CONFERENCE
CHAIR:
Una-May
O'Reilly,
CSAIL, MIT. 
EDITOR IN CHIEF:
Hans-Georg
Beyer 
WORKSHOPS AND LATE BREAKING
PAPERS CHAIR:
Franz Rothlauf 
COMPETITIONS CHAIR:
Simon
Lucas 
BUSINESS COMMITTEE:
David E. Goldberg 
Erik Goodman 
John R. Koza 
Riccardo
Poli 
LOCAL ARRANGEMENTS:
Co-Chairs:
R. Paul Wiegand 
Ronald W.
Morrison 
STUDENT WORKSHOP
GRADUATE:
Michael O'Neill 
UNDERGRADUATE :
Laurence D. Merkle 
EVOLUTIONARY COMPUTATION IN PRACTICE:
Cem Baydar, Accenture: 
Tina Yu, Chevron: 
-----------------------------------------
PROGRAM
TRACKS:
| A-Life,
Evolutionary Robotics and Adaptive Behavior: more
info |
Hod
Lipson  |
| Ant
Colony Optimization and Swarm Intelligence: more
info |
Christian
Blum  |
| Artificial
Immune Systems: more
info |
Dipankar
Dasgupta  |
| Biological
Applications: more
info |
James
A. Foster 
Wolfgang Banzhaf  |
| Coevolution: more
info |
Edwin
De Jong  |
Estimation
of Distribution Algorithms: more
info
|
Martin
Pelikan  |
Evolutionary
Combinatorial Optimization:
more
info |
Gunther
Raidl  |
Evolutionary
Multiobjective Optimization -Co-Chairs:
|
Kalyanmoy
Deb 
Eckart Zitzler  |
| Evolutionary
Strategies, Evolutionary Programming: more
info |
Dirk
Arnold  |
| Evolvable
Hardware: more
info |
Andy
Tyrrell  |
| Genetic
Algorithms: more info |
Erick
Cantu-Paz  |
| Genetic
Programming: more
info |
Terry
Soule  |
Learning
Classifier Systems and Other Genetics-Based Machine
Learning more info
|
Xavier
Llora  |
| Meta-heuristics
and Local Search: more info |
Jean-Paul
Watson  |
| Real
World Applications: more info: |
Eric
Bonabeau  |
| Search-based
Software Engineering: more
info |
Spiros
Mancoridis  |
ISGEC EXECUTIVE BOARD:
Erik
D. Goodman (chair)
David Andre
Wolfgang Banzhaf
Kalyanmoy Deb
Kenneth De Jong
Marco Dorigo
David E. Goldberg
John H. Holland |
John
R. Koza
Una-May O'Reilly
Ingo Rechenberg
Marc Schoenauer
Lee Spector
Darrell Whitley
Annie S. Wu
|
A-Life,
Evolutionary Robotics and Adaptive Behavior:
This track examines evolutionary computation as model for understanding
natural systems and for generating biologically-inspired artificial
systems. From artificial models of biological systems, to the synthesis
of "life" on artificial media; from self-organizing, self-replicating,
and self-learning structures, to bio-inspired adaptive robots and
mobile
agents; This area deals with algorithmic, synthetic, empirical, and
theoretical advances in artificial systems inspired by evolution,
biology, and life.
|
Ant
Colony Optimization and Swarm Intelligence:
Swarm intelligence (SI) algorithms take their inspiration from the
collective behaviour of, for example, ant colonies, flocks of birds,
or fish schools. Examples are algorithms for clustering and data
mining inspired by ants' cemetery building behaviour, dynamic task
allocation algorithms inspired by the behaviour of wasp colonies,
particle swarm optimization (PSO) algorithms, and many more. A
particularly successful strand of SI is ant colony optimization (ACO).
The inspiring source of ACO is the foraging behavior of real ants,
which works via an indirect communication between the ants by means of
pheromone trails. Their foraging behaviour allows them to find
shortest paths between their nest and food sources. This functionality
of real ant colonies is exploited in artificial ant colonies in order
to solve discrete (as well as continuous) optimization problems, but
also to solve tasks such as routing and load balancing arising in
networks. This track invites submissions that present original work on
ACO/SI algorithms. Submissions in the following areas of ACO/SI
research are encouraged:
- applications of ACO/SI algorithms to real-world problems
- applications of ACO/SI algorithms to scientific test cases
- new theoretical results on ACO/SI
- new swarm intelligence algorithms
- new hybrids between ACO/SI algorithms and other methods for
optimization
- biological foundations of ACO/SI
- models of the behavior of social insects
|
Artificial
Immune Systems:
The field of artificial immune
systems (AIS) is an emerging area, which
explores and employs different immunological mechanisms in order to
solve computational problems. This special track will provide a great
opportunity for presenting and disseminating latest work in the field
of Artificial Immune Systems. Papers in this track would include (but
are not limited to):
· Computational
models of the Immune System,
· Extensions
or improvements of existing AIS models,
· Applications
of Immunity-Based Techniques,
· Combination
of AIS with other soft computing paradigms
· Hardware
implementations of AIS
· Immunoinformatics,
etc.
|
Biological
Applications:
The scope of this track will be any research applying genetic and
evolutionary computation to biological hypotheses and data. GEC uses
the process of evolution as an algorithmic heuristic, and so GEC
provides an algorithmic approach to answering biological questions. All "flavors" of
GEC are included in this scope: genetic algorithms,
genetic
programming, evolution strategies, evolutionary programming, and hybrid
systems with any of these components.
Some specific appropriate biological issues that GEC may address
include:
• Data mining in biological data bases
• Sequence alignment
• Phylogenetic reconstruction
• Gene expression and regulation, alternative splicing
• Functional diversification through gene duplication and exon
shuffling
• Structure prediction for biological molecules (structural
genomics
and proteomics)
• Network reconstruction for development, expression, catalysis
etc.
• Dynamical system approaches to biological systems
• Simulation of cells, viruses, organisms and whole ecologies
• Sensitivity of speciation to variations in evolutionary
processes
• Relationships between evolved systems and their environment
(phylogeography, e.g.)
• Relationships within evolved communities (cooperation,
coevolution,
symbiosis, etc.)
|
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 2005 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
For detailed information, see http://www.cs.uu.nl/~dejong/geccocoev05.html
|
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.
This track invites submissions that present original work on EDAs with
the focus on theory and applications of EDAs, the design of new EDAs,
and the improvement of existing EDAs. More specifically, submissions
in the following areas of EDA research are encouraged:
- EDA theory (modeling, prediction, limitations)
- EDA applications (interesting artificial and real-world problems)
- efficiency enhancement of EDAs
- empirical studies of EDAs
- theoretical and empirical comparison of EDAs and other optimization
methods
- design of new EDAs
- design of hybrid methods by combining EDAs with other optimization
methods
- adaptation of operators/parameters in EDAs
|
Evolutionary
Combinatorial Optimization:
Evolutionary algorithms have often been shown to be effective
for difficult combinatorial optimization problems appearing in
various industrial, economical, and scientific domains. Prominent
examples of such problems are scheduling, timetabling, network
design, transportation and distribution problems, vehicle routing,
traveling salesperson, other graph problems, satisfiability, packing
problems, planning problems, and
general mixed integer programming.
Topics of interest include, but are not limited to:
- Applications of evolutionary algorithms and related nature-inspired
meta-heuristics like memetic algorithms or ant colony optimization
to combinatorial optimization problems;
- hybrid methods and hybridization techniques;
- representation techniques;
- evolutionary operators;
- constraint-handling techniques;
- parallelization;
- theoretical developments;
- search space analyses;
- comparisons to other (also non-evolutionary) techniques.
|
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
|
Evolvable
Hardware:
Evolvable hardware techniques enable self-reconfigurability and
adaptability of programmable devices and thus have the potential to
significantly increase the functionality of deployed hardware systems.
Evolvable Hardware is expected to have major impact on future system
designs. Evolvable hardware is also expected to greatly enrich the
area of commercial applications in which adaptive information
processing is needed; such applications range from human-oriented
hardware interfaces and internet adaptive hardware to automotive
applications.
Evolvable Hardware is an emerging field that applies evolution to
automate design and adaptation of physical structures such as
electronic systems, antennas, MEMS and robots. The aim of this track
is to bring together leading researchers from the evolvable hardware
community, representatives of the automated design and programmable/
reconfigurable hardware communities, and end-users from the aerospace,
military and commercial sectors. Contributions dealing with theory,
techniques, and performance evaluation, are solicited, but not limited
to, the following:
- Intrinsic and on-line evolution
- Hardware/software co-evolution
- Novel devices, testbeds and tools supporting evolvable hardware
- Adaptive computing and adaptive hardware
- Real-world applications of evolvable hardware.
|
Genetic Algorithms
:
This track invites submissions that present
original work on genetic
algorithms. We welcome submissions on theory, design of new GAs
(including representations and operators), improvements of existing
algorithms, comparisons with other methods, empirical evaluations, and
other topics relevant to GAs.
|
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. A sample of papers of papers from the GP track
for GECCO-2004 includes: "pi Grammatical Evolution" by Michael
O'Neill
et al. "Evolving Caching Strategies for the Internet" by Juergen
Branke
et al., "A Descriptive Encoding Language for Evolving Modular Neural
Networks" by Jae-Yoon Jung and James A. Reggia, and "Evolving
Quantum
Circuits and Programs through Genetic Programming" by Paul Massey
et
al.
|
Learning Classifier Systems and Other Genetics-Based Machine Learning:
Since the inception of learning classifier systems (LCS) by John
Holland,
learning paradigms driven by genetic algorithms (GA) have shown their
competence on a broad spectrum of fields and applications. In a broader
spectrum, genetics-based machine learning (GBML) systems have
successfully tackled the creation of cognitive models, classification
and
prediction systems, and anticipatory behavior.to mention a few.
Recently,
GBML has been experiencing an important renaissance thanks to two
key
factors: (1) the new GA theoretical achievements have provided a
better
understanding of the underlying complex mechanisms used, and (2)
the
successful applications of such systems to real-world problems such
as
data mining.
The track for LCS and other GBML encourage the authors to submit
papers
encompassing one or more of the areas suggested below.
(a) Theoretical advances in LCS and GBML
Theoretical analysis of mechanisms
Identification of learning, anticipation, and scalability bounds
Connections to ML theory
Analysis of the influence of noise
Complexity measures for maze environments
LCS and reinforcement learning
(b) Systems and frameworks
Michigan style (SCS, NewBoole, EpiCS, ZCS, XCS .)
Pittsburgh style (GABIL, GIL, COGIN, REGAL, GA-Miner, GALE, MOLCS,
GAssist.)
Anticipatory LCS (ACS, ACS2, YACS, MACS .)
Incremental evolutionary rule learning
Genetic-based inductive learning, genetic fuzzy systems, learning
based on estimation of distribution algorithms
Hybrids (XCS with messy coding, s-expressions, or neural network
conditions .)
(c) Problems
Competent operators design and implementation
Aliasing states in static environments
Dynamic environments and time series learning
Encapsulation
Default hierarchies
Data sampling
Knowledge representations, extraction and inference
Building block identification and linkage learning for GBML systems
(d) Application areas
Rapid application development frameworks for GBML
Data mining
Bioinformatics and life sciences
Robotics, engineering, hardware/software design, and control
Stock market analysis, trading, and prediction
|
Meta-Heuristics
and Local Search:
The distinction between evolutionary/nature-inspired algorithms and
meta-heuristics such as tabu search, scatter search,
and variable neighborhood search is largely counterproductive. Despite
surface-level dissimilarities, the central themes
underlying these two types of heuristic are nearly identical, e.g.,
intensification versus diversification, mechanisms for
escaping local optima, intelligent design of move/mutation/crossover
operators, and the structure of the fitness landscape.
We invite submissions that explore research topics at the intersection
of
these two historically isolated communities, such
as the design of hybrid algorithms and cross-paradigm comparisons of
algorithm performance. We are especially interested in
papers that demonstrate either fundamental similarities or
differencesbetween the different approaches, with the goal of
understanding the types of problems or sub-problems for which particular
techniques are most appropriate.
|
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
2005
Proceedings. Therefore, if publication is important to you, we suggest
you
submit your papers to RWA.
|
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. Below is a list of the best papers
from GECCO 2002 and 2003.
GECCO 2002: Improving Evolutionary Testing by Flag Removal, Mark
Harman, Lin Hu, Robert Hierons, Andre Baresel, Harmen Sthamer
GECCO 2003: Modeling the Search Landscape of Metaheuristic Software
Clustering Algorithms, Brian Mitchell, Spiros Mancoridis
|
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