Program Tracks:
Three
days
of presentations of the latest
high-quality results in 16
separate and independent
program tracks specializing
in various aspects of genetic
and evolutionary
computation.
General
Chair:
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Conor Ryan -  |
Editor-in-Chief:
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Maarten Keijzer -  |
Business
Committee:
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Wolfgang
Banzhaf - 
Erik Goodman - 
John R. Koza -  |
Workshops
Chair:
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Marc Ebner -  |
Tutorials Chair: |
Jano Van Hemert - |
Competitions
Chairs:
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Terry Soule - 
Robert Heckendorn - |
Late
Breaking Papers Chair:
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Mike Cattolico -  |
Graduate
Student Workshop Chair:
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Steve Gustafson -  |
Evolutionary
Computation in Practice Chairs:
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David Davis - 
Jorn Mehnen - |
Program Tracks and Chairs:
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Martin Pelikan - 
Fernando Lobo -
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Julian Miller - 
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Evolution
Strategies, Evolutionary Programming:
more info:
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Nikolaus
Hansen - 
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Evolutionary Combinatorial Optimization:
more info: |
Frank Neumann - http://www.mpi-inf.mpg.de/~fne/ |
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Daniel Howard - 
Adrian Stoica -
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Genetics-Based
Machine Learning and Learning Classifier
Systems:
more info:
|
John Holmes - 
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Bioinformatics
and Computational Biology:
more info:
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Jason H.
Moore - 
Clare Bates Congdon -
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Artificial
Life, Evolutionary Robotics, Adaptive
Behavior, Evolvable Hardware:
more
info:
|
Gregory S. Hornby - 
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Ant
Colony Optimization, Swarm Intelligence,
and Artificial Immune Systems:
more info:
|
James Kennedy - 
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Evolutionary
Multiobjective Optimization:
more info:
|
Kalyanmoy
Deb - 
El Ghazli Talbi -
|
|
Jordan Pollack - 
|
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Kumara Sastry - 
|
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Giulio Antoniol - / 
|
|
|
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Sanjeev Kumar - 
Ken Stanley -  |
SIGEVO Officers
Chair: |
Darrell Whitley |
Vice
Chair: |
John R. Koza |
Secretary: |
Una-May O'Reilly |
Treasurer: |
Wolfgang Banzhaf |
SIGEVO Executive Committee
Wolfgang Banzhaf |
John R. Koza |
Erick Cantú-Paz |
Una-May O’Reilly |
Dave Davis |
Riccardo Poli |
Kalyanmoy Deb |
Franz Rothlauf |
Kenneth De Jong |
Marc Schoenauer |
Marco Dorigo |
Lee Spector |
David E. Goldberg |
Dirk Thierens |
Erik Goodman |
Darrell Whitley |
John H. Holland |
Annie S. Wu |
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:
• 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
• Parallelization and other efficiency-enhancement techniques
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.

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Genetic Programming:
Genetic Programming (GP) is the automatic induction of computer programs
and other variable-size structures representing executable programs or
computable functions from a high-level statement of a < problem through
evolutionary algorithms. This track invites submissions of original work
in all areas and derivatives of GP. Areas of interest include: theory,
algorithm design, and novel representations, operators and algorithms.
Authors interested in submitting manuscripts are encouraged to look at
previous years' papers.

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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
• interesting 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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Evolutionary Combinatorial Optimization:
The aim of the track "Evolutionary Combinatorial
Optimization" is to provide a
forum for high quality research on
metaheuristics
for combinatorial optimization problems.
Metaheuristics have often been shown
to be successful for difficult combinatorial
optimization
problems appearing in various industrial,
economical, and scientific domains.
Prominent examples of metaheuristics
are evolutionary
algorithms, ant colony optimization,
tabu search, memetic algorithms, iterated
local
search, and estimation of distribution
algorithms. Successfully solved problems
include scheduling,
timetabling, network design, transportation
and distribution problems, graph problems,
satisfiability, and packing problems.
Scope
Submission concerning all kinds of metaheuristics
for combinatorial optimzation problems are
encouraged.
Topics include (but not limited to):
• Applications of metaheuristics to combinatorial
optimization problems
• Representation techniques
• Neighborhoods and efficient algorithms
for searching them
• Variation operators
• Constraint-handling techniques
• Hybridization techniques
• Theoretical investigations
• Comparisons between different (also
exact) techniques

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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, 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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Genetics-Based
Machine Learning and Learning Classifier
Systems:
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 methods can be applied to all 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 evolutionary
algorithms, 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 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.
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 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
• Incremental 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...)
• 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
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
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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 systems 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.
(This track was formerly named "Biological
Applications" and still encompasses
the same biological application areas
as before.)
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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Artificial
Life, Evolutionary Robotics, Adaptive Behavior,
Evolvable Hardware:
Much exciting research in recent years has shown that just as
ideas from evolution can be appropriated to create strong algorithms,
these algorithms can in turn be used to address issues in the biological
realm.
For instance, artificial life research explores questions regarding
the necessary and sufficient conditions for lifelike processes
to emerge in software. Evolutionary robotics seeks to instantiate
lifelike and intelligent behaviors in mechanical devices. Evolvable
hardware strives to bring the fluid, robust and adaptable capabilities
of biological processes to bear on the problem of creating and
maintaining electronic devices.
The aim of this track is to provide a forum for exploring the
bidirectional conversation between biology and computer science.
A sampling of more specific issues include
• Necessary and sufficient conditions for simulating lifelike
processes
• The role of emergence in complex systems
• Maximizing adaptability and robustness in automatically designed systems
• General principles underlying adaptive behavior
• The role of biological realism in artificial life research

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Ant Colony Optimization,
Swarm Intelligence, and Artificial Immune
Systems.
Swarm Intelligence (SI) and Artificial Immune Systems (AIS) are computing
techniques that take their inspiration from natural phenomena. 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, or dynamic task allocation algorithms
inspired by the behavior of wasp colonies.
AIS 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.
Submissions of original and previously unpublished work in the following
areas of ACO/SI/AIS research are encouraged:
• applications of these algorithms to real-world problems
• applications of these algorithms to scientific test cases
• new theoretical results
• new computational models and techniques
• new hybrids between these algorithms and other methods
• biological foundations
• models of the behavior of social insects

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Evolutionary Multiobjective
Optimization (EMO):
EMO is one of the largest and most significant emergent fields of research
and application in the area of genetic and evolutionary computation.
Major research, starting around the beginning of nineties, the EMO field
has now matured to have standard algorithms, public domain as well as
commercial softwares, and many real-world case studies. A significant
edge of EMO over classical multiobjective optimization is its ability
to find a number of trade-off optimal solutions in a single simulation.
The knowledge of many high-performing trade-off solutions not only provides
a better decision-making power for choosing a particular solution for
implementation, the flexible yet efficient search power of EMO and the
information hidden in multiple solutions can also be utilized in other
kinds of problem solving tasks effectively. This year's GECCO track on
EMO will provide a platform for researchers and applicationists to come
and discuss their latest studies in all aspects of EMO by submitting
papers through GECCO paper submission guidelines.
The EMO track encourages papers related
to, but not limited to, the following
aspects of EMO:
1. Theoretical aspects of EMO
2. Test problems, dynamic EMO, scheduling EMO, robust EMO, reliability-based
EMO, EMO for handling noise
3. New EMO algorithms and comparative studies
4. EMO performance measures and analysis
5. Show-casing EMO for better decision-making through applications
6. EMO for other kinds of problem-solving tasks -- through use of additional
objectives
7. EMO for machine learning, data-mining etc.
8. Hybrid EMO and decision-making
9. Hybrid EMO and classical MCDM methodologies
10. EMO in other evolutionary computing strategies
11. Interactive EMO
12. Review of a specific EMO aspect
EMO tracks within GECCO are usually
well-attended. This will be an excellent
opportunity to present and discuss your
research/application with experts and
participants of GECCO and the EMO track.

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Coevolution:
Coevolution offers the potential to address problems for which no accurate
evaluation function is known. Rather than following a fixed approximation
of the unknown true evaluation function for a problem, the coevolutionary
evaluation of an individual depends on other evolving individuals. The
optimization process can thereby adaptively construct its own evaluation
function.
Coevolution can be an effective approach for problems where performance
can be measured using tests, as well as for problems in which multiple
components that make up a whole are to be co-adapted. In addition to
these forms of optimization, the adaptive nature of the evaluation process
in coevolution may in principle give rise to a self-propelled and open-ended
evolutionary process.
It has been found early on that the dynamic evaluation of coevolution
can lead to unreliability. In recent years however, the possible goals
for coevolutionary algorithms have become better understood, and for
several algorithms theoretical properties have been provided. These developments
generate the exciting prospect that practical reliable algorithms for
coevolution may now be within reach.
The Coevolution Track of the Genetic and Evolutionary Computation
Conference provides a venue where researchers from all directions and
approaches to coevolution can meet. Submissions on any aspect of coevolution
are encouraged, including but not limited to the following:
• Applications
• Measuring progress
• Game-theoretic studies
• New coevolutionary algorithms
• The structure of coevolution problems
• Empirical studies of coevolutionary methods
• Behavioral dynamics of coevolutionary setups
• Theoretical guarantees for coevolutionary algorithms
• Empirical comparisons between coevolutionary and other methods

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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.

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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 to:
Requirements engineering
System and software design
Implementation
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
Development processes
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 experim ental 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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Formal Theory:
The aim of this track is to provide
a forum for strong theory papers that
up to now were scattered over different
tracks. With the current track, we increase
the visibility of these works also towards
the traditional algorithm theory community.
The definition of a theory paper is as
follows.
A theory paper considers a particular
randomized search heuristic on a particular
problem or class of problems. It contains
formally stated theorems which are proven
rigorously. It is not enough to validate
a model or a theory by experiments.

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Generative and Developmental Systems:
Generative and Developmental Systems concern the use,construction or
evolution of genotype-phenotype mappings that involve either re-writing,
iteration, or time, or environmental interaction, either for the simulation
of biological development, solution of computational or design problems,
or to increase the scalability and/or the evolvability of evolutionary
algorithms.

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