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Committees and Program Tracks
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GECCO Organizers
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Conference Chair:
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Mike Cattolico
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Proceedings Editor-in-Chief:
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Maarten Keijzer
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Business Committee:
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David E. Goldberg
Erik Goodman
John R. Koza
Una-May O'Reilly
Mike Cattolico
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Workshops Chair:
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Jano van Hemert
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Late Breaking Papers Chair:
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Jörn Grahl
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Competitions Chair:
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Riccardo Poli
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Student Workshop Chair:
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Terry Soule
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Evolutionary Computation in Practice Chair:
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Cem Baydar,
Tina Yu, Memorial University
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Publicity Chair:
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John Koza
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Program Tracks and Chairs
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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.
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Ant Colony Optimization and Swarm Intelligence:
Swarm intelligence (SI) algorithms take their inspiration from the collective
behaviour of social insects such as ants, bees, and wasps, as well as from other animal societies such as flocks of birds, or fish schools. Examples are
algorithms for clustering and data mining inspired by ants' cemetery building behaviour, or dynamic task allocation algorithms inspired by the behaviour of
wasp colonies. The advantage of these approaches over traditional techniques is often their robustness and flexibility.
Two popular swarm intelligence
techniques for optimization are ant colony optimization (ACO) and particle swarm optimization (PSO).
The inspiring source of ACO is the foraging behavior of
real ants, whereas PSO is inspired by the social behaviour of fish schools and bird flocks.
Submissions of original and previously unpublished work 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 SI techniques
- new hybrids between ACO/SI algorithms and other methods for
optimization
- biological foundations of ACO/SI
- models of the behavior of social insects
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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.
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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.)
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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 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.eecs.harvard.edu/~sevan/gecco_coev/
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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.
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
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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.
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Evolutionary Multiobjective Optimization
Although most real-world problems have several (and normally conflicting) objectives
that have to be satisfied at the same time, for the sake of simplicity, we tend to transform all but one of those objectives into constraints in order to
simplify the optimization task.
Vilfredo Pareto stated in 1896 a concept (known today as "Pareto optimum")
that constitutes the origin of research in multiobjective optimization. According to this concept, the solution to a multiobjective optimization problem is
normally not a single value, but instead a set of values (also called the Pareto set).
The interest of applying evolutionary computation techniques to multiobjective
optimization dates back to the 1960s, with Rosenberg's doctoral dissertation. One of the reasons why evolutionary algorithms are so suitable for multiobjective
optimization is because they can generate a whole set of solutions (the Pareto set) in a single run rather than requiring an iterative process like traditional
mathematical programming techniques.
The interest on Evolutionary Multiobjective Optimization (EMO) is reflected by the
high volume of publications in this topic in the last few years (over 128 PhD theses, more than 545 journal papers, and more than 1236 conference papers). So,
the aim of this track organized within the 2006 Genetic and Evolutionary Computation Conference (GECCO'2006) is to provide a forum to exchange ideas and
discuss current research on all aspects of evolutionary multiobjective optimization. Both experts and newcomers working on EMO are welcome to submit their
original papers on all aspects of evolutionary multiobjective optimization, which include (but are not limited to) the following topics:
Real-world applications of EMO algorithms
Test functions for EMO algorithms
New EMO techniques
Metrics for EMO algorithms
Techniques to keep diversity in the population of an EMO algorithm
Comparison of EMO techniques
Theoretical aspects of EMO algorithms
Uncertainty management in EMO algorithms
Parallel issues of EMO algorithms
Interactive EMO techniques
Hybridization of EMO algorithms with mathematical programming techniques
http://www.cs.cinvestav.mx/~EVOCINV/gecco2006/
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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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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.
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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.
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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 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.
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Learning Classifier Systems and Other Genetics-Based Machine Learning:
Since the inception of learning classifier systems (LCS) by John Holland in the
1970s, learning paradigms driven by genetic algorithms (GA) have shown their competence on a broad spectrum of fields and applications. Genetics-based machine
learning (GBML) systems have successfully tackled the creation of classification and prediction systems, control architectures, cognitive models, and adaptive
behavior, just to mention a few. Recently, GBML has been experiencing a strong renaissance thanks to three key factors: (1) advancements 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) advancements 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 datamining and control problems h ave confirmed thestrength, robustness,
and broad applicability of the GBML approach.
During GECCO 2006, the LCS&GBML track is designed to encompass researchers from
machine learning applyingevolutionary computation techniques in their systems as well as researches from evolutionary computation that utilize other machine
learning techniques in their systems. The exchange of expertise is highly encouraged. The track sessions during the conference will focus on the hybrid and
interactive nature of the presented systems.
Submissions
The LCS and other GBML track encourages submissions encompassing, but not
limited to, one or more of the areas suggested below.
- Theoretical Advances in LCS and 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) enviro nments
- Complexity analysis in MDP and PoMDP problems
- Evolutionary algorithm combined with reinforcement learning or other
estimation techniques
- Systems and Frameworks
- Incremental evolutionary rule learning, including but not limited to:
- Michigan style (SCS, NewBoole, EpiCS, ZCS, XCS...)
- 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
- Other hybrids combining evolutionary techniques with other machine learning
techniques
- 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
- Application Areas
- Data mining
- Bioinformatics and life sciences
- Robotics, engineering, hardware/software design, and control
- Cognitive systems
- Rapid application development frameworks for GBML
- Dynamic environments
- Time series and sequence learning
Further information:http://www-illigal.ge.uiuc.edu/~butz/LCSaoGBML2006/
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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
(2) Papers accepted in the RWA track will be published in the GECCO 2006
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 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
http://www.dcs.shef.ac.uk/~phil/sbse2006/
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