Program Tracks: Three days of presentations of the
latest high-quality results in at least
15 separate and independent
program tracks specializing in various
aspects of genetic and evolutionary
computation.
GECCO Organizers:
Program Tracks and Chairs:
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Artificial Life, Evolutionary Robotics,
Adaptive Behavior, Evolvable Hardware:
This track promotes evolutionary computation as an instrument to generate
bio-inspired systems able to face engineering problems in different
investigation areas that include but are not limited to: artificial
life, evolutionary robotics, adaptive behavior and evolvable hardware.
In particular, this tack will show the latest developments in the field
of evolutionary algorithms applied to enginering problems. Participants
will share the challenges the industry and academia face, and learn how
these challenges may be addressed using innovative evolutionary
techniques developed in academia.
We encourage the submission of original works in the following not
exclusive topics:
• Evolutionary robotics
• Adaptive control behavior
• Artificial life
• Analog circuit design
• Automatic test pattern generation
• Built-in self test
• Design verification
• Evolutionary design of electronic circuits
• Evolutionary hardware design methodologies
• Evolutionary robotics
• Evolvable hardware
• Floorplanning
• Hardware/Software co-design
• Hybrid evolutionary/exact approach
• Hardware accelerated methodologies
• Logic synthesis
• Production Test Automation
• Routing
• Test program generation
All submissions will be reviewed by a board of experts in both EC and
Electronic Design Automation.
Please, refer to this web site for the submission procedure.
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Bioinformatics, Computational, Systems and Synthetic Biology
The scope of this track is any research that applies evolutionary (and related) techniques to solving biological and biomedical problems. All "flavors" of evolutionary techniques consistent with GECCO are included in this scope, including genetic algorithms, genetic programming, estimation of distribution algorithms, evolution strategies, evolutionary programming, ant colony optimization, swarm intelligence, artificial life and hybrid methods with any of these components. Papers that integrate evolutionary methods into bioinformatics, biomedical informatics, biostatistics, patient specific information processing, computational biology, systems biology and synthetic biology, for example, are welcome. Research that demonstrates experimental validation and/or biological interpretation of computational results, specially in systems and synthetic biology, are also particularly welcome.
Some specific examples of biological and biomedical issues that papers could address include:
• Data mining and classification in biological or biomedical databases
• Metabolomics, Proteomics and Genomics
• Biological network analysis
• Systems biology
• Synthetic biology
• Diagnostic or predictive testing in epidemiology and genetics
• Functional diversification through gene duplication and exon shuffling
• Gene expression and regulation, alternative splicing
• Genetic association studies
• Haplotype and linkage disequilibrium analysis
• Image analysis and pattern recognition
• Microarray analysis
• Network reconstruction for development, expression, catalysis etc.
• Pharmacokinetic and pharmacodynamic analysis
• Phylogenetic reconstruction and analysis
• Relationships between evolved systems and their environment (e.g. phylogeography)
• Relationships within evolved communities (cooperation, coevolution, symbiosis, etc.)
• Sensitivity of speciation to variations in evolutionary processes
• Sequence and structure alignment and analysis
• Simulation of cells, viruses, organisms and whole ecologies
• Structure prediction for biological molecules (structural biology)
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Natalio Krasnogor:
He is an Associate Professor and Reader in Interdisciplinary Computer Science in the School of Computer Science at the University of Nottingham. He is a member of the Automated Scheduling, Optimisation and Planning Research Group (ASAP) and also leads the Interdisciplinary Optimisation Laboratory (IOL). Krasnogor's research activities lie at the interface of Computer Science and the Natural Sciences, e.g. Biology, Physics, Chemistry. In particular, he develops innovative and competitive search methodologies and intelligent decision support systems for transdisciplinary optimisation, modelling of complex systems and very-large datasets processing.
He has applied his expertise to Bioinformatics, Systems Biology, Synthetic Biology, Nanoscience and Chemistry. He is member of the editorial boards for the journal Modelling and Simulation in Engineering and the journal Artificial Evolution and Applications. He is associate editor of the Evolutionary Computation journal and founding technical editor-in-chief of the new journal Memetic Computing. Krasnogor has acted as grant reviewer for the EPSRC (UK), BBSRC(UK), European Science Foundation, European Union, The Israel Science Foundation, DOE Computational Biology Programme (USA), CNRS (France), etc. More details are available at www.cs.nott.ac.uk/~nxk |
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Combinatorial Optimization and Metaheuristics:
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 huge
variety of areas, including bioinformatics, logistics,
engineering, business, etc., have been tackled successfully
with metaheuristic approaches. For many problems the
resulting algorithms are considered to be state-of-the-art.
Apart from evolutionary algorithms, the class of metaheuristics
includes prominent members such as tabu search, iterated
local search, simulated annealing, GRASP, and ant colony
optimization.
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
• Theoretical developments in combinatorial optimization and metaheuristics
• Representation techniques
• Neighborhoods and efficient algorithms for searching them
• Variation operators for stochastic search methods
• Search space analysis
• Comparisons between different (also exact) techniques
• Constraint-handling techniques
• Hybrid methods and hybridization techniques
• Insight into problem characteristics of problem classes for EMO"
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Estimation of Distribution Algorithms
What are Estimation of Distribution Algorithms?
Estimation of distribution algorithms (EDAs) replace
traditional variation operators of genetic and 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 genetic
and 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. As a result, 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. We encourage the submission of original
and previously unpublished work in, e.g., the following
areas:
• Advances in the theoretical foundations of EDAs
• Novel applications for EDAs
• Case studies or showcases that highlight the use of EDA in practical decision
making
• Interfaces between EDA, Ant Colony Optimization, Evolution Strategies, Cross-Entropy
Method or other
• Position papers
• Reviews of specific EDA-related aspects
• Comparisons of EDA to other metaheuristics, EAs, classical methods from Operations
Research or hybrids thereof
• EDA for dynamic, multiobjective or noisy problems and interactive EDA
• New EDAs
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Kumara Sastry |
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Evolutionary Multiobjective
Optimization:
EMO is concerned with the formulation and the solution,
by heuristic means, of all types of vector-valued optimization
and search problems. EMO builds naturally on some of
the themes of genetic and evolutionary computation -
robustness, adaptation, diversity and niching, exploration/exploitation,
and the deep connections between optimization and learning
- to search large problem spaces for *sets*
of optimal "tradeoffs".
The EMO track at GECCO recognizes the full gamut of
research effort in this now broad area, including but
not limited to the following suggested topics:
• Research on all types of iterative search method applied
to multiobjective problems, including GAs, EP, GP, ESs,
memetic algorithms, stochastic local search methods,
PSO, ACO, differential evolution, EDAs, PMBLAs and others;
• Theory related to EMO algorithms, performance measures,
order relations or problem characteristics;
• Real-valued
optimization problems with multiple criteria;
• Combinatorial
and mixed-integer problems with multiple criteria;
• Multiobjective constraint satisfaction problems;
• Constrained
problems treated in a multiobjective way, or multiobjective
and constrained problems;
• Multi-competence cybernetics,
or studies of multiobjective adaptation in natural or
artificial life systems;
• EMO in online optimization
problems and optimal control;
• EMO for robust optimization
problems;
• Theoretical or empirical studies on any component
of EMO methods, e.g. niching, archiving and elitism,
selection, local improvement;
• Special representations
for EMO problems;
• Special search operators for EMO
problems;
• Integration of decision-making (MCDA) in
EMO approaches;
• Preference articulation methods combined
with search, and preference measures on sets;
• Incorporation
of machine learning into EMO algorithms;
• Multiobjective
problems with expensive-to-evaluate functions;
• Multiobjective
problem decomposition including objective-dimensionality
reduction;
• Theory of set-valued optimization;
• Studies
on scalability of EMO approaches, including many-objective
optimization;
• Solving single-objective problems using
EMO approaches;
• Parallel models and implementations
of EMO approaches;
• Dynamic, stochastic and noisy optimization
problems involving or making use of multiple objectives;
• EMO applications and case studies in any sphere, e.g.
engineering, design, telecommunications and IT, robotics,
machine learning and data mining, computational biology,
operations management, and others;
• Hybrid methods,
especially combinations of evolutionary and non-evolutionary
approaches to MO;
• Empirical performance comparisons
of EMO approaches and/or comparisons with non-EMO approaches;
• Test functions and problems for benchmarking EMO methods.
As you can see, there are many objectives for the field
to pursue, and despite some solid foundations, we are
still a long way from the Pareto front! We thus look
forward to your contributions.
[NB: Whilst we do not wish to exclude any relevant papers
a priori, we would like to warn authors that the EMO
track is usually very large. It may be better in some
circumstances to submit your paper elsewhere if its *main*
subject is not really EMO, or if it would fit well in
another track. Contact the track chairs if you are unsure.]
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Kalyanmoy Deb
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Evolution Strategies, Evolutionary
Programming:
Evolution strategies (ES) and evolutionary programming (EP) are nature-inspired optimization paradigms that generally operate on the "natural" problem representation (i.e., without a genotype-phenotype mapping). For example, when used in connection with real-valued problems, ES and EP use real-valued representations of search points. Moreover, both may rely on sophisticated mechanisms for the adaptation of their strategy parameters. ES and EP owe much of their success to their universal applicability, ease of use, and robustness.
This track invites submissions that present original work on ES/EP and related algorithms that may include, but is not limited to, theoretical and empirical evaluations, further development and improvement of the algorithms, and applications to benchmark problems and test function suites. Particularly encouraged are submissions with focus on
• Adaptation mechanisms
• Interesting ES/EP applications
• ES/EP theory
• Comparisons of ES/EP with other optimization methods
• Related algorithms, such as differential evolution
• Hybrid strategies
• ES/EP in uncertain and/or changing environments
• Constrained and/or multimodal problems
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Dirk Arnold:
He is an Associate Professor with the Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada. He received the Ph.D. degree from the Department of Computer Science, University of Dortmund, Germany, in 2001. His research interests include evolutionary computation, optimization, and computer graphics and animation. |
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Genetic Algorithms:
The Genetic Algorithm (GA) track has always been the largest scientific 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
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Design of new GAs (including representation and operators)
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Improvements over existing algorithms
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Comparisons with other methods / empirical performance
analysis
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Hybrid approaches
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New application areas
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Handling uncertainty (dynamic and stochastic problems,
robustness)
•
Metamodelling
•
Interactive GAs
•
Co-evolutionary algorithms
As a large and diverse track, the GA track will be
an excellent
opportunity to present and discuss your research/application
with
experts and participants of GECCO.
Since 2009 is the year in which we celebrate the 200th
anniversary of Charles Darwin we also
invite original high-quality manuscripts discussing the
historical relationship between the work of Darwin and
Wallace and the field of GA research.
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Genetics Based Machine Learning:
The Genetics-Based Machine Learning (GBML) track encompasses advancements in any system that addresses machine learning problems with evolutionary computation methods. Combinations of machine learning with evolutionary computation techniques are particularly welcome.
The field of Learning Classifier Systems (LCS), introduced by John Holland in the 1970s, is one of the most active and best-developed forms of GBML and we welcome all work on LCSs. Artificial Immune Systems (AIS) are another family of techniques included in this track, which takes inspiration of different immunological mechanisms in vertebrates in order to solve computational problems. Moreover, neuroevolution technologies, which combine neural network techniques with evolutionary computation, are welcome. However, also any other technique or approach listed below will be considered gladly.
In list form, the GBML track encourages submissions including but not limited to the following areas:
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 (or noisy) environments
• Complexity analysis in MDP and POMDP problems
2. System advancements and new developments
Systems included (but not limited to):
• Evolutionary rule learning, including but not limited to:
• Michigan style (SCS, NewBoole, EpiCS, ZCS, XCS, UCS...)
• Pittsburgh style (GABIL, GIL, COGIN, REGAL, GA-Miner, GALE, MOLCS, GAssist...)
• Anticipatory LCS (ACS, ACS2, XACS, YACS, MACS...)
• Iterative Rule Learning Approach (SIA, HIDER, NAX, BioHEL, ...) Genetic-based inductive learning
• Genetic fuzzy systems
• Learning using evolutionary estimation of distribution algorithms
• Evolution of Neural Networks
• Evolution of ensemble systems
• Other hybrids combining evolutionary techniques with other machine learning
techniques
• Artificial Immune Systems
Enhancements included (but not limited to):
• 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
3. Applications
• Data mining
• Large scale datasets
• 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
• Network security
• Other kinds of real-world applications
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Martin V. Butz:
He received his PhD in computer science at the University of Illinois at Urbana-Champaign in October 2004 under the supervision of David E. Goldberg. His thesis "Rule-based evolutionary online learning systems: Learning Bounds, Classification, and Prediction" puts forward a modular, facet-wise system analysis for Learning Classifier Systems (LCSs) and analyzes and enhances the XCS classifier system. Until September 2007, Butz was working at the University of Würzburg at the Department of Cognitive Psychology III on the interdisciplinary cognitive systems project "MindRACES: From reactive to anticipatory cognitive embodied systems". In October 2007 he founded his own cognitive systems laboratory: "Cognitive Bodyspaces: Learning and Behavior" (COBOSLAB), funded by the German research foundation under the Emmy Noether framework. |
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Genetic Programming:
In the field of Genetic Programming, evolutionary algorithms
are used to automatically search for an algorithm or
structure solving a given problem.
Representations include tree-structures, linear sequences of code, graphs or
grammars. The use of Genetic Programming basically reduces the programming
of computers to the definition of a suitable fitness function.
The Genetic Programming (GP) track invites original submissions on all aspects
of the evolutionary generation of computer programs or other variable
sized structures. Topics include but are not limited to
• Theoretical developments
• Empirical studies of GP performance and behavior
• Novel algorithms, representations and/or operators
• Hybrid architectures including GP components
• Unconventional evolvable computation
• Evolution of tree or graph structures
• Evolution of Lindenmayer Systems
• Grammar-based GP
• Linear GP
• Self-Reproducing Programs
• Evolution of various classes of automata or machines (e.g. cellular
automata, finite state machines, pushdown automata, Turing machines)
• Object-oriented Genetic Programming
• Evolution of functional languages
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Leonardo
Vanneschi:
• Born
in Florence (Italy) on
October 3rd, 1970.
• Laurea (maitrise) “summa cum laude” in Computer Science at
the University of Pisa, achieved on February 23rd, 1996.
• PhD in Computer Science at the University of Lausanne (Switzerland),
achieved on September 06th, 2004.
• Current Position: Assistant Professor (Ricercatore) by the Dipartimento
di Informatica, Sistemistica e Comunicazione (D.I.S.Co.) of the Science Faculty
of the University of Milano-Bicocca (Italy). |
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Generative and Developmental
Systems:
Generative and Developmental Systems (GDS) is the study of indirect
encodings, i.e. indirect mappings between the genotype and phenotype. The
aim of such systems is to exploit powerful encodings to compactly represent
complex structures and increase the scalability and evolvability of
evolutionary algorithms. Traditionally, GDS has focused on mappings that
involve a developmental stage, implemented in a variety of ways including
re-write systems and cell chemistry simulations, and in some cases is used
to gain insight into biological development and aspects thereof.
However, GDS concerns a wide range of indirect encodings,
including high-level abstractions of biological development
and environmental interactions. The GDS track is open
to all submissions concerned with the genotype-phenotype
map and encourages comparison, discussion, and analysis
of the advantages and relationships among various encodings.
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Julian Miller:
He is a lecturer in the Department of Electronics
at the University of York. His main research interests are genetic programming
(GP), and computational development. He has published over 130 refereed
papers on evolutionary computation, genetic programming, evolvable hardware,
and computational development. He has been chair or co-chair of twelve
conferences or workshops in genetic programming, computational development,
evolvable hardware and evolutionary techniques. |
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Parallel Evolutionary Systems (PES)
This track in GECCO aims at developing the cross-fertilization
of knowledge between evolutionary algorithms (metaheuristics
in general) and parallelism. Parallel algorithms represent
a very important topic in all international research
programs, and virtually any algorithm can profit from
parallelism to create efficient optimization and learning
tools capable of unseen performance in complex problems.
Working
in two domains of research is at the same time difficult
and fruitful. Knowing on parallelism
and networking helps in creating parallel algorithms
for clusters or grids of computers. 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...
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Real World Applications:
The Real World Applications (RWA) track invites submissions
that present rigorous applications of Evolutionary Computation
(EC) to real world problems.
Of particular interest are:
(1) Papers that describe advances in the field of EC
for implementation purposes.
(2) Papers that present
rigorous comparisons across techniques in a real world
application.
(3) Papers that present novel uses of EC
in the real world.
(4) Papers that present new applications
of EC to real world problems.
Domains of applications include all industries (e.g.,
automobile, biotech, chemistry, defense, finance, oil
and gas, telecommunications, etc.) and functional areas
include all functions of relevance to real world problems
(logistics, scheduling, timetabling, design, pattern
recognition, data mining, process control, predictive
modeling, etc.).
The RWA track differs from the Evolutionary Computation
Practice (ECP) workshop in that
(1) RWA only accepts papers with the same high technical
and scientific quality as that of the rest of the
GECCO track papers. ECP is generally< suitable for
researchers and managers from industry, who have less
time to write
a technical paper but still would like to present
significant successes of the technology solving a real-world
problem.
(2) Papers accepted in the RWA track will be published
in the GECCO 2008 Proceedings. Therefore, if publication
is important to you, we suggest you submit your papers
to RWA.
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Search-Based Software Engineering:
The goals of the GECCO SBSE track are to:
• Develop and extend the emerging community working on Search-Based
Software Engineering;
• Continue to inform researchers in Evolutionary Computation about
problems in Software Engineering;
• Increase awareness and uptake of Evolutionary Computation
technology within the Software Engineering community;
• Provide definitions of representations, fitness/cost functions,
operators and search strategies for Software Engineering problems.
Topics include (but are not limited to) the application of search-based algorithms for:
• Requirements engineering
• Building recommendation systems to support the development process
• Building self-healing, self-optimizing systems
• Developing highly dynamic service-oriented systems
• Network design and monitoring
• Software security
• System and software integration
• Quality assurance and testing
• Project management, control, prediction, administration and
organization
• Maintenance, change management, optimization and transformation
As an indication, `search- based' techniques are taken to include (but are not limited to):
• Genetic Algorithms
• Genetic Programming
• Evolution Strategies
• Evolutionary Programming
• Simulated Annealing
• Tabu Search
• Ant Colony Optimization
• Particle Swarm Optimization
Papers should address a problem in the software engineering domain and should approach the solution to the problem using a heuristic search st rategy. Papers may also address the use of methods and techniques for i mproving the applicability and efficacy of search-based techniques when applied to software engineering problems. While experimental results are important, papers that do not contain results, but rather present new approaches, concepts and/ or theory will also be considered.
http://www.cse.unl.edu/~myra/gecco-sbse2010/
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Theory
The GECCO 2010 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 welcomes 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.
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Carsten Witt:
He studied Computer
Science at
the University of Dortmund,
Germany, where he received
his diploma and Ph.D.
in 2000 and
2004, respectively. Since spring 2009, he is an assistant professor at
the Technical University of Denmark in Copenhagen. Carsten's main
research interests are the theoretical aspects of randomized search
heuristics, in particular evolutionary algorithms and ant colony
optimization.
More information: http://ls2-www.cs.uni-dortmund.de/~witt |
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SIGEVO Officers
| Chair: |
Darrell Whitley |
| Vice Chair: |
John R. Koza |
| Secretary: |
Una-May O’Reilly |
| Treasurer: |
Wolfgang Banzhaf |
SIGEVO Executive Committee
| Darrell Whitley (chair) |
Wolfgang Banzhaf |
| Erick Cantú-Paz |
John R. Koza |
| Dave Davis |
Riccardo Poli |
| Kalyanmoy Deb |
Franz Rothlauf |
| Kenneth De Jong |
Marc Schoenauer |
| Marco Dorigo |
Lee Spector |
| David E. Goldberg |
Dirk Thierens |
| Erik Goodman |
Una-May O’Reilly |
| John H. Holland |
Annie S. Wu |
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Organizers and Tracks:
