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:

General Chair:
Franz Rothlauf  -   
Editor-in-Chief:
Günther Raidl  -   
Business Committee:
Wolfgang Banzhaf   -   
Erik Goodman   -   
Una-May O'Reilly  -   
Publicity Chair: Martin Pelikan  -   
Workshops Chair:
Anna I. Esparcia  -   
Competitions Chairs:
Pier Luca Lanzi  -     
Tutorials Chair: Martin V. Butz  -   
Late Breaking Papers Chair:
Ying-ping Chen   -   
Local Chair: Christian Gagné  -   
Evolutionary Computation in Practice Chairs:
Thomas Bartz-Beielstein   -   
David Davis   -  
Jörn Mehnen  -   
Graduate Student Workshop Chair: Steve Gustafson   -  
Undergraduate Student Workshop Chair: Clare Bates Congdon   -  
Larry Merkle   -  
Frank Moore   -  

Program Tracks and Chairs:

Artificial Life, Evolutionary Robotics, Adaptive Behavior, Evolvable Hardware:     
more info:
Giovanni Squillero    -   
Rolf Drechsler   -   
Ant Colony Optimization and Swarm Intelligence:
more info:
Thomas Stützle    -   
Mauro Birattari    -    
Bioinformatics and Computational Biology:
more info:
Clare Bates Congdon    -   
Martin Middendorf    -    
Combinatorial Optimization and Metaheuristics:
more info:
Christian Blum    -   
Carlos Cotta    -   
Estimation of Distribution Algorithms:
more info:
Peter Bosman     -    
Jörn Grahl     -      
Evolutionary Multiobjective Optimization :
more info:
Joshua Knowles    -   
David Corne    -   
Evolutionary Strategies and Evolutionary Programming:
more info:
Hans-Georg Beyer    -   
Generative and Developmental Systems:
more info:
Ken Stanley    -   
Julian F. Miller    -   
Genetic Algorithms:
more info:
Jano van Hemert    -   
Tom Lenaerts     -    
Genetic Programming:
more info:
Marc Ebner    -   
Genetics-Based Machine Learning:
more info:
Jaume Bacardit    -   
Real World Applications:
more info:
Michael O'Neill    -   
Search Based Software Engineering:
more info:
Massimiliano Di Penta    -   
Theory:
more info:
Benjamin Doerr    -   
Thomas Jansen    -   
Riccardo Poli    -   
Parallel Evolutionary Systems:
more info:
Enrique Alba    -   

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.



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/



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


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



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


Evolutionary Multiobjective Optimization:

EMO is concerned with the formulation and the solution, by heuristic means, of all types of vector-valued optimization and search problems. EMO builds naturally on some of the themes of genetic and evolutionary computation - robustness, adaptation, diversity and niching, exploration/exploitation, and the deep connections between optimization and learning - to search large problem spaces for *sets* of optimal "tradeoffs".

The EMO track at GECCO recognizes the full gamut of research effort in this now broad area, including but not limited to the following suggested topics:

- Research on all types of iterative search method applied to multiobjective problems, including GAs, EP, GP, ESs, memetic algorithms, stochastic local search methods, PSO, ACO, differential evolution, EDAs, PMBLAs and others;
- Theory related to EMO algorithms, performance measures, order relations or problem characteristics;
- Real-valued optimization problems with multiple criteria;
- Combinatorial and mixed-integer problems with multiple criteria;
- Multiobjective constraint satisfaction problems;
- Constrained problems treated in a multiobjective way, or multiobjective and constrained problems;
- Multi-competence cybernetics, or studies of multiobjective adaptation in natural or artificial life systems;
- EMO in online optimization problems and optimal control;
- EMO for robust optimization problems;
- Theoretical or empirical studies on any component of EMO methods, e.g. niching, archiving and elitism, selection, local improvement;
- Special representations for EMO problems;
- Special search operators for EMO problems;
- Integration of decision-making (MCDA) in EMO approaches;
- Preference articulation methods combined with search, and preference measures on sets;
- Incorporation of machine learning into EMO algorithms;
- Multiobjective problems with expensive-to-evaluate functions;
- Multiobjective problem decomposition including objective-dimensionality reduction;
- Theory of set-valued optimization;
- Studies on scalability of EMO approaches, including many-objective optimization;
- Solving single-objective problems using EMO approaches;
- Parallel models and implementations of EMO approaches;
- Dynamic, stochastic and noisy optimization problems involving or making use of multiple objectives;
- EMO applications and case studies in any sphere, e.g. engineering, design, telecommunications and IT, robotics, machine learning and data mining, computational biology, operations management, and others;
- Hybrid methods, especially combinations of evolutionary and non-evolutionary approaches to MO;
- Empirical performance comparisons of EMO approaches and/or comparisons with non-EMO approaches;
- Test functions and problems for benchmarking EMO methods.

As you can see, there are many objectives for the field to pursue, and despite some solid foundations, we are still a long way from the Pareto front! We thus look forward to your contributions.

[NB: Whilst we do not wish to exclude any relevant papers a priori, we would like to warn authors that the EMO track is usually very large. It may be better in some circumstances to submit your paper elsewhere if its *main* subject is not really EMO, or if it would fit well in another track. Contact the track chairs if you are unsure.]

 


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


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.

 


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.


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


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


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.


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.


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


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...


 

 
 Genetic and Evolutionary Computation Conference (GECCO-2009)
GECCO 2008 site       GECCO 2007 site     GECCO 2006 site      GECCO 2005 site         GECCO 2004 site    
GECCO 2003 site       GECCO 2002 site         GECCO 2001 site      GECCO 2000 site      GECCO 1999 site