
behaviors using decentralized control and self-organization.
These behaviors emerge as the result of local interactions of the
individuals with each other and with their environment. The models
underlying SI algorithms are inspired by the behavior of social
insects such as ants, bees, and wasps, as well as by that of other
animal societies such as flocks of birds and fish schools. Ant
Colony Optimization (ACO) and Particle Swarm Optimization (PSO)
constitute two of the most popular SI algorithms with numerous
applications in science and engineering. Other prevailing approaches
include Honey Bee Optimization, Bacterial Foraging, Firefly
Optimization, as well as approaches based on specialized behaviors
of social insect communities.
The ACO-SI Track welcomes submissions of original and unpublished work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
* Combinations with local search techniques
* Constraint-handling and penalty function approaches
* Benchmarking and new empirical results
* Parallel/distributed implementations and applications
* Large-scale applications
* Applications in multi-objective, dynamic, and noisy problems
* Applications in continuous and discrete search spaces
* Multi-swarm and self-adaptive approaches
* Software and high-performance implementations
She is Professor of Computer Science in INSA de Lyon, France, and she received the Ph.D. degree in Computer Science from the University of Nice-Sophia Antipolis, France, in 1993. Her current research interests include Ant Colony Optimization, meta-heuristics, and Constraint Programming.
He studied Mathematics at
University of Patras, where he also received his Ph.D.
degree in 2005. His research is focused on Computational
Optimization with an emphasis on Swarm Intelligence and
Evolutionary Computation. He has co-authored 1 book and
more than 80 papers, while his work has received more
than 2000 citations. He currently serves as Assistant
Professor at University of Ioannina.
Technologies and Arts (DETA)
The arts, music, and games are key application fields for computational intelligence, evolutionary computation, and related techniques. This track explicitly focusses on these areas, strengthening a domain of high scientific, commercial, and cultural relevance. We invite submissions describing original work involving the use of computational intelligence in the creative arts, including design, games, and music. Works of a methodological, experimental, or theoretical nature will be considered.
Topics of interest include, but are not limited to:
* Aesthetic measurement / control
+ machine learning for predicting or controlling aesthetic preference
+ aesthetic measures for sound, photos, textures
+ non-realistic rendering, animations
+ content-based similarity or recommendation
+ user modelling
* 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
+ automated content generation
+ game AI
+ intelligent interactive narratives
+ learning and adaptation in games
+ player satisfaction
* 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 is an academic at Monash University where he is an Artificial Life researcher and generative artist working in electronic media. His interests include ecosystem simulation and agent-based modelling, artificial chemistry, self-assembling systems, the evolution of complexity, the history and philosophy of science and art, and the links that bind all fields together. His interactive software, animations, musical and other artistic works typically employ biologically-inspired generative systems and have been exhibited or performed internationally at museums, galleries and in science museums. Alan has a PhD in Computer Science (Monash University 1999), a Postgrad. Dip. in Animation and Interactive Media (RMIT 1995), Honours in Comp. Sci. (Monash University 1992) and a B.Sc. (App. Maths 1991).
He is a postdoctoral fellow at the Institut des Systèmes Complexes (ISC-PIF), CNRS, in Paris, France. Previously, he worked at Monash University in Melbourne, Australia, and the Memorial University in St. John's, Canada. Taras has written on artificial development, generative and ecosystemic art, computational creativity, machine learning, and synthetic biology. He has chaired the DevLeaNN workshop on developmental networks, the SynBioCCC workshop on synthetic biology, served as an organizer for EvoNet, and has exhibited his art internationally. He has his MSc and PhD in computer science (Concordia University, 2003, 2007) and a B.Sc. in pure mathematics (University of Toronto, 2000).
Evolutionary Combinatorial Optimization and Metaheuristics (ECOM)
The aim of this track is to provide a forum for high quality research on metaheuristics for combinatorial optimization problems. Plenty of hard problems in a wide variety of areas, including logistics, network design, bioinformatics, engineering, business, etc., have been tackled successfully with metaheuristic approaches. For many problems, the resulting algorithms are considered to be state-of-the-art. Metaheuristics include evolutionary algorithms, but also many other methods such as tabu search, iterated local search, variable neighborhood search, memetic algorithms, simulated annealing, GRASP, ant colony optimization and others.
Scope: Submission concerning applications or the theory of all kinds of metaheuristics for combinatorial optimization problems are encouraged. Topics include (but are not limited to):
* Applications of metaheuristics to combinatorial optimization problems, including multi-objective, stochastic and dynamic problems.
* In-depth experimental analysis and comparisons of metaheuristics
* Theoretical developments in combinatorial optimization and metaheuristics
* Engineering of metaheuristic algorithms and automatic configuration
* Hybrid metaheuristics, including memetic algorithms and matheuristics
* Learning and adaptation techniques that enhance metaheuristics
* Large-scale neighborhoods
* Search space analysis
* Insight into problem characteristics of problem classes
Keywords: stochastic local search, local search, variable neighborhood search, iterated local search, tabu search, simulated annealing, very large scale neighborhood search, search space analysis, hybrid metaheuristics, matheuristics, memetic algorithms, ant colony optimization, particle swarm optimization, scatter search, path relinking, GRASP, vehicle routing, cutting and packing, scheduling, timetabling, bioinformatics, transport optimization, routing, network design, algorithm engineering, representations, automatic configuration, parameter tuning, parameter adaptation
Manuel López Ibáñez:
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, Edinburgh, U.K., in 2009.
He is a Postdoctoral Researcher (Chargé de recherche) of the Belgian F.R.S.-FNRS at the Institut de Recherches Interdisciplinaires et de Développements en Intelligence Atificielle (IRIDIA), Université libre de Bruxelles, Brussels, Belgium. His current research interests are algorithm engineering, experimental analysis and automatic configuration of stochastic optimization algorithms, for single and multi-objective optimization.
He received the Diplom, M.S. degree, in business engineering from the Université Karlsruhe (TH), Karlsruhe, Germany in 1994, and the Ph.D. degree and the "Habilitation" in computer science both from the Computer Science Department of Technische Universität, Darmstadt, Germany in 1998 and 2004, respectively.
He is a Research Associate of the Belgian F.R.S.-FNRS working in the Institut de Recherches Interdisciplinaires et de Développements en Intelligence Atificielle (IRIDIA), Université libre de Bruxelles, Brussels, Belgium. He is author of the two books: Stochastic Local Search: Foundations and Applications(Morgan Kaufmann) and Ant Colony Optimization (MIT Press). He has published extensively in the wider area of metaheuristics (more than 150 peer-reviewed articles in journals, conference proceedings, or edited books). His research interests range from stochastic local search (SLS) algorithms, large scale experimental studies, automated design of algorithms, to SLS algorithms engineering.
Multiobjective optimization problems (MOPs) arise frequently in applications. They have several (two or more), normally conflicting, objectives that have to be satisfied at the same time. The Evolutionary Multiobjective Optimization (EMO) track calls for contributions describing the use of a range of metaheuristic methodologies (mainly but not limited to evolutionary algorithms) to solve MOPs, aiming to find good trade-off (or compromise) solutions.
The EMO track at GECCO aims at bringing together both experts and newcomers working in this area to discuss different issues including (but not limited to):
1. Real-world applications in engineering, business, computer science, biological sciences, scientific computation, etc.
2. New multiobjective optimization algorithms based on metaheuristics such as genetic algorithms, evolution strategies, scatter search, genetic programming, evolutionary programming, artificial immune systems, particle swarm optimization, ant colony optimization, etc.
3. Performance measures for EMO
4. Test functions and comparative studies of algorithms for EMO
5. Techniques to maintain diversity in an EMO context
6. Theoretical investigations of EMO
7. Dimensionality analysis (e.g., techniques to deal with a high number of objectives and/or decision variables)
8. Parallelization of EMO techniques
9. Hybrid approaches (e.g., combinations with mathematical programming techniques)
10. Local search in an EMO context (e.g., memetic algorithms for multiobjective optimization)
11. Multiobjective combinatorial optimization
12. Incorporation of preferences into EMO algorithms
13. Handling uncertainty and noise in an EMO context
14. Dynamic multiobjective optimization using EMO algorithms
15. Special representations and operators for EMO algorithms
16. Software architectures for development of EMO algorithms
17. Learning and intelligent mechanisms for EMO
18. Multi-level optimization using EMO algorithms
19. Many-objective optimization using EMO algorithms
20. Multi-criteria decision making and EMO techniques
21. Multiobjectivization studies
22. Set-based Multicriteria Decision Making (MCDM) approaches
applications of evolutionary multiobjective optimization, dynamic multiobjective optimization, expensive evaluation functions, interactive multiobjective methods, machine learning in multiobjective optimization, many-objective optimization, multi-level optimization problems, multiobjective problem decomposition, multiobjective algorithm comparison, multiobjective combinatorial problems, multiobjective constraint satisfaction, multiobjective online optimization, multiobjective operators, multiobjective performance metrics, multiobjective preferences management, multiobjective robust optimization, multiobjective solution encodings, multiobjective test functions, multiple criteria decision making techniques, niching, elitism and diversity techniques, parallel multiobjective algorithms, theoretical studies in multiobjective optimization, uncertainty and noisy multiobjective optimization
received his diploma in computer science from University of Dortmund, Germany in 2005 and his PhD (Dr. sc. ETH) from ETH Zurich, Switzerland in 2009. Afterwards, he held postdoctoral research positions in France at INRIA Saclay Ile-de-France (2009-2010) and at Ecole Polytechnique (2010-2011). Since November 2011, he has been a permanent researcher at INRIA Lille - Nord Europe, France. His research interests are focused on evolutionary multiobjective optimization (EMO), in particular on theoretical aspects of indicator-based search, algorithm design, and the benchmarking of (multiobjective) blackbox algorithms in general.
He studied Technical Computer Science in Dortmund and Kiel, Germany.
He received his diploma and Ph.D. from the Christian-Albrechts-University of Kiel in 2002 and 2006, respectively.
Currently, he is an associate professor at the School of Computer Science, University of Adelaide, Australia. In his work, he considers evolutionary computation methods in particular for combinatorial and multi-objective optimization problems. With Ken De Jong he organised FOGA 2013 in Adelaide and together with Carsten Witt he has written the textbook "Bioinspired Computation in Combinatorial Optimization - Algorithms and Their Computational Complexity" published by Springer.
In the field of Genetic Programming, evolutionary algorithms are used to automatically search for an algorithm or structure that solves a given problem. Various representations have been used in GP, such as tree-structures, linear sequences of code, graphs or 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 Genetic Programming (GP) track invites original submissions on all aspects of the evolutionary generation of computer programs or other variable sized structures for specified tasks. Topics include but are not limited to:
1. Theoretical developments
2. Empirical studies of GP performance and behavior
3. Novel algorithms, representations and/or operators
4. Hybrid architectures including GP components
5. Unconventional evolvable computation
6. Tree-based, graph-based, linear, grammar-based genetic programming
7. Probabilistic genetic programming
8. Parallel genetic programming
9. Real-world applications of genetic programming
10. Self-reproducing programs
11. Evolution of various classes of automata or machines (e.g. cellular automata, finite state machines, pushdown automata, Turing machines)
12. Object-oriented genetic programming
13. Fitness landscape analysis of genetic programming
14. Multi-objective genetic programming
15. Self-reproducing programs
classification, planning, evolutionary theory, parallelization, computer vision, animation, functional GP, financial application, GPU, dynamic systems, GP fitness landscape, symbolic regression, CGP and its applications, developmental GP and financial applications, modular GP and analysis, bloat, SAT, local search, grammatical evolution, coevolution, hierarchical GP, evolvability, applications (robot, financial, gene chip etc), constraints, grammar, data mining, visualization, art, biological applications, fitness landscapes.
holds a PhD from Eötvös Loránd University, Budapest, Hungary. She is a senior lecturer in Computer Science at Aston University, Birmingham, United Kingdom. Her research interests range from theory and application of evolutionary computation, and genetic programming in particular, to real-world applications of a variety of artificial intelligence and data mining methods. She is leading the Aston team on the ADVANCE "Advanced predictive analysis based decision support engine for logistics" European research project led by the Computer and Automation Research Institute of the Hungarian Academy of Sciences and in collaboration with the University of Gröningen, the Netherlands and industrial partners TTS, Italy and Palletways, UK. She serves on the Editorial Board of the Genetic Programming and Evolvable Machines (GPEM) and the Neural Computing and Applications (NCA) journals. She is also Postgraduate Programme Director in Computer Science at Aston.
is an Assistant Professor at the Department of Veterinary Sciences, University of Torino, where he leads the Computational Epidemiology Group. His research interests focus on artificial life, both as bio-inspired computational techniques and as modeling of biological phenomena principally using concepts and instruments of network science. Part of his research activity is carried out at the Molecular Biotechnolgy Center, where he is the principal investigator of the Complex Systems Unit, and at the Department of Computer Science, where he is member of the Applied Research on Computational Complex Systems Group.
Genetics-Based Machine Learning (GBML)
The Genetics-Based Machine Learning (GBML) track at GECCO covers all advances in theory and application of evolutionary computation methods to Machine Learning (ML) problems. ML presents an array of paradigms - unsupervised, semi-supervised, supervised, and reinforcement learning - which frame a wide range of clustering, classification, regression, prediction and control tasks.
The literature shows that evolutionary methods can tackle many different tasks within the ML context:
- addressing subproblems of ML e.g. feature selection and construction
- optimising parameters of other ML methods
- as learning methods for classification, regression or control tasks
- as meta-learners which adapt base learners
* evolving the structure and weights of neural networks
* evolving the data base and rule base in genetic fuzzy systems
* evolving ensembles of base learners
The global search performed by evolutionary methods can complement the local search of non-evolutionary methods and combinations of the two are particularly welcome.
Some of the main GBML subfields are:
* Learning Classifier Systems (LCS) are rule-based systems introduced by John Holland in the 1970s. LCSs are one of the most active and best-developed forms of GBML and we welcome all work on them.
* 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.
* Artificial Immune Systems (AIS).
* Evolving neural networks or Neuroevolution.
* Genetic Fuzzy Systems (GFS) which combine evolution and fuzzy logic.
In addition we encourage submissions including but not limited to the following:
1. Theoretical advances
* Theoretical analysis of mechanisms and systems
* Identification and modeling of learning and scalability bounds
* Connections and combinations with machine learning theory
* Analysis and robustness in stochastic, noisy, or non-stationary environments
* Complexity analysis in MDP and POMDP problems
* Efficient algorithms
2. Modification of algorithms and new algorithms
* Evolutionary rule learning, including but not limited to:
o Michigan style (SCS, NewBoole, EpiCS, ZCS, XCS, UCS...)
o Pittsburgh style (GABIL, GIL, COGIN, REGAL, GA-Miner, GALE, MOLCS, GAssist...)
o Anticipatory LCS (ACS, ACS2, XACS, YACS, MACS...)
o Iterative Rule Learning Approach (SIA, HIDER, NAX, BioHEL, ...)
* Artificial Immune Systems
* Genetic fuzzy systems
* Learning using evolutionary Estimation of Distribution Algorithms (EDAs)
* Evolution of Neural Networks
* Evolution of ensemble systems
* Other hybrids combining evolutionary techniques with other machine learning techniques
3. Issues in GBML
* Competent operator design and implementation
* Encapsulation and niching techniques
* Hierarchical architectures
* Default hierarchies
* Knowledge representations, extraction and inference
* Data sampling
* (Sub-)Structure (building block) identification and linkage learning
* Integration of other machine learning techniques
* Mechanisms to improve scalability
* Data mining
* Bioinformatics and life sciences
* Rapid application development frameworks for GBML
* Robotics, engineering, hardware/software design, and control
* Cognitive systems and cognitive modeling
* Dynamic environments, time series and sequence learning
* Artificial Life
* Adaptive behavior
* Economic modelling
* Network security
* Other kinds of real-world applications
5. Related Activities
* Visualisation of all aspects of GBML (performance, final solutions, evolution of the population)
* Platforms for GBML, e.g. GPGPUs
* Competitive performance, e.g. GBML performance in Competitions and Awards
* Education and dissemination of GBML, e.g. software for teaching and exploring aspects of GBML.
He received a BEng and MEng in Computer Engineering and a PhD in Computer Science from the Ramon Llull University in Barcelona, Spain in 1998, 2000 and 2004, respectively. His PhD thesis involved the adaptation and application of Learning Classifier Systems to Data Mining tasks in terms of scalability, knowledge representations and generalization capacity. In 2008 he was appointed as a Lecturer in Bioinformatics at the University of Nottingham. His post was created with the aim of developing interdisciplinary research at the interface of computer science and the biosciences. His research interests include the application of Learning Classifier Systems and other kinds of Evolutionary Learning to data mine large-scale challenging datasets and, in a general sense, the use of data mining and knowledge discovery for biological domains. He has more than 40 refereed international publications between journal papers, conference papers and book chapters, has given 8 invited talks and co-edited two books. From 2007 to 2010 he was the co-organizer of the International Workshop on Learning Classifier Systems and he was the chair of the Genetics-Based Machine Learning track of GECCO2009.
I am a senior lecturer in computer science at the university of Bristol,
where I am director of the MSc in machine learning, data mining and high
performance computing. I hold a BA in psychology, and an MSc and PhD in
computer science. Most of my work involves the analysis of complex
adaptive systems. I have written about learning classifier systems,
artificial immune systems, ensembles, methodological issues in machine
learning, the complexity of learning, network intrusion detection, AI for
game playing, the design of software for reinforcement learning, social
insect biology, and education. I co-chaired AISB 2006, IWLCS 2005 and
2006, was a track chair for GECCO 2007 and 2012 and an associate editor
of the IEEE Transactions on Evolutionary Computation from 2004-10.
Parallel Evolutionary Systems (PES)
Modern research during these last twenty years has expanded to address very interesting problems of large complexity (dimensionality, restrictions, computing intensive...). In particular, those coming from real-world scenarios are getting both larger in size and harder in complexity. Aiming at finding accurate (and robust) solutions in the shortest possible computational time, these problems face researchers to new challenges of difficult solution with traditional techniques and computers. One way to achieve unseen numerical and efficient results is the use of parallel algorithms, hardware, and specialized techniques. With the evolution of parallel architectures (symmetric multiprocessors, multi/many-cores, GPUs, etc.), many opportunities emerge for the design of efficient algorithms.
This track in GECCO aims at developing the cross-fertilization of knowledge between evolutionary algorithms (metaheuristics in general) and parallelism. Working in two domains of research is at the same time difficult and fruitful. Knowing on parallelism helps in creating parallel algorithms for clusters, grids of computers or GPUs architectures. However, it also needs 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 a big deal of high quality information on contributions to the theory and the application of techniques born from the crossover of the traditional parallel field and metaheuristics. Articles are wanted to provide significant contributions to problem solving (efficiency and also accuracy) and be methodologically well-founded.
This track includes (but not limits) topics concerning the design, implementation, and application of parallel evolutionary algorithms, and also metaheuristics in general: ACO, PSO, VNS, SS, SA, EDAs, TS, ES, GP, GRASP… As an indication, contributions are welcomed in the following areas:
- Parallel evolutionary algorithms
- Parallel metaheuristics
- Master/slave models
- Massively parallel algorithms
- SIMD/MIMD and FPGA parallelization
- Distributed and shared memory parallel algorithms
- Multicore execution of parallel algorithms
- Concurrent algorithms with several threads of execution
- Algorithms running in clusters of machines
- Grid computing
- Peer to peer (P2P) algorithms
- Ad-hoc and mobile networks for parallel algorithms
- Parallel software frameworks/libraries
- Theory on decentralized and parallel algorithms
- Parallel test benchmarks
- Parallel hybrid/memetic algorithms
- Parallel multiobjective algorithms
- Parallel algorithms and dynamic optimization problems (DOP)
- Algorithms and tools for helping in designing new parallel algorithms
- Competitive/cooperative parallel algorithms and agents
- Statistical assessment of performance for parallel algorithms
- Unified view of parallel approaches and results
- Real-world applications in telecoms, bioinformatics, engineering...
He received Ph.D. in Computer Science from the University of Málaga in 2006. He is now an PhD assistant professor at the Department of Languages and Computer Science at the University of Málaga in Spain. He is the coauthor of more than 50 international publications and the coauthor of the a recent book about Parallel Genetic Algorithms. His major research interests include the design of new metaheuristics, specifically on parallel algorithms, and their application to complex problems in the domains of bioinformatics, natural language processing, and combinatorial optimization in general. He is also currently working on the design of theory-driven algorithms and on the application of parallel technique to solve dynamic optimization problems. He also co-chaired several special sessions and workshops about metaheuristics, parallel techniques, and tools for the development of optimization methods.
He received the Master and Ph.D. degrees in Computer Science from the Institut National Polytechnique de Grenoble in France. He is a full Professor at the University of Lille and the head of DOLPHIN research group from both the Lille's Computer Science laboratory (LIFL, Université Lille 1, CNRS) and INRIA Lille Nord Europe. His current research interests are in the field of multi-objective optimization, parallel algorithms, metaheuristics, combinatorial optimization, cluster and cloud computing, hybrid and cooperative optimization, and applications to logistics/transportation, bioinformatics and networks. Professor Talbi has to his credit more than 150 international publications including journal papers, book chapters and conferences proceedings.
The GECCO 2013 theory track welcomes all papers that address theoretical issues
in the whole of evolutionary computation and allied sciences. So, in addition to
Genetic Algorithms, Evolutionary Strategies, Genetic Programming and other
traditional EC areas, we also very much welcome theory papers in Artificial Life,
Ant Colony Optimization, Swarm Intelligence, Bioinformatics, Computational Biology,
Estimation of Distribution Algorithms, Generative and Developmental Systems,
Genetics-Based Machine Learning, Search Based Software Engineering, and more.
The theory track considers submissions performing theoretical analyses or concerning
theoretical aspects in the areas described above. Results can be proven with
mathematical rigor or obtained via a thorough experimental investigation.
More information can be found at http://www.theinf.uni-jena.de/en/GECCO13.html.
Submission concerning bridging theory and practice are encouraged. Topics include (but are not limited to):
* fitness landscapes and problem difficulty
* population dynamics
* runtime analysis and blackbox complexity
* single- and multi-objective problems
* statistical approaches
* stochastic and dynamic environments
* working principles of evolutionary computing techniques in general.
He received his MSc in computer science from Univ. of Sheffield, UK, in 2002 and his diploma in mathematics from Univ. of Jena, Germany, in 2005. In 2007 he obtained a PhD in theoretical computer science from Saarland Univ., Germany. After a postdoctoral stay at ICSI Berkeley, USA, he was a senior researcher at the Max Planck Institute for Informatics and an independent research group leader at the Cluster of Excellence MMCI in Saarbrücken, Germany. Since 2012 he is full professor for theoretical computer science at Univ. of Jena, Germany. The central topics of his work are randomized algorithms (both classical and evolutionary) and randomized methods in mathematics and computer science in general. He won three best paper awards at GECCO. More information can be found at http://www.theinf.uni-jena.de.
is currently a Post-doctoral Research Fellow in the School of Computer Science at the University of Birmingham. He holds a PhD in Computer Science from the University of Essex and a Master (Laurea) in Computer Engineering from the Polytechnic University of Turin, Italy. Before the current appointment, he worked as a researcher for Hewlett-Packard Research Laboratories, as an Assistant Professor for the University of Coimbra, Portugal, and as post-doctoral research associate affiliated with the School of Computing and the Centre for Reasoning, University of Kent, UK. He has 50+ peer-reviewed publications in journals and conference proceedings almost exclusively on the geometric view of evolutionary algorithms, which he has developed in his PhD study and post-doctoral research. His current research interest is on the theoretical foundations of Evolutionary Computation, and theory-laden design of search operators.
Life/Robotics/Evolvable Hardware (ALIFE)
This track promotes evolutionary computation and bio-inspired heuristics as instruments able to face engineering problems and scientific questions in different areas that include (but are not limited to): artificial life, evolutionary robotics, and evolvable hardware.
Artificial life studies artificial systems (software, hardware, or chemical) with properties similar to those of living systems. There are two main complementary goals: to better understand living systems and to use this understanding to build artificial systems with properties of living systems, such as behavior, adaptability, evolvability, active perception, communication, self-organization and cognition. This track also welcomes models of problem-solving through (social) agent interaction, emergence of collective phenomena and models of the dynamics of ecological interactions in an evolutionary context.
Evolutionary computation techniques can be particularly useful for a large branch of robotics. The evolution of controllers, morphologies, sensors, and communication protocols is being used to build systems to provide robust, adaptive and scalable solutions to different problems in robotics. This track welcomes contributions addressing problems from control to morphology, from single robot to collective adaptive systems. Contributions are expected to deal explicitly with Evolutionary Computation, with experiments either in simulation or with real robots.
The term evolvable hardware has been used in the past to denote both the design of electronic devices able to evolve themselves, and the generic exploitation of evolutionary techniques for creating hardware. While the first task sounds ambitious, the second is routinely applied by industries. Contributions in this area are expected to show either real and potential applications.
is professor at Université Pierre et Marie Curie (UPMC, Paris, France). He is a member of the Architectures and Models for Adaptation and Cognition team at the Institute of Intelligent Systems and Robotics (ISIR, CNRS). He holds a Master's degree in Cognitive Science and a PhD degree in Computer Science from University Paris-Sud. His field of research is mostly concerned with Evolutionary Computation and Complex Systems (self-adaptive collective robotic systems, generative and developmental systems, evolutionary robotics).
is professor at the Department of Zoology at the Karl-Franzes-University, Graz, Austria, where he is supervising the Artificial Life Lab, which he founded in 2007. Currently, from July to December 2012, he is also the Basler Chair of Excellence at the East Tennessee State University (ETSU), Johnson City, TN, USA. Besides his research activities in the fields of zoology, biological/ecological modeling, bio-inspired robotics (swarm robotics, modular robotics, neural networks, artificial hormone systems, evolutionary robotics) he teaches also at the Department of "Environmental System Sciences" at the URBI faculty at Karl-Franzes-University Graz. He also acts as a lecturer in multi-agent modeling at the course of study "Industrial simulation" at the University of Applied Sciences in St.Poelten, Austria.
Biological and Biomedical
Computers have long been applied to biology and biomedical applications but the advent of genetic and evolutionary computation has dramatically increased interest and activity in the field. The aim of this GECCO track is to provide a focus for the use of genetic and evolutionary computation to the biological and biomedical sciences.
Submissions are welcome in the following and related areas:
- Biological systems
- Biomedical systems
- Data analysis
- Data mining
- Genetic networks
- Microarray Data Analysis
- Sequence analysis
- Visualization and imaging
She is an associate professor at North Carolina State University in the Bioinformatics Research Center, and Department of Statistics. Her primary research interest is statistical genetics, and she relies on evolutionary computation approaches to detect and understand associations with genetic, environmental, and metabolomic variables and human diseases. She has published over 100 publications in statistics, genetics, and computer science journals. http://www4.stat.ncsu.edu/~motsinger/Lab_Website/Home.html
He received a BSc in Computer Science and then an MSc and PhD in Electronic Engineering from the University of Kent, UK. He is currently a senior lecturer in the Department of Electronics at the University of York, UK. Steve's main research interests are in developing novel representations of evolutionary algorithms particularly with application to problems in medicine. His work is currently centered on the diagnosis of neurological dysfunction and analysis of mammograms. Steve was program chair for the Euromicro Workshop on Medical Informatics, program chair and local organizer for the Sixth International Workshop on Information Processing in Cells and Tissues (IPCAT) and guest editor for the subsequent special issue of BioSystems journal. Most recently he was tutorial chair for the IEEE Congress on Evolutionary Computation (CEC) in 2009, local organizer for the International Conference on Evolvable Systems (ICES) in 2010 and general co-chair for the 9th International Conference on Information Processing in Cells and Tissues (IPCAT). Steve is associate editor for the journal Genetic Programming and Evolvable Machines and a member of the editorial board for the International Journal of Computers in Healthcare and Neural Computing Applications. Steve has some 75 refereed publications, is a Chartered Engineer and a fellow of the British Computer Society.
Estimation of distribution algorithms (EDAs)
Estimation of distribution algorithms (EDAs) are based on the explicit use of probability distributions. They replace traditional variation operators of evolutionary algorithms, such as mutation and crossover, by building a probabilistic model of promising solutions and sampling the built model to generate new candidate solutions. Using probabilistic models for exploration in evolutionary algorithms enables the use of advanced methods of machine learning and statistics for automated identification and exploitation of problem regularities for broad classes of problems. In addition they provide natural ways to introduce problem information into the search by means of the probabilistic model and also to get information about the problem that is being optimized. EDAs provide a robust and scalable solution to many important classes of optimization problems with only little problem specific knowledge.
The aim of the track is to attract the latest high quality research on EDAs in particular, and the use of explicit probabilistic and graphical models in evolutionary algorithms in general. We encourage the submission of original and previously unpublished work especially in the following areas:
1. Advances in the theoretical foundations of EDAs.
2. Novel applications for EDAs.
3. Case studies or showcases that highlight the use of EDA in practical decision making.
4. Interfaces between EDA, Ant Colony Optimization, Evolution Strategies, Cross-Entropy Method and other related methods.
5. Position papers on EDA-related topics.
6. Reviews of specific EDA-related aspects.
7. Comparisons of EDA and other metaheuristics, evolutionary algorithms, more traditional optimization methods of operations research or hybrids thereof.
8. EDA for dynamic, multiobjective or noisy problems and interactive EDA.
9. Hybrid EDAs.
10. New EDAs.
11. Statistical modeling in evolutionary algorithms.
12. Probabilistic models as fitness surrogates
The above list of topics is not exhaustive; if you think that your work does not fit the above categories but the work should belong to the EDA track, please contact the track chairs to discuss this issue.
José Antonio Lozano
He received an M.Sc. degree in mathematics and an M.Sc. degree in computer science from the University of the Basque Country, Spain, in 1991 and 1992 respectively, and the PhD degree in computer science from the University of the Basque Country, Spain, in 1998. Since 2008 he is full professor of the University of the Basque Country, where he leads the Intelligent System Group. He is the coauthor of more than 50 ISI journal publications and co-editor of the first book published about Estimation of Distribution Algorithms. His major research interests include machine learning, probabilistic graphical models, evolutionary computation, data mining, metaheuristic algorithms, and real-world applications. Prof. Lozano is associate editor of IEEE trans. on Evolutionary Computation and member of the editorial board of Evolutionary Computation journal, Soft Computing, Applied Intelligence and other three journals.
is a Professor of Computing in the IDEAS Research Institute at Robert Gordon University in Scotland.
Originally a pure mathematician (algebraic topology), he has over twenty years research experience in naturally-inspired computing. Major themes of his research include the development and analysis of novel metaheuristics, particularly markov-network EDAs, and probabilistic modelling for optimisation and learning. Application areas of his research include medical decision support, drilling rig market analysis, analysis of biological sequences, staff rostering and scheduling, image analysis and bio-control. Algorithms developed from his research have been implemented as commercial software.
Prof. McCall has over 70 publications in books, international journals and conferences and he chairs the IEEE ECTC Task Force in Evolutionary Algorithms based on Probabilistic Models.
Evolution Strategies and Evolutionary Programming (ESEP)
The ES/EP track is concerned with nature-inspired black-box search paradigms for continuous optimization. The historical Evolution Strategies (ES) and Evolutionary Programming (EP) operate on the
phenotypic problem representation (i.e., with a trivial genotype-phenotype mapping), generally on real-valued representations. They often employ sophisticated mechanisms for the
adaptation of their strategy parameters and owe much of their success to their high efficiency, solid theoretical foundations, universal applicability, ease of use, and robustness.
Henceforth, this track invites submissions that present original work on algorithms for continuous optimization, either stochastic or deterministic, starting with but not limited to ES/EP, and including Differential Evolution, Particle Swarm Optimization for continuous problems, Real-Coded Genetic Algorithms, continuous Estimation of Distribution Algorithms, Markov Chain Monte Carlo methods for continuous optimization, Cross-Entropy Methods, …). We encourage papers focusing on theoretical analysis as well as applications to real-world problems and benchmark function suites. We welcome further development and improvement of existing algorithms, particularly for uncertain and/or changing environments and for constrained, multi-modal, multi-objective, budgeted, large-scale, and/or mixed-integer problems.
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 and 2013. Together with Benjamin Doerr, she is editor of the book "Theory of Randomized Search Heuristics".
He studied Computer Science at the Technical University of Dortmund, Germany. In 2002, he received his Doctoral degree from the Faculty of Technology, Bielefeld University, Germany, and in 2010 his Habilitation degree from the Department of Electrical Engineering and Information Sciences, Ruhr-University Bochum, Germany. From 2002 to 2010, Christian was a Juniorprofessor for Optimization of Adaptive Systems at the Institute for Neural Computation, Ruhr-University Bochum. In October 2010, he was appointed professor with special duties in machine learning at DIKU, the Department of Computer Science at the University of Copenhagen, Denmark.
Generative and Developmental Systems (GDS)
The continuing growth in systems’ size and complexity (of hardware, software and networks) has rendered the engineering traditions of rigid top-down planning and control unsustainable. Understanding the evolution of natural complex systems—large sets of elements interacting locally and giving rise to collective behavior—can help create a new generation of truly autonomous and adaptive artificial systems. Biological evolution has produced the astounding complexity and diversity of living organisms based on random mutations, nonrandom selection and self-organization. The Generative and Developmental Systems (GDS) track seeks to unlock the full potential of “in silico” evolution as a design methodology that can scale to systems of great complexity. It aims to create complex and diverse artifacts that meet our specifications with minimal guidance and programming effort.
Indirect and open-ended representations Representing more than the information needed to produce a single individual, the genotype is a layered repository of many generations of evolutionary innovation, and is shaped by two requirements: to be fit in the short term, and to be evolvable over the long term through its influence on the production of variation. “Indirect representations” such as morphogenesis or string-rewriting grammars, which rely on developmental or generative processes, may allow long-term improvement via accumulated elaborations and emergent new features. “Direct representations” are not capable of open-ended elaboration because they are restricted to predefined features.
Complex environments encourage complex phenotypes While complex genotypes are not necessarily favored in simple environments, they may enable unprecedented phenotypes and behaviors that can later successfully invade new, uncrowded niches in complex environments—which can create pressure toward increasing complexity. Many factors may affect environmental (hence genotypic) complexity, such as spatial structure, temporal fluctuations, or competitive co-evolution.
More is more Today’s typical numbers of generations, sizes of populations, and components inside individuals are still too small. Just like physics needs higher-energy accelerators and farther-reaching telescopes to understand matter and space-time, evolutionary computation needs a strong boost in computational resolution and scope to understand the spontaneous generation of complex functionality. Biological evolution involved 4 billion years and untold numbers of organisms. We expect that datacenter-scale computing power will be applied in the future to produce artificially evolved artifacts of great complexity. How will we apply such resources most efficiently?
Over 150 years after Darwin’s and Mendel’s work, and the subsequent “Modern Synthesis” of evolution and genetics, the developmental process that maps genotype to phenotype is still poorly understood. Yet, development cannot remain an abstraction if we wish to encourage open-ended evolutionary novelty in artificial systems. The GDS track at GECCO 2013 seeks to understand the full evolution-of-development (“evo-devo”) picture. It stresses the importance of the generative and developmental processes that generate the raw material for selection; such representations are uniquely capable of producing ongoing, open-ended innovation.
We invite all papers related to the evolution of complexity, including in the areas of:
- artificial development, artificial embryogeny
- evo-devo robotics, morphogenetic robotics
- evolution of evolvability
- gene regulatory networks
- grammar-based systems, generative systems, rewriting systems
- indirect mappings, compact encodings, novel representations
- measures of complexity, theories of scalable design
- morphogenetic engineering
- neural development, neuroevolution, augmenting topologies
- spatial computing, amorphous computing
Additionally, papers in the following areas will be considered if they have a particular focus on representations and/or scaling to high complexity:
- competitive co-evolution (arms races)
- complex, spatially structured, and dynamically changing environments
- diversity preservation, novelty search
- large numbers of generations, individuals, and internal components
- unconventional computing, natural computing, organic computing
- synthetic biology, biological and chemical IT, artificial chemistry
More information can be found at http://iscpif.fr/gds2013
He is a Research Associate Professor at Drexel University (Philadelphia), and a former Director of the Complex Systems Institute, Paris. He co-founded the Complex Systems Master's at Ecole Polytechnique, where he is an Adjunct Lecturer. Previously, he was a Visiting Assistant Professor in computer science at the University of Nevada, after an extended period in the Bay Area's software industry. An alumnus of Ecole Normale Supérieure, Paris, he completed his PhD in 1991 and a postdoc in neuroinformatics at the Ruhr-Universität Bochum, Germany. The main theme of R. Doursat's research is the computational modeling and simulation of morphogenetic engineering systems (book published), i.e. how complex architectures self-organize from a swarm of heterogeneous agents via dynamical, developmental, and evolutionary processes. He was the General Chair of ECAL 2011, the European Conference on Artificial Life.
Michael E. Palmer
He is a Research Associate in the Department of Biology at Stanford University. He was previously Chief Technical Officer of the Web Search Division of Inktomi Corporation, a provider of internet-scale web search results to partners including Yahoo!, MSN, and AOL. At Stanford, Dr. Palmer studies the evolutionary increase of complexity over macroevolutionary timescales, and how this process might be manipulated and harnessed. He is the author of the LBrain system for artificial neurogenesis and synaptogenesis, an open-source software package for the evolution of neural networks that are "grown" via Lindenmayer-system-like rules, with applications to robotic control. His most recent paper is entitled Survivability Is More Fundamental Than Evolvability.
He is Associate Professor in computer science at the University of Vermont (UVM). He was named a Microsoft Research New Faculty Fellow in 2006 and a member of the "TR35: MIT Technology Review's top 35 innovators under the age of 35" in the same year. In 2011 he was awarded a Presidential Early Career Award for Scientists and Engineers (PECASE) by U. S. President Barack Obama. He currently serves as a vice chair of the UVM Complex Systems Spire, and is the co-author of the popular science book entitled How the Body Shapes the Way We Think: A New View of Intelligence, MIT Press. His interests include evolutionary robotics, crowdsourcing and machine science.
Real-world applications (RWA)
The RWA track welcomes rigorous experimental, computational and/or applied advances in evolutionary computation (EC) in any discipline devoted to the study of real-world problems. The aim is to bring together a rich and diverse set of fields, such as: Engineering and Technological Sciences, Mathematical Sciences, Numerical and Computational Sciences, Physical Sciences, Cosmological Sciences, Environmental Sciences, Geophysical Sciences, Oceanographic Sciences, Chemical Sciences, Biological Sciences, Atmospheric Sciences, Aerospace Sciences, Social Sciences and Economics; into a single event where the major interest is on applications including but not limited to:
1. 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.
2. Papers that describe EC systems using distributed computing (cloud, Mapreduce / Hadoop, grid, GPGPU, etc.) for real-world applications.
3.Papers that present rigorous comparisons across techniques in a real-world application.
4.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.).
Keywords: Real-world applications, distributed computing, scalability, industrial applications, scalable implementations.
He graduated from Dept. Science of University of Tokyo in 1985 and received Ph.D. degree from Dept. Engineering of University of Tokyo in 1970. Since then, he had been working in ETL (ElectroTechnical Lab). He joined Department of Electronical 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.
He is a research director of the evolutionary computer vision laboratory, EvoVisión, at the CICESE Research Center in Baja California, Mexico. Dr. Olague holds a PhD in Computer Graphics, Vision and Robotics from INPG/INRIA. He is recipient of the "2003 First Honorable Mention for the Talbert Abrams Award", offered by the American Society for Photogrammetry and Remote Sensing for his work on camera placement. Dr. Olague has won several best paper awards at major international conferences such as: GECCO and EvoStar like the Real-world application track, EvoIASP, and EvoHOT. Together with his students he has won two times the bronze medal at the Human Competitive awards for his work on the synthesis of interest point detectors and descriptors. He has been co-editor at the journals of Pattern Recognition Letters and Evolutionary Computation, MIT Press. Dr. Olague is charing a new IEEE task force on Evolutionary Brain Computing. He is on the editorial board of Neural Computing !
and Applications, Springer. Today, Dr. Olague is preparing a monograph on Evolutionary Computer Vision for Springer to be published next year.
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. 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 Algorithms
The software engineering tasks to which they are applied are drawn from throughout the engineering lifecycle and include, but are not limited to:
- Project Management and Organization
- Requirements Engineering
- Developing Dynamic Service-Oriented Systems
- Configuring Cloud-Based Architectures
- Enabling Self-healing/Self-optimizing Systems
- Creating Recommendation Systems to Support Software Development
- Software Security
- System and Software Integration
- Test Data Generation
- Regression Testing Optimization
- Network Design and Monitoring
- Software Maintenance, Program Repair, Refactoring and Transformation
He is an associate professor in the NEO-GISUM Group in the Department of Languages and Computing Sciences of the University of Malaga, Spain. His research interests include the application of randomized search techniques to Software Engineering problems. In particular, he contributed to the domains of software testing, model checking and software project scheduling. He is the author of more than 60 refereed publications, has 3 best paper awards, has served on 20 program committees and is frequent reviewer in more than 10 international journals.
He is professor of Software Engineering in the Department of Computer Science at University College London, where he directs the CREST centre and is Head of Software Systems Engineering. He is widely known for work on source code analysis and testing and was instrumental in founding the field of Search Based Software Engineering (SBSE). He has given 27 keynote invited talks and has 9 best paper awards. Professor Harman is the author of more than 200 refereed publications, is on the editorial boards of six international journals, and has served on 153 program committees.
Self-* Search (SS)
Search and optimization problems are everywhere, and search algorithms are getting increasingly powerful. However, they are also getting increasingly complex, and only autonomous self-managed systems that provide high-level abstractions can turn search algorithms into widely used methodologies. Such systems should be able to configure themselves on the fly, automatically adapting to the changing problem conditions, based on general goals provided by their users. Self-* search systems generally incorporate ideas from adaptation, online and offline machine learning. The overall goal is to reduce the role of the human expert in the process of designing an algorithm to solve a computational search problem.
The aim of the Self-* Search track is to bring together researchers from computer science, artificial intelligence and operations research, interested in software systems able to automatically tune, configure, or even generate and design optimization algorithms and search heuristics.
We also encourage submissions related to the automated design and configuration of algorithms in other areas such as machine learning, games and constraint programming.
We invite all papers related to Self-* Search, in particular (but not limited to) those in the following subject areas:
- Adaptive differential evolution and particle swarm optimisation
- Adaptive and co-evolutionary multimeme algorithms
- Adaptive and self-adaptive parameter control
- Adaptive operator selection
- Algorithm selection and portfolios
- Applications of self-* techniques to multi-objective, dynamic, and complex real-world problems.
- Auto-constructive evolution
- Automated construction of heuristics and/or algorithms
- Automatic algorithm configuration
- Autonomous control for search algorithms
- Computer-aided algorithm design
- Cross-domain heuristic search
- Evolving heuristics and/or algorithms
- Meta-learning and meta-genetic programming
- Multi-level search
- Online learning for heuristic/operator selection
- Reactive search and intelligent optimization
More info: http://www.cs.stir.ac.uk/~goc/geccoss2013/index.html
Dr Gabriela Ochoa is a Senior Research Fellow in the Division of Computer Science and Mathematics, University of Stirling, Scotland, UK. She holds BEng and MRes degrees in Computer Science from the University Simon Bolivar, Venezuela; and a PhD in Computer 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 and fitness landscape analysis. Dr. Ochoa was involved in founding the Self-* Search (SS) track at GECCO in 2011, and proposed and co-organised the first Cross-domain Heuristic Search Challenge (CHeSC 2011). She is an associate editor of the Journal of Evolutionary Computation (MIT Press).
Gisele L. Pappa
Gisele L. Pappa received her PhD in Computer Science from the University of Kent, Canterbury, UK, in 2007. She is currently an Associate Professor at Federal University of Minas Gerais, Brazil, and is also a member of the Brazilian National Institute for the Web (InWeb).
She is the author of a research-oriented book on data mining and evolutionary algorithms, and her current research interests are on bio-inspired computational intelligence algorithms for learning, hyperheuristics and social networks.
Genetic Algorithm (GA)
The Genetic Algorithm (GA) track has always been a large and important track at GECCO. We invite submissions to the GA track that present original work on all aspects of genetic algorithms, including, but not limited to:
1. Practical and theoretical aspects of GAs
2. Design of new GA operators including representations, fitness functions, initialization, termination, selection, recombination, and mutation
3. Design of new and improved GAs
4. Comparisons with other methods (e.g., empirical performance analysis)
5. Hybrid approaches (e.g., memetic algorithms)
6. Design of tailored GAs for new application areas
7. Handling uncertainty (e.g., dynamic and stochastic problems, robustness)
8. Metamodeling and surrogate assisted evolution
9. Interactive GAs
10. Co-evolutionary algorithms
11. Parameter tuning & control (including adaptation and meta-GAs)
12. Constraint Handling
13. Diversity Control (e.g., Fitness Sharing & Crowding, Automatic Speciation, Spatial models such as Island/Diffusion)
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.
He is an Associate Professor in the Department of Computer Science at Missouri University of Science and Technology (S&T), Guest Scientist at Los Alamos National Laboratory, and the founding director of S&T's Natural Computation Laboratory (NC-LAB). He received his Ph.D. in 2002 from Leiden University for Adaptive Information Filtering employing a novel type of evolutionary algorithm. He served previously as GECCO 2010 Late Breaking Papers Chair, COMPSAC 2011 Doctoral Symposium Chair, and GECCO 2012 GA Track Co-Chair. For several years he has served on the GECCO GA track program committee and the Congress on Evolutionary Computation program committee. His research interests include the design of novel self-configuring evolutionary algorithms and the application of evolutionary algorithms in cyber security, critical infrastructure protection, and search-based software engineering. He was granted a US patent for an artificially intelligent rule-based system to assist teams in becoming more effective by improving the communication process between team members.
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 18 journal papers, 39 conference papers, contributed seven book chapters and authored one book on evolutionary algorithm theory.
His research is centred around design and theoretical analysis of evolutionary algorithms, artificial immune systems, and other randomised search heuristics. He is associate editor of Evolutionary Computation (MIT Press), member of the steering committee of the Theory of Randomised Search Heuristics workshop series (ThRaSH), was program co-chair of the GECCO theory track 2009 and at PPSN 2008, co-organised FOGA 2009, co-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).
Integrative Genetic and
Evolutionary Computation (IGEC)
GECCO has traditionally been a collection of mini-conferences, so authors have to choose a particular track to submit to. While this works fine for the majority of papers, some authors struggled to choose a track. This is why GECCO has introduced the track on "Integrative Genetic and Evolutionary Computation (IGEC)".
This track welcomes all papers that the authors feel do not fit into a particular track or that cross multiple tracks.
Topics include but are not limited to:
•Research on combining multiple evolutionary algorithms, such as Genetic Algorithms, Evolutionary Programming, Evolution Strategies and others
•Classification of evolutionary algorithms
•Hybrid algorithms in general
•Novel nature-inspired paradigms
•Dynamic and stochastic environments
•Statistical analysis techniques for EAs
•Evolutionary algorithm toolboxes
•Evolutionary algorithms with expensive fitness evaluations
All accepted papers will appear in the proceedings of GECCO 2013, which will be published by ACM (Association for Computing Machinery).
is Professor of Operational Research and Systems at Warwick Business School (UK). His research in evolutionary computation includes, among other topics, multiobjective optimization, the handling of uncertainty (such as optimization in dynamic and stochastic environments), as well as complex systems design. Juergen has been an active researcher in evolutionary computation for 19 years, and has published more than 130 papers in international peer-reviewed journals and conferences. He is area editor of the Journal of Heuristics and associate editor of the Evolutionary Computation Journal.
Yew Soon Ong
is an Associate Professor with the School of Computer Engineering, Nanyang Technological University, Singapore, and he is Director of the Center for Computational Intelligence. His current research interest lies in Natural Computing that spans across Evolutionary computation, Memetic computing, Robust & Complex Optimization, Machine Learning & Informatics. He is 1) co-founder and editor-in-chief of Memetic Computing Journal 2) co-founder and chief editor of Book Series on Studies in Adaptation, Learning, and Optimization, associate editor of 1) IEEE Computational Intelligence Magazine, 2) IEEE Transactions on Systems, Man and Cybernetics - Part B, 3) Information Sciences, 4) Soft Computing Journal, 5) International Journal of System Sciences. He currently chairs the IEEE Computational Intelligence Society Emergent Technology Technical Committee and is founder of the Task Force on Memetic Computing. He has coauthored over 120 refereed publications. More information can be found at http://www.ntu.edu.sg/home/asysong
SIGEVO Executive Committee
Wolfgang Banzhaf (chair)
Erik D. Goodman
|Pier Luca Lanzi