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Program Tracks: Three
days of presentations of the latest high-quality results in 15
separate and independent program tracks specializing in various
aspects of genetic and evolutionary computation.
GECCO Organizers:
Program Tracks and Chairs:
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 |
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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Ant Colony Optimization and Swarm
Intelligence:
Swarm Intelligence (SI) deals with natural and artificial
systems composed of a large number of individuals that
generate collective behaviors using decentralized control
and self-organization.
These behaviors are a result of the local interactions
of the individuals with each other and with their environment.
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, or fish schools.
Two popular swarm intelligence techniques for optimization
are ant colony optimization (ACO) and particle swarm optimization
(PSO). Other examples include algorithms for clustering
and data mining inspired by ants' cemetery building behavior,
ordynamic task allocation algorithms inspired by the behavior
of wasp colonies.
Submissions of original and previously unpublished work
in the following areas of ACO and, more in general, SI
research are encouraged:
• applications of ACO, PSO, and SI algorithms to real-world problems • applications
of these algorithms to scientific test cases •
new computational models and techniques •
new hybrids between these algorithms and other methods •
biological foundations •
models of the behavior of natural and artificial SI systems •
new theoretical
results
Track website: http://iridia.ulb.ac.be/gecco2009/
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Bioinformatics and Computational
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,
computational biology and systems biology, for example, are particularly welcome.
Papers that provide experimental validation and/or biological interpretation
of computational results are also particularly welcome.
Some specific examples of biological and biomedical
issues that papers could address include:
Data mining in biological or biomedical databases
• 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
• Metabolomics
• 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 alignment and analysis
• Simulation of cells, viruses, organisms and whole ecologies
• Structure prediction for biological molecules (structural biology)
• Systems biology
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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
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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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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;
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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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Evolutionary Strategies, Evolutionary
Programming:
Both 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, both 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 that may include, but is not limited to,
theoretical and empirical evaluations of ES/EP, improvements
and modifications to the algorithms, and applications
of ES/EP to benchmark problems and test function suites.
Particularly encouraged are submissions with focus on
• Adaptation mechanisms
• Iinteresting ES/EP applications
• ES/EP theory
• ES/EP in uncertain and/or changing environments
• Comparisons of ES/EP with other optimization methods
• Hybrid strategies
• Meta-strategies
• Constrained and/or multimodal problems
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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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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
•
Design of new GAs (including representation and operators)
•
Improvements over existing algorithms
•
Comparisons with other methods / empirical performance
analysis
•
Hybrid approaches
•
New application areas
•
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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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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Genetics-Based Machine Learning:
Machine Learning (ML) presents an array of paradigms -- unsupervised,
semi-supervised, supervised, and reinforcement learning -- which frame wide
range of clustering, classification, regression, prediction and control tasks.
Evolutionary and other bio-inspired methods such as Artificial Immune Systems
(AIS) can be applied to all of them and we refer to any such work as Genetics-Based
Machine Learning (GBML). The GECCO GBML track encompasses all work on machine
learning problems using these techniques, whether alone or hybridised with
other machine learning methods. Indeed, submission of work on hybrid systems,
and work which otherwise develops connections with non-evolutionary/AIS ML
methods, are encouraged.
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. Recently, GBML has been experiencing a strong
renaissance thanks to three key factors: (1) advances in GA theory have not
only deepened the understanding of evolutionary learning and optimization but
have also enabled the successful analysis of GBML systems; (2) advances in
machine learning theory and understanding have enabled further successful and
robust combinations of machine learning with evolutionary computation techniques
(3) successful applications of GBML systems to real-world problems such as
data mining and control problems have confirmed the strength, robustness, and
broad applicability of the GBML approach.
AIS are another family of techniques included in this track with long history
and very active development. These techniques take inspiration of different
immunological mechanisms in vertebrates in order to solve computational problems.
Various aspects of these mechanisms have been used to develop algorithms for
distributed and adaptive control, machine learning, pattern recognition, fault
detection, computer security, optimization, and distributed system design.
The GBML track encourages submissions including but not limited to the following
areas.
1. Theoretical Advances in GBML
• 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. Systems and Frameworks
• 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
3. System Enhancements
• 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
for GBML systems
• Integration of other machine learning techniques
4. Application Areas
• Data mining
• Bioinformatics and life sciences
• Robotics, engineering, hardware/software design, and control
• Cognitive systems and cognitive modelling
• Rapid application development frameworks for GBML
• Dynamic environments
• Time series and sequence learning
• Artificial Life
• Adaptive behaviour
• Large scale datasets
• Network security
• Other kinds of real-world applications
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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.
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Theory
The GECCO 2009 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.
For more info, please visit: http://www.mpi-inf.mpg.de/~doerr/gecco2009.html
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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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