2003 Congress on Evolutionary Computation

Canberra, Australia, December 8-12, 2003

Accepted Special Sessions

 

1. Evolutionary Computation and Games
    Organizers: Alan Blair, David Fogel, and Risto Miikkulainen

2. Real World Applications (RWA)
    Organizers: Rajkumar Roy and Victor Oduguwa

3. Evolutionary Computation in Bioinformatics and Computational Biology
    Organizers: Gary B. Fogel and David W. Corne

4. Design Optimisation with Evolutionary Computation
    Organizer: Bernhard Sendhoff

5. Evolutionary Computation for Systems and Control Applications
    Organizers: Kit Po Wong and ZhaoYang Dong

6. Genome Informatics
    Organizer: Ikuo Yoshihara

7. Evolutionary Scheduling   
    Organizers: Edmund K. Burke, Graham Kendall and Kay Chen Tan

8. Evolutionary Multiobjective Optimization (EMO)
    Organizers: Kay Chen Tan, Kalyanmoy Deb and Andrzej Jaszkiewicz

9. Evolutionary Design Automation
    Organizer: Giovanni Squillero

10. Swarm Intelligence and its Applications
     Organizers: Xiaodong Li, Gao Liang and Gao Haibing

11. Computational Time Complexity of Evolutionary Algorithms
     Organizers: Jun He, Xin Yao and Qingfu Zhang

12. Biomolecular Computing
     Organizers: John A. Rose, Max H. Garzon and Byoung T. Zhang

13. Genetic Regulatory Networks
     Organizers: Janet Wiles and Jennifer Hallinan

14. Evolutionary Computation in Computer Security
     Organizers: Pedro Isasi and Julio César Hernández

15. Artificial Immune Systems
     Organizer: Dipankar Dasgupta

16. Evolutionary Computation in Economics
     Organizers: Edward Tsang, Shu-Heng Chen, Jerzy Korczak and Sheri Markose

 

1.    Evolutionary Computation and Games

        Organizers: Alan Blair, David Fogel, and Risto Miikkulainen

        Dr David Fogel
        Natural Selection, Inc.
        3333 N. Torrey Pines Ct., Suite 200
        La Jolla, CA 92037
        Tel: 858-455-6449
        Fax: 858-455-1560
        www.natural-selection.com

One of the fundamental mathematical constructs is the game. Formally, two or more players (of which one may be "nature") have resources to allocate and receive a payoff for their allocations. Each player may desire to maximize his or her immediate payoff, long-term payoff, or may be concerned with more complex issues that relate to other players (e.g., maximize collective payoff, ensure minimizing payoff to an opponent). Evolutionary computation has proven to be an interesting and effective tool in machine learning methods to address games of many forms. These include the iterated prisoner's dilemma, standard board games, military simulations, and other instances. Evolutionary algorithms have been used to learn effective strategies against both fixed and simultaneously evolving opponents (co-evolution), in cases of complete and also incomplete, uncertain, and noisy information about the environment of the game. Many open issues have been identified, including but not limited to the selection of evolvable representations, choosing opponents effectively to promote evolutionary learning, and the requirements for sustaining coevolutionary arms races and open-ended evolution. The special track will entertain submissions in all areas of evolutionary computation and games.

 

2.    Real World Applications (RWA)

        Organizers: Rajkumar Roy and Victor Oduguwa

        Dr Rajkumar Roy
        Department of Enterprise Integration, School of Industrial and Manufacturing Science
        Cranfield University, Cranfield, Bedford, MK43 0AL, UK
        Tel: +44 (0) 1234 754072
        Fax: +44 (0) 1234 750852
        www.cranfield.ac.uk/sims/cim/people/roy.htm

Traditional methods often employed to solve complex real world problems tend to inhibit elaborate exploration of the search space, which can be expensive and often results in sub-optimal solutions. Evolutionary Computation (EC) is generating considerable interest for solving real world engineering problems. It is proving robust in delivering global optimal solutions and helps to resolve limitations encountered in traditional methods. EC harnesses the power of natural selection to turn computers into optimization tools. The core methodologies are Genetic Algorithms (GA), Evolutionary Programming (EP), Evolution Strategies (ES) and Genetic Programming (GP). While many issues have been addressed in recent researches, limitations for wider applications of these techniques still exist that restrict more realistic solutions to be achieved. Current challenges include, for example, complexity of the search space, nature of constraints, multiple objectives, and search within integrated qualitative/quantitative space. This special track invites submissions in all areas of evolutionary computation dealing with the challenges of applying EC techniques to real world problems.

 

3.    Evolutionary Computation in Bioinformatics and Computational Biology

        Organizers: Gary B. Fogel and David W. Corne

Dr Gary B. Fogel
Natural Selection, Inc.
3333 N. Torrey Pines Ct., Suite 200
La Jolla, CA 92037
USA
Tel: (858) 455-6449
Fax: (858) 455-1560
www.natural-selection.com

Dr David W. Corne
Department of Computer Science
School of Systems Engineering
The University of Reading
P.O. Box 225
Whiteknights, Reading
Berkshire RG6 6AY
United Kingdom
Tel +44 (0)118 931 8983
Fax +44 (0)118 975 1994
www.personal.rdg.ac.uk/~ssscorne/

Bioinformatics and computational biology present a number of difficult optimization problems with large search spaces. Recent applications of evolutionary computation in this area suggest that they are well-suited to this area of research. This special session will highlight applications of evolutionary computation to a broad range of topics including drug docking, protein folding, sequence alignment, genomics, proteomics, metabolics, medicine, and ecological modeling. Particular interest will be directed towards novel applications of evolutionary computation to problems in these areas.

 

4.    Design Optimisation with Evolutionary Computation

        Organizer: Bernhard Sendhoff

Dr Bernhard Sendhoff
Honda R&D Europe (Deutschland) GmbH
Carl-Legien-Strasse 30
D-63073 Offenbach/Main
Germany
Tel: +49 (0)69-8 90 11-0, Extension -736
Fax: +49 (0)69-8 90 11-749

Design optimisation has become one of the primary areas of application of evolutionary computation in recent years. With the strong increase of available computing power even complex evaluations, like three-dimensional and dynamical simulations using multi-physics solvers became feasible. At the same time, meta-models with online learning have been successfully used for design optimisation. Hybrid approaches combining "traditional" methods like design of experiments with evolutionary algorithms show promising results. Systems which are able to incorporate human decisions allow the usage of expert knowledge in design optimisation in the engineering sense as well as the application of evolutionary optimisation to subjective evaluations in the area of aesthetic design and art.

This session is dedicated to the application of evolutionary computation to design optimisation. Problems are discussed which are either particular to this field or which are addressed in the context of design optimisation. Topics include but are not limited to:

(1) Design of 2D or 3D objects for different applications, examples of which are: aerodynamic structures (wing shapes, turbine blades, etc.), structures for static and dynamic stability (automobile frames for crash tests), objects of everyday use (aesthetic design optimisation), multi-disciplinary and multi-physics optimisation (aerodynamic plus mechanic plus electrodynamic constraints).

(2) Issues from evolutionary computations: the choice of the representation (splines, NURBS), the choice of the operators (self-adaptation) and the comparison of existing evolutionary approaches (ES, GA, GP).

(3) Hybrid and multi-level optimisation, for example adaptive representations, knowledge incorporation into evolutionary optimisation in the framework of design optimisation or the combination of design of experiments methods and optimisation.

(4) The use of meta-models and their integration in the evolutionary algorithms. The combination of online learning of meta-models and evolutionary computation.

(5) The parallelisation of existing algorithms for design optimisation.

(6) The incorporation of multiple criteria like robustness or manufacturing costs in the evolutionary search.

(7) Special constraints for open and closed-loop experimental design optimisation.

 

5.    Evolutionary Computation for Systems and Control Applications

        Organizers: Kit Po Wong and ZhaoYang Dong

Professor Kit Po Wong
Department of Electrical Engineering
The Hong Kong Polytechnic University
Hung Hom, Kowloon
Hong Kong
Tel: +852 - 2766 6168
Fax: +852 - 2330 1544
www.ee.polyu.edu.hk

Dr ZhaoYang Dong
School of Information Technology and Electrical Engineering
The University of Queensland, St Lucia
QLD 4072, Australia
Tel: +61-7-3346 9052
Fax: + 61-7 3365 4999
www.itee.uq.edu.au/~zdong

Evolutionary computing techniques are capable of solving global optimization and search problems with robust performance. The advances in evolutionary computation techniques are making them more popular in solving complex, nonlinear, nonconvex and dynamically interactive problems. Evolutionary computation has been successfully applied in the areas of power systems engineering, including system planning, security assessment, decision making, electricity market management and control; Evolutionary computation has also been used to solve sophisticated control systems and communications engineering problems, such as system identification, linearization, optimal and robust control.

The objective of this special session is to bring together research and development of evolutionary computation in system and control areas. The topics of interests are (but not limited to) evolutionary identification and modelling, evolutionary control system design, industrial applications, robotics and sensors, learning and optimization, evolutionary computation application in power systems engineering and communications.

 

6.    Genome Informatics

        Organizer: Ikuo Yoshihara

Professor Ikuo Yoshihara
Department of Computer Science and Systems Engineering,
Faculty of Engineering, Miyazaki University
1-1, Gakuen-Kibanadai-Nishi, Miyazaki, 889-2192, Japan
Tel & Fax: +81-985-58-7384
http://www.cs.miyazaki-u.ac.jp/index-e.html

This special session aims at collecting currently on-going research in the field of genome informatics, which is an approach to reveal a mechanism of life system coded by genes and evolution of creatures and to construct intelligent database. The genome informatics must make a breakthrough in biology, medical science, pharmacy, agriculture, engineering, physics, and human science; moreover evolutionary computation itself.

To develop a powerful tool to acquire knowledge and rules from enormous volumes of gene data is required. Evolutionary computation is one of the promising approaches for that requirement. Topics include (but are not limited to) gene network, gene expression, genome sequence, gene database, gene database mining, cluster pairs, E-cell, 3-D structure of protein, prediction of gene function, relation between DNA and hormone and protein, hybrid techniques etc.

 

7.    Evolutionary Scheduling

        Organizers: Edmund K. Burke, Graham Kendall and Kay Chen Tan

Professor Edmund K. Burke
School of Computer Science and Information Technology
University of Nottingham
Nottingham NG8-2BB, UK
Tel: +44 (0)115 951 4206
Fax: +44 (0)115 951 4249
http://www.cs.nott.ac.uk/~ekb/

Dr Graham Kendall
School of Computer Science and Information Technology
University of Nottingham
Nottingham NG8-2BB, UK
Tel: +44 (0) 115 846 6514
Fax: +44 (0) 115 951 4249
http://www.cs.nott.ac.uk/~gxk

Dr Kay Chen Tan
Department of Electrical and Computer Engineering
National University of Singapore
4 Engineering Drive 3, Singapore 117576,
Republic of Singapore
Tel: (65) 6874 2127
Fax: (65) 6779 1103
http://vlab.ee.nus.edu.sg/~kctan

The session will cover all aspects of evolutionary scheduling and related issues. We would hope to attract a balance of applied and theoretical papers from across the evolutionary computing and meta-heuristic research communities. Typical examples of such problems include rostering, machine scheduling, timetabling, vehicle routing, resource assignment, planning, etc. This special track invites submissions in all areas of evolutionary scheduling and metaheuristics.

All submissions or enquiries to this special session should be emailed to Prof. Edmund K. Burke (ekb@Cs.Nott.AC.UK) or Dr Graham Kendall (gxk@cs.nott.ac.uk) or Dr Kay Chen Tan (eletankc@nus.edu.sg).

 

8.    Evolutionary Multiobjective Optimization (EMO)

        Organizers: Kay Chen Tan, Kalyanmoy Deb and Andrzej Jaszkiewicz

Dr Kay Chen Tan
Department of Electrical and Computer Engineering
National University of Singapore
4 Engineering Drive 3, Singapore 117576,
Republic of Singapore
Tel: (65) 6874 2127
Fax: (65) 6779 1103
http://vlab.ee.nus.edu.sg/~kctan

Kalyanmoy Deb
Department of Mechanical Engineering
Indian Institute of Technology Kanpur
Kanpur, PIN 208 016, India
Tel: +44 121 4148556
Fax: +44 121 4144281
http://www.iitk.ac.in/kangal/deb.htm

Dr Andrzej Jaszkiewicz
Institute of Computing Science
Poznan University of Technology
Piotrowo 3a
60-965 Poznan, POLAND
Tel: (+48 61) 6652 371
Fax: (+48 61) 8771 525
http://www-idss.cs.put.poznan.pl/~jaszkiewicz/

Evolutionary techniques for multiobjective optimization have been gaining significant attentions from researchers in various fields, which are reflected by the overwhelming number of participants in recent EMO related conferences and special sessions. The main aim of this session is to bring together both experts and newcomers working on EMO algorithms to discuss different issues including (among others) the following:

(1) Real-world applications of EMO algorithms.

(2) Comparison of different EMO algorithms.

(3) Test functions, performance metrics, and benchmarks for EMO algorithms.

(4) Theoretical aspects of EMO, such as convergence, diversity, elitism, and complexity analysis of EMO algorithms.

All submissions or enquiries to this special session should be emailed to Dr Kay Chen Tan (eletankc@nus.edu.sg).

 

9.    Evolutionary Design Automation

        Organizers: Giovanni Squillero

Dr Giovanni Squillero
Politecnico di Torino
Dipartimento di Automatica e Informatica
Corso Duca degli Abruzzi 24
I-10129 Torino
ITALY
Tel: +39-011564.7092
Fax: +39-011564.7099
http://www.cad.polito.it/~squiller/

IEEE publishes an average of 20 papers each year where evolutionary techniques are exploited to solve design automation problems. Concurrently, the field of evolutionary computation reveals a significant interest in evolvable hardware and problems such as routing, placement, or test pattern generation.

The special session on Evolutionary Design Automation in CEC2003 will show the latest developments in the field of evolutionary algorithms applied to design automation. Design and test professionals will confront the challenges the industry faces, and learn how these challenges may be addressed exploiting innovative evolutionary techniques developed in academia.

The session will cover all evolutionary computation techniques applied to design automation, including (but not limited to):

For additional information please refer to the special session web page (http://www.cad.polito.it/eda03/) or contact the organizer (giovanni.squillero@polito.it).

 

10.   Swarm Intelligence and its Applications

        Organizers: Xiaodong Li, Gao Liang and Gao Haibing

Dr Xiaodong Li
Lecturer
Computer Science Department
RMIT University
GPO Box 2476v, Melbourne, 3001
Phone: +61 3 99259585

Dr Gao Liang and Dr Gao Haibing
School of Mechanical Science & Engineering, Huazhong
University of Science & Technology,
Wuhan, China, 430074
Tel: 862787543871; 862787556924
Fax: 862787543074

Swarm Intelligence (SI) is an AI technique that focuses on studying the collective behaviour of a decentralised system made up by a population of simple agents interacting locally with each other and with the environment. Although there is typically no centralised control dictating the behaviour of the agents, local interactions among the agents often cause a global pattern to emerge. Examples of systems like this can be found abundant in nature, including ant colonies, bird flocking, animal herding, honey bees, bacteria, and many more. This kind of "swarm-like" algorithm, such as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), have already been applied successfully to solve real-world optimization problems in engineering and telecommunication. SI models have many features in common with Evolutionary Algorithms (EAs). Like EA, SI models are population-based. The system is initialised with a population of individuals (i.e., potential solutions). These individuals are then manipulated over many generations by ways of mimicking social behaviour of insects or animals, in an effort to find the optima. Unlike EA, SI models do not use evolutionary operators such as crossover and mutation. A potential solution simply "flies" through the search space by modifying itself according to its relationship with other individuals in the population and the environment.

This special session will highlight the latest development in this rapidly growing research area of Swarm Intelligence. Authors are invited to submit their original and unpublished work in the areas including (but not limited to) the following:

 

11.   Computational Time Complexity of Evolutionary Algorithms

        Organizers: Jun He, Xin Yao and Qingfu Zhang

It has been recognised in the evolutionary computation (EC) community that EC theory is lagging behind applications. However, progresses have been made in recent years on a number of theoretical topics, e.g., convergence of evolutionary algorithms (EAs), (local) convergence rate of EAs, modified schema theorems, fitness landscape analysis, etc. Some insights have been gained through such studies. Interestingly, most mainstream computer scientists regard computational time complexity as a key issue in the analysis of algorithms, while few such complexity results exist in EC. This special session aims at bringing researchers who are interested in the computational time complexity of EAs together to review the current state-of-art, exchange the latest ideas and explore future directions. The major topics of interest include, but not limited to,

All submissions or enquiries to this special session should be sent to:

    Dr. Jun He
    School of Computer Science
    The University of Birmingham
    Edgbaston, Birmingham B15 2TT, UK
    Email: j.he@cs.bham.ac.uk
    http://www.cs.bham.ac.uk/~jxh
or

    Dr. Qingfu Zhang
    Department of Computer Science
    University of Essex
    Wivenhoe Park, Colchester CO4 3SQ, UK
    Email: qzhang@essex.ac.uk

Email submissions of PDF files are strongly encouraged!

 

12.   Biomolecular Computing (Call for Papers)

        Organizers: John A. Rose, Max H. Garzon and Byoung T. Zhang

            Dr John A. Rose
            Information and Computer Science
            University of Tokyo
            7-3-1 Hongo, Bunko-ku, Tokyo, JAPAN
            http://hagi.is.s.u-tokyo.ac.jp/johnrose/

            Prof. Max H. Garzon
            Computer Science, The University of Memphis
            Memphis, TN 38152, U.S.A.
            http://www.cs.memphis.edu/~garzonm

            Prof. Byoung T. Zhang
            Computer Science and Engineering
            Seoul National University, Korea
            http://bi.snu.ac.kr/~btzhang/

Biomolecular computing is a rapidly growing, interdisciplinary field, which focuses on the development, application, and discovery of biopolymer-based techniques for applications in computation, biotechnology and bioinformatics. Papers are sought in all areas relating to biomolecular computing, including (but not restricted to)

Of particular interest will be submissions that report experimental and/or simulation results. All submissions will be peer reviewed by referees expert in the topic. Accepted papers will be invited for presentation, and will be included for publication in the CEC 2003 proceedings. Publication requires at least one author to register for and attend CEC 2003 in order to present the accepted work. People with little experience in the field can benefit from a tutorial, where recent developments will be surveyed. To increase further exchange, discussion with experts will be fostered in a workshop on a specialized topic (conventional computing tools for biomolecular computing).

 

13.   Genetic Regulatory Networks

        Organizers: Janet Wiles and Jennifer Hallinan

            A/Prof Janet Wiles
            School of ITEE
            University of Queensland
            QLD 4072, Australia
            http://www.itee.uq.edu.au/~cogsci/

            Dr. Jennifer Hallinan
            IMB, University of Queensland
            QLD 4072, Australia

EC has much to learn from the way natural systems engineer complexity. Genetic regulatory networks are systems of genes which interact to switch each other on and off. They have been called the operating systems of cells. This session will cover both theoretical and empirically based models of genetic regulatory networks and their properties.

 

14.   Evolutionary Computation in Computer Security

        Organizers: Pedro Isasi and Julio César Hernández

  Dr Pedro Isasi
  Artificial Intelligence Group
  Carlos III University
  Avda. Universidad, 30
  28911 Leganés, Madrid, Spain
  Tel: +34 91 624 94 55
  Fax: +34 91 624 91 29

            Dr. Julio César Hernández
            Computer Security Group
            Carlos III University
            Avda. Universidad, 30
            28911 Leganés, Madrid, Spain
            Tel: +34 91 624 94 99
            Fax: +34 91 624 91 29

Techniques taken from the field of Evolutionary Computation (specially Genetic Algorithms and Genetic Programming, but also others) are steadily becoming more and more present in the area of computer security, both in network/host security and in the very demanding area of cryptology. In recent years, algorithms which take advantage of approaches based on Evolutionary Computation have been proposed, for example, in the design and analysis of a number of new cryptographic primitives, ranging from pseudorandom number generators to block ciphers, in the cryptanalysis of state-of-the-art cryptosystems, and in the detection of network attacking patterns, to name a few. There is a growing interest from the computer security community towards Evolutionary Computation techniques, as a result of these recent successes, but there still are a number of open problems in the field that should be addressed. This special session will try to do it by asking for submissions in all areas of evolutionary computation dealing with applications to computer security, and by giving the interested researchers an opportunity to review the current state-of-art of the topic, exchange recent ideas, and explore promising new directions.

For additional information, please visit the special session webpage at http://tracer.uc3m.es/CFP-SS-CEC2003.html

 

15.   Artificial Immune Systems

        Organizer: Dipankar Dasgupta

  Dipankar Dasgupta
  Division of Computer Science
  The University of Memphis
  Memphis, TN 38152, USA
  Ph: 901-678-4147, Fax: 901-678-2480
  Email: dasgupta@memphis.edu

The biological immune system is a complex, adaptive, pattern-recognition system that defends the body from foreign pathogens. It is a distributed system with several functional components for multi-level defense. The system uses learning, memory, and associative retrieval to solve recognition and classification tasks. In particular, it learns to recognize relevant patterns, remember patterns that have been seen previously, and use combinatorics to construct pattern detectors efficiently. Also, the overall behavior of the system is an emergent property of many local interactions. The biological immune system is a great source of inspiration for developing intelligent problem-solving techniques. Like other biologically motivated approaches (Neural Networks, Genetic Algorithms, etc.), the Artificial Immune System is also a rapidly emerging field. Artificial Immune Systems are used in pattern recognition, fault detection, computer security, and a variety of other applications in science and engineering. This session will provide a great opportunity for presenting and disseminating latest work in the field of Artificial Immune Systems.

 

16.   Evolutionary Computation in Economics

        Organizers:  Edward Tsang, Shu-Heng Chen, Jerzy Korczak and Sheri Markose

  Professor Edward Tsang
  Department of Computer Science
  University of Essex
  Colchester CO4 3SQ UK
  Tel: +44 1206 872774
  Fax: +44 1206 872788
  Email: edward@essex.ac.uk
  URL: http://cswww.essex.ac.uk/CSP/edward

Evolutionary computation has been established as a useful tool for studying economics and finance. Example applications include financial forecasting, real and artificial stock markets creation, micro-behaviour analysis, stock trading and portfolio optimization, market dynamics, game theory, risk analysis and many other areas. All of these areas are built on firm economic foundations. However, the applicability of most economic theories is limited by their simplifying assumptions. Advances in computing, in both hardware and algorithms, enable researchers to study economics and finance with a completely different approach. For example, one can seriously attempt to recognize patterns in complex systems, simulate complex agents' behaviour in market environments, study the interaction of complex strategies, study algorithmic strategies in game theory, analyze volatility in financial markets, etc. This session will accept papers in evolutionary computation applications in economics and finance, including, but not limited to, the above mentioned areas.

 

Call for Special Sessions Proposals