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Special Sessions

Special sessions have been both a tradition and an essential aspect of IEEE CEC. With the aim of bringing together researchers on a specific topic, such sessions are organised by renowned experts in the field across the globe. The IEEE CEC 2009 Programme Committee solicits proposals for special sessions that are encompassed within the technical scope of the conference. Papers submitted for these sessions will be peer-reviewed with the same criteria used for other contributed papers. All accepted papers in the special sessions will be included in the published conference proceedings.

Interested researchers are invited to submit a proposal, which should include the session title, a brief description of the scope and motivation, names, contact information, and brief CVs of the organisers. It should be made clear in the application why a special session is needed and how it fits within the wider scope of the conference. The final submission deadline for proposals is 1st September 2008, however we are accepting proposals at any time up to that date.

For enquires, please contact the Special Sessions Chair Dr. Jon Timmis or submit your proposal via email to Dr Timmis. The accepted special sessions will be posted and updated here regularly.

Accepted Special Sessions

Session Title & OrganisersCall For Papers (PDF)
Agent Based Memetic Algorithms
Ruhul Sarker, Michela Milano & Andrea Roli
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Artificial Biochemical Networks
Michael Lones
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Cartesian Genetic Programming (CGP)
James Alfred Walker & Julian Francis Miller
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Computational Intelligence in Games
Pier Luca Lanzi, Daniele Loiacono & Julian Togelius
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Concurrent Approaches to Collaboration in Evolutionary Computation
Paul Andrews, Adam Sampson, Fiona Polack & Susan Stepney
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Differential Evolution
Uday K. Chakraborty
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Evolutionary Algorithms Based on Probabilistic Models
Qingfu Zhang, José Antonio Lozano, Pedro Larrañaga & Aimin Zhou
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Evolutionary Computation in Finance and Economics
Pedro Isasi & Asuncion Mochon
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Evolutionary Computation in Bioinformatics and Computational Biology
Alioune Ngom & Clare Bates Congdon
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Evolutionary Computation in Dynamic and Uncertain Environments
Shengxiang Yang, Hans-Georg Beyer, Yaochu Jin & Ponnuthurai N. Suganthan
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Evolutionary Computation in Network-on-Chip Based Systems
Nadia Nedjah & Luiza de Macedo Mourelle
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Evolutionary Computation in Scheduling and Planning
Lam T. Bui, James M. Whitacre & Hussein A. Abbass
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Evolutionary Computation in Space and Air
Massimiliano Vasile, Oliver Schuetze, David W. Corne & Bruce Conway
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Evolutionary Computer Vision
Vic Ciesielski, Mario Koeppen & Mengjie Zhang
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Evolutionary Development
Till Steiner, Gunnar Tufte & Markus Olhofer
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Evolutionary Games on Complex Networks
Akira Namatame & Jun Tanimoto
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Evolutionary Robotics
Patricia A. Vargas, Sabine Hauert, Dario Floreano & Phil Husbands
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Exploiting the Computational Properties of the Immune System: Applications and Algorithms
Emma Hart & Julie Greensmith
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Hardware Aspects of Bio-Inspired Architectures and Systems
Andrew J. Greensted & Martin A. Trefzer
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Hybrid Evolutionary Algorithms in Forecasting Models
Wei-Chiang Samuelson Hong & Edward Tsang
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Linkage in Evolutionary Computation
Ying-Ping Chen, Chuan-Kang Ting, Pier Luca Lanzi & Meng-Hiot Lim
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Memetic Algorithms for Hard to Solve Problems
Ferrante Neri, Pablo Moscato & Hisao Ishibuchi
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Parallel Bio-inspired Optimisation Methods: Algorithms and Applications
Dr Andrew Lewis, Dr Sanaz Mostaghim & Dr Marcus Randall
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Parameter Setting in Evolutionary Algorithms
Selmar Smit, Evert Haasdijk & Gusz Eiben
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PerAda: Adaptation Strategies for Pervasive Adaptation
Ben Paechter, Emma Hart & A.E. Eiben
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Performance Assessment of Constrained / Bound Constrained Multi-Objective Optimization Algorithms (Competition papers only)
Qingfu Zhang & P. N. Suganthan
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PERPLEXUS: Pervasive reconfigurable platform for modelling complex systems
Andres Upegui & Andres Perez-Uribe
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Techniques for Online and Distributed Evolutionary Computation
Dr Daniele Miorandi, Dr Lidia Yamamoto, Dr Emma Hart & Dr Tina Yu
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Theoretical Foundations of Evolutionary Computation
Benjamin Doerr & Frank Neumann
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What is Computational Swarm Intelligence?
T. Blackwell, M. Bishop & Slawomir Nasuto
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Agent Based Memetic Algorithms

Memetic Algorithms (MAs) are one of the recently growing research areas in Evolutionary Algorithms (EAs). Memetic algorithms are a general name for a broad class of population-based heuristics that is capable of local refinements. Recent studies have revealed that MAs are successful on a wide variety of real world problems. Particularly, they converge to high quality solutions more efficiently compared to their conventional systematic counterparts.

A multi-agent system (MAS) is composed of multiple interacting agents, possibly equipped with intelligent capabilities. By agents here, we typically mean software agents. Recently, multi-agent systems are increasingly used for solving problems which are difficult or impossible for an individual agent. They are also used as a programming and software development paradigm. In a problem solving multi-agent system, agents usually have some of the basic properties and characteristics of usual MASs, such as autonomy, local view, social ability (communications), learning and adaptive ability.

A population in MAs can simply be thought as a collection of agents. In addition, since MAs are hybrid techniques, as they incorporate both population-based and local search metaheuristics possibly combined with tree-search techniques, MASs are indeed a powerful framework for modelling, designing and implementing them. By integrating the agent concept in MAs, we can enhance the performance of MAs as evident in the literature. The agents can bring many interesting features in MAs which are beyond the scope of traditional evolutionary process and learning.

The aim of this special session is to reflect the most recent advances in the field, and increase the awareness of the computing community at large on this effective technology. In particular, we endeavor to demonstrate the current state-of-the-art in the theory and practice of Agent based MAs. Topics of interest include (but are not limited to):

  • Novel frameworks of Agent based MAs (AMAs)
  • Analytical and/or theoretical studies that enhance our understanding of AMAs
  • Design of multi-agent architecture within AMAs
  • Design of agent communication and learning strategy
  • Analysing the affect of agent type, architecture, cooperation, communication and learning on the overall performance of AMAs
  • Convergence and complexity analysis of AMAs
  • AMAs for global, constrained, dynamic and large scale optimization
  • Multi-objective AMAs
  • Multi-method local search in AMAs
  • Hybrid search strategies in AMAs
  • Real-world applications of AMAs

Artificial Biochemical Networks

  • Session Organisers: Michael Lones
  • Call For Session Papers: PDF

Biological organisms appear to be orders of magnitude more complex than present-day computational systems, yet current understanding of their genomes suggests they consist of only a relatively small number of functional components. This highlights that the source of biological complexity lies not within the individual gene products but rather within their interactions and consequent organisation into biochemical networks.

Biochemical networks are able to carry out very complex behaviours using relatively few functional components, and they do this using a genetic representation that is inately evolvable. In essence, this is exactly what is desired when evolving computation within an evolutionary algorithm: complex behaviours, a small search space, and an evolvable representation. This has led to a growing interest in computational models based upon the structure and behaviour of biochemical networks, such as computational models of genetic regulatory networks (GRNs), artificial chemistries, and cell signalling models.

These artificial biochemical network models feature a number of related behaviours, such as self-organisation, feedback, and complex dynamics, and consequently they face a number of common issues: such as methods of programming and input-output coupling, the use of appropriate levels of abstraction, and techniques for analysing, understanding and visualising their behaviour.

This session aims to bring together researchers interested in artificial biochemical networks and other computational models motivated by the behaviours of biological cells. We welcome submissions reporting theoretical or empirical results for both standalone models and those used within the context of evolutionary algorithms.

Topics of interest include, but are not limited to:

  • Computational models of genetic regulatory networks
  • Algorithmic/Computational chemistries
  • Computational models of cell signalling
  • Programmability, scalability, evolvability and fault tolerance
  • Dynamical systems analysis and mathematical modelling
  • Implementations and applications, in both hardware and software
  • Uses within computational development

Cartesian Genetic Programming (CGP)

  • Session Organisers: James Alfred Walker & Julian Francis Miller
  • Call For Session Papers: PDF

Cartesian Genetic Programming is a form of genetic programming developed by Julian Miller and Peter Thomson in 1997. In its classic form, CGP represents a program as a directed graph using a very simple integer-based representation. In a number of studies, it has been shown to be comparatively efficient when compared to other GP techniques. Since then, the classical form of CGP has been enhanced in various ways to include automatically defined functions (ADFs), multiple chromosomes, and most recently, self-modification and crossover operators. In addition, CGP has also been applied to a number of novel and real-world applications in academia and industry.

This is the first special session given on this increasingly popular form of GP. The aim of this special session is to reflect recent advances and state-of-the- art developments in CGP, to allow the opportunity for CGP practitioners to discuss key issues and exchange new ideas regarding CGP, to raise awareness and the profile of CGP to a wider audience, and to encourage the growth of the CGP community. The submission of technical and position papers is invited on all aspects of CGP. Topics of interest include, but are not limited to:

  • Applications of CGP to novel and real-world problems
  • Alternative representations and operators for CGP
  • Exploiting modularity and problem decomposition in CGP
  • Novel CGP frameworks
  • Algorithms using CGP for development and development-based CGP
  • Analytical, empirical or theoretical analysis that enhances the
  • Hybridisation of CGP with other evolutionary and bio-inspired
  • CGP performance benchmarks and comparisons
  • Evolutionary art, sculpture, and music using CGP
  • Scalability of CGP
  • CGP for learning and game-playing
  • CGP algorithms and applications using GPUs

Computational Intelligence in Games

  • Session Organisers: Pier Luca Lanzi, Daniele Loiacono & Julian Togelius
  • Call For Session Papers: PDF

Games are an ideal domain to study computational intelligence methods in that they provide cheap, competitive, dynamic, reproducible environ ments suitable for testing new search algorithms, pattern based evaluation methods or learning concepts. At the same time they are interesting to observe, fun to play, and very attractive to students.

Computational techniques have successfully been applied to many different kinds of games, however many research issues are still open. The proposed session aims at getting together leading researchers and practitioners in this field who study and apply computational intelligence methods to computer games. In the context of IEEE CEC 2009 this special session will specifically focus on those methods that in different ways exploit techniques from the area of genetic and evolutionary computation, e.g., genetic algorithms, evolutionary strategies, genetic programming, classifier systems, artificial life, artificial immune systems, etc. Topics of interest include but they are not limited to:

  • Learning and adaptation in games
  • Knowledge representation in games
  • Neuro-evolution in games
  • Coevolution in games
  • Opponent modelling in games
  • Knowledge-free and self-learning algorithms in games
  • Challenges for CI in games
  • Theoretical or empirical analysis of CI algorithms
  • Representations for games
  • Comparative studies (e.g. CI versus human-designed players)
  • Multi-agent and multi-strategy learning
  • Board and card games
  • Economic or mathematical games
  • Imperfect information and non-deterministic games
  • Evasion (predator/prey) games
  • 3D computer and console games
  • "Realistic" games for simulation or training purposes
  • Games for mobile platforms
  • Games involving control of physical objects (e.g. remote control car racing)
  • Games involving physical simulation

Concurrent Approaches to Collaboration in Evolutionary Computation

  • Session Organisers: Paul Andrews, Adam Sampson, Fiona Polack & Susan Stepney
  • Call For Session Papers: PDF

The study of concurrency encompasses a wide range of techniques including the use of concurrent programming languages and algorithms and the distribution of programs across multiple hosts. Concurrency is appealing in nature-inspired computing for both philosophical and practical reasons. The world around us is highly concurrent, so concurrent programming techniques provide a natural way to model entities and interactions in the real world. Evolutionary computing and bio-inspired agent-based techniques can be very resource intensive especially for large problems. Concurrency can enable us to harness the huge computing power available today in multi-core processors, distributed PC clusters and other parallel systems.

Many bio-inspired computing approaches, such ants and swarms, make use of interacting and collaborative elements that produce emergent behaviours to perform their allotted tasks. These approaches are ideally suited to exploit the power of concurrency to produce performance gains or tackle intensive problems. Their constituent elements can often be parallelised or distributed, with communication between elements occurring where necessary. The concurrency techniques employed to achieve this are often generic, applicable to many different collaborative evolutionary computing techniques.

We will be interested in receiving both technical and position papers that make use of concurrent languages, algorithms or distributed techniques in the following subjects:

  • Bio-inspired Agent-based systems
  • Alife models
  • Collective behaviour
  • Co-evolution
  • Autonomous systems
  • Bio-inspired robotics
  • Swarm behaviours

Differential Evolution

  • Session Organisers: Uday K. Chakraborty
  • Call For Session Papers: PDF

Differential evolution, an attractive global optimization method with relatively fewer parameters, is a relatively new member of the evolutionary computation family. This method has recently been shown to produce superior results in a wide variety of real-world applications. This special session seeks to highlight the latest developments in this rapidly emerging research area by bringing together researchers and practitioners. Authors are invited to submit their original and unpublished work to this Special Session. Topics of interest include, but are not limited to:

  • Theory of differential evolution
  • Analysis of parameter settings (scale factor, crossover rate, population size)
  • Multi-objective differential evolution
  • Differential evolution for noisy problems
  • Differential evolution for constrained optimization
  • Hybridization (with local search and other soft computing approaches)
  • Connections to / comparison with particle swarm optimization
  • Connections to other soft computing paradigms (e.g., ANN, Fuzzy systems)
  • Applications in diverse domains

Evolutionary Algorithms Based on Probabilistic Models

Evolutionary algorithms based on probabilistic models (EAPM) have been recognized as a new computing paradigm in evolutionary computation. Instances of EAPMs include, estimation of distribution algorithms, probabilistic model building genetic algorithms, ant colony optimization, cross entropy methods, to name a few. There is no traditional crossover or mutation in EAPMs. Instead, they explicitly extract global statistical information from their previous search and build a probability distribution model of promising solutions, based on the extracted information. New solutions are sampled from the model thus built. EAPMs represent a new systematic way to solve hard search and optimization problems. The last decade has seen growing interest in this area. As an interdisciplinary research area, the development of EAPMs needs joint efforts from the researchers and practitioners in evolutionary computation, machine learning, statistics and simulation. This special session aims at bringing researchers who are interested in EAPM together to review the current state-of-art, exchange the latest ideas and explore future directions. The major topics of interest include, but are not limited to:

  • Theory of EAPMs,
  • New algorithms,
  • Combination of machine learning techniques and EAPMs,
  • Combination of statistics techniques and EAPMs,
  • Combination other heuristics and EAPMs,
  • EAPMs for multiobjective optimization problems,
  • EAPMs in dynamic environments,
  • Parallel implementation of EAPMs,
  • Real-world/novel applications.

Evolutionary Computation in Finance and Economics

Both financial and economics problems are more frequently being explore with Evolutionary Computation (EC) techniques. Theses methods have been proven to be a powerful tool in domains were analytic solutions are not a good alternative. Problems in real world involve complexity, noisy environments, imprecision, uncertainty and vagueness. For this reason EC techniques are needed in order to solve problems related to these areas. So far it has been successfully used for estimating econometric parameters, macroecomics models, replicating laboratory results with human subjects, searching equilibriums, studying the emergence of the representative agent and rational expectations, designing public policy, in financial engineering, risk management, portfolio optimization, industrial organization, auctions, experimental economics, financial forecasting, market simulation or agent-based computational economics among many other areas.

Evolutionary Computation in Bioinformatics and Computational Biology

  • Session Organisers: Alioune Ngom & Clare Bates Congdon
  • Call For Session Papers: PDF

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. Particular interest will be directed towards novel applications of evolutionary computation to problems in these areas.

The bioinformatics special session has been a part of CEC since 1999 and is soliciting high quality papers of original research and application papers that have not been published elsewhere and are not under consideration for publication elsewhere. All papers will be rigorously reviewed by at least 2 reviewers. Accepted papers will be published in the CEC proceedings. There is a clear interest in both the computational intelligence comunity and biology communities for this special session.

Evolutionary Computation in Dynamic and Uncertain Environments

Many real-world optimization problems are subjected to dynamic and uncertain environments that are often impossible to avoid in practice. For instance, the fitness function is uncertain or noisy as a result of simulation/measurement errors or approximation errors (in the case where surrogates are used in place of the computationally expensive high fidelity fitness function). In addition, the design variables or environmental conditions many also perturb or change over time. For these dynamic and uncertain optimization problems the objective of the evolutionary algorithm is no longer to simply locate the global optimum solution, but to continuously track the optimum in dynamic environments, or to find a robust solution that operates optimally in the presence of uncertainties. This poses serious challenges to conventional evolutionary algorithms.

Evolutionary Computation in Network-on-Chip Based Systems

Network-on-Chip (NoC) is an emerging paradigm for communications within large VLSI systems implemented on a single silicon chip. IT is used as a new approach to designing complex System-on-a-chip (SoCs) design. NoC-based systems can accommodate multiple complex SoC designs. In a NoC-based system, modules such as processor cores, memories and specialized IP blocks exchange data using a on-chip network. An NoC is constructed from multiple point-to-point data links interconnected by switches also called routers, such that messages can be relayed from any source module to any destination module over several links, by making routing decisions at the switches.

VLSI designers of NoC-based systems face several problems, among which we can cite, for instance, planning the architecture that is most suitable to a given application is order to improve performance and mapping the sub- systems that form the application into a multiple nodes of the NoC architecture. Evolutionary computation can be used as a very robust tool to bring some answers to this kind of design problems.

The aim of this special session is to bring together hardware, middleware and application designers that exploit the evolutionary computation principles to provide CAD tools for NoC-based systems. This session will allow researchers to share experiences and identify theoretical and technical issues in this field of expertise. Submitted papers may describe applications, computing models, modeling frameworks, or hardware platforms and architecture.

Evolutionary Computation in Scheduling and Planning

Scheduling and planning problems are generally complex, constrained and multi-objective. The application of evolution-inspired, meta-heuristic and other soft computing techniques to this problem domain has received considerable attention from the research community.

This special session on Evolutionary Computation in Scheduling and Planning (ECSP) seeks to bring together researchers from around the globe for a creative discussion on recent advances and challenges facing ECSP research.

Evolutionary Computation in Space and Air

In the Aeropsace Sciences, many applications require the solution of global single and multi-objective optimization problems or problems with mixed variables and non-differentiable quantities. From global trajectory optimization to multidisciplinary aircraft and spacecraft design, from planning and scheduling for autonomous vehicles to the synthesis of robust controllers for airplanes or satellites, evolutionary based techniques have become an important tool for tackling these kinds of problems providing interesting solutions. Not only has this given the way to application of evolutionary computation but has led also to the development of new approaches.

In most of the cases the basic evolutionary heuristics have been hybridized with other techniques, such as gradient methods or branch and prune methods, or modified to better adapt to the specific application under investigation. This has led to the creation of new heuristics, new meta-heuristics or new hybridizations that have proven to be very effective.

Evolutionary Computer Vision

Computer vision is a major unsolved problem in computer science and engineering. Over the last decade there has been increasing interest in using evolutionary computation approaches to solve vision problems. Computer vision provides a range of problems of varying difficulty for the development and testing of evolutionary algorithms.

The theme of the proposed special session is the use of evolutionary computation for solving computer vision and image processing problems. This special session seeks to highlight the latest developments in this research area by bringing together researchers and practitioners in both evolutionary computation and computer vision. Authors are invited to submit their original and unpublished work to this Special Session.

Evolutionary Development

This special session of the IEEE 2009 CEC Congress on Evolutionary Computation will focus on the design and analysis of developmental systems in evolutionary computation.

Over the recent years researchers in the evolutionary computation comunity have created an increasing number of evolutionary developmental systems with varying levels of complexity. Much attention has been paid to the creation of these systems and the evaluation of their abilities to produce large, complex, modular, and robust phenotypes.

Due to the inherent complexity of developmental systems and of the created solutions, the analysis of developmental processes and their outcome proves to be very difficult. Results often have to be restricted to basic experimental status, whilst a detailed understanding of the dynamics in the system is frequently not available. To address these issues and to lead science towards an enhanced understanding of the processes involved in artificial EvoDevo systems, a revision of methods and tools for analysis seems necessary.

Concepts and ideas for the development of these methods might be found by simultaneously integrating multiple scales, combining for example the dynamics on developmental timescale with dynamics on evolutionary timescale, or for multi-cellular representations, the behaviour of single cells, groups of cells, and the resulting character of the phenotype.

The aim of this special session is to promote discussion of evolutionary developmental systems with a focus on their analysis and understanding, as well as to suggest possible approaches to exploit the features unique to developmental systems with respect to system design.

Topics of interest include but are not limited to:

  • analysis and modeling of dynamic systems for genotype-phenotype mapping
  • generative systems
  • analysis and modeling of regulatory networks for evolutionary development
  • multi-scale and multi-level modeling of evolutionary development
  • regulatory vs. functional mechanisms in evolutionary development
  • applications for evolutionary development
  • transfer of system-level biological properties to computational and technical systems
  • relation between structural and functional analysis of biological systems
  • genotype - phenotype maps
  • analysis and modeling of cellular representations
  • self assembly
  • hierarchical modeling in developmental systems
  • self-replicating systems

Evolutionary Games on Complex Networks

  • Session Organisers: Akira Namatame & Jun Tanimoto
  • Call For Session Papers: PDF

The aim of this session is to bring together researchers working in the area of evolutionary games with relation to network effects.

The emergence of cooperation in overcoming a dilemma can be explained by several theories such as kin selection, direct reciprocity, indirect reciprocity, network reciprocity, and group selection. Network reciprocity is one of the most important ideas among them, since the network reciprocity can make altruism emerge, even though requiring that agents use only the simplest strategy-either cooperation (C) or defection (D). Thus, the network reciprocity may explain why a number of animal species, unsophisticated in terms of information processing, have evolved cooperative social systems. Observing ourselves, the network reciprocity might be able to give a plausible answer of why human society evolves complex networks to solve conflicting problems that are often accompanied by a heavy social dilemma. Thus, evolutionary games on complex networks shed a clear light on those unsolved inquiries in evolutionary biology, sociobiology and other social sciences. Moreover, recently, the evolutionary games on complex networks also call particular interests from interdisciplinary areas of nonlinear science, because physicists have observed that there are several analogies between emergence of cooperation on evolutionary network games and phase transition of crystal lattice structures.

Evolutionary Robotics

Evolutionary Robotics (ER) aims to apply evolutionary computation techniques, inspired by Darwin's principle of selective reproduction of the fittest, to automatically design the control and/or hardware of both real and simulated autonomous robots.

Having an intrinsic interdisciplinary character, ER is being employed towards the development of many fields of research, among which we can highlight neuroscience, cognitive science, evolutionary biology and robotics. Hence the objective of this special session is to assemble a set of high-quality original contributions that reflect and advance the state-of-the-art in the area of Evolutionary Robotics, with an emphasis on the cross-fertilization between ER and the aforementioned research areas, ranging from theoretical analysis to real-life applications.

Topics of interest include (but are not restricted to):

  • Evolution of robots which display minimal cognitive behaviour, learning, memory, spatial cognition, adaptation or homeostasis.
  • Evolution of neural controllers for robots, aimed at giving an insight to neuroscientists or advancing control structures.
  • Evolution of communication, cooperation and competition, using robots as a research platform.
  • Co-evolution and the evolution of collective behaviour.
  • Evolution of morphology in close interaction with the environment, giving rise to self-reconfigurable, self-designing, self-healing and self-reproducing robots or humanoid and walking robots.
  • Evolution of robot systems aimed at real-world applications as in aerial robotics, space exploration, industry, search and rescue, robot companions, entertainment and games.
  • Evolution of controllers on board real robots or the real-time evolution of robot hardware.
  • Novel or improved algorithms for the evolution or robot systems.
  • The use of evolution for the artistic exploration of robot design.

Exploiting the Computational Properties of the Immune System: Applications and Algorithms

  • Session Organisers: Emma Hart & Julie Greensmith
  • Call For Session Papers: PDF

Recent developments in immunology propose a re-positioning of the natural immune system away from the traditional view of it as purely a defence system to a complex, self-organising computational system which is able to continuously compute the state of the body, and then to respond appropriately. This achieves host regulation, maintenance and of course, protection. This interpretation of the human immune system emphasises a number of its functional properties which have clear potential for exploitation in computational and engineered systems. Those properties encompass embodiment, composition of heterogeneous and naturally distributed components, life-long and continuous learning, adaptation and homeostasis. The inclusion of such properties differentiates the immune-inspired paradigm of Artificial Immune Systems (AIS) from other biologically-inspired paradigms such as Evolutionary or Swarm Computation.

Algorithms inspired by the human immune system have been successfully applied to a variety of application domains, most notably pattern recognition and optimisation. However, it appears that rich research potential remains in the exploitation of the unique properties of the immune system for transfer into the computational domain, which are also essential properties for many engineered systems. For example, recent advances in technology enable the construction of pervasive, autonomous systems constructed from perhaps tens of thousands of devices. Such systems must exhibit autonomic properties including self-repair and self-optimisation in addition to achieving desired functionality. For instance, networked devices such as internet routers which operate in dynamic and unpredictable environments must both maintain integrity and exhibit fault-tolerance.

This workshop addresses the development and application of novel immune-inspired algorithms to applications which can benefit from the unique and defining features of the immune system. In order to progress research in this area, an interdisciplinary approach to algorithm design is required, in which algorithms are developed that are rooted firmly in the underlying immunology. This process necessitates mathematical and computational modelling, in addition to obtaining a strong understanding of the respective application areas. To this end, we solicit both position and technical papers which address these areas.

Hardware Aspects of Bio-Inspired Architectures and Systems

  • Session Organisers: Andrew J. Greensted & Martin A. Trefzer
  • Call For Session Papers: PDF

Bio-inspired techniques and systems, supported by a wide range of state of the art electronic systems, have the potential for creating novel and competitive real-world applications. Furthermore, this research area offers the possibility to explore and master emerging technologies. However, could the challenge of embedding complex algorithms in hardware or developing proof-of-principle hardware prototypes into complete solutions explain why they have not been more widely adopted?

With the rapid increase in computational power of standard low-cost PCs, it has become easier to develop highly sophisticated algorithms within the field of evolutionary computation. Due to this, most applications are only developed in software relying on large systems utilising great computational power. However, without undertaking a hardware implementation stage, these systems reduce their chances of being deployed in many real-world applications and loose out on the advantages customised hardware platforms can offer: reduced size, energy efficiency, dependability and mobility as well as greater parallelism and hardware acceleration of critical operations. In order to make novel bio-inspired techniques industrially and commercially competitive, these properties are crucial and generally cannot be achieved in software only solutions. In areas such as robotics, mobile computing, automotive industries and real-time data processing these factors become vital.

How do people see Hardware?
Tool or Tissue?
Used or Abused?
Explored or Exploited?

This session is intended to bring together researchers who are implementing bio-inspired techniques in hardware, who are addressing the challenges this process presents and who are pushing forward alternative technologies for bio-inspired investigations. This session will provide a great opportunity for researchers to discuss their approaches and exchange their expertise and solutions. Submitted papers should be based upon, but not restricted to, the following topics:

Enhancing Electronic Systems and Advancing Technology with Bio-Inspired Techniques

  • Adaptive and homeostatic architectures
  • Developmental systems
  • Reconfigurability and fault tolerance
  • Evolution of electronic circuits

Hardwarisation of Bio-Inspired Techniques

  • Encoding complex hardware architectures (configurations, state)
  • Algorithm acceleration (Parallelism, custom IP)
  • Resource limited data representation in hardware (sensor data, population management)
  • Dynamic routing techniques
  • State management within runtime reconfigurable architectures

Alternative technologies for Bio-Inspired Investigations

  • Novel/Unconventional reconfigurable fabrics
  • Exploiting consumer devices
  • Natural substrates

Hybrid Evolutionary Algorithms in Forecasting Models

  • Session Organisers: Wei-Chiang Samuelson Hong & Edward Tsang
  • Call For Session Papers: PDF

Businesses require accurate forecasts of demand in order to make effective decisions, such as marketing, financial investment, inventory, distribution, human resource planning, purchasing, and so on. These forecasts are usually based on a function combination system (forecasting with evolutionary computing models) of traditional statistical methods, evolutionary algorithms (EA), evolutionary computation (EC), and management judgment. Although the wide application of hybrid modeling concepts, due to lack of abilities to catch the forecast data pattern, hybrid evolutionary algorithms resulted in over-reliance on the use of informal judgment and higher expense.

With the advantages of EA computing capabilities over the traditional optimization approaches, recently, they have been applied to catch the data pattern more accurate via systematical computation process, however, hybrid evolutionary algorithms (HEA), such as genetic algorithms with simulated annealing algorithms (GA-SA), chaotic search with particle swarm optimization algorithm (CPSO) and chaotic search with genetic algorithms (CGA), require more detail researches and empirical studies.

The objective of this special session is to invite together research and application of hybrid evolutionary algorithms for any forecasting applications.

This special session invites contributions in all aspects of applying hybrid evolutionary algorithms to improve the usage efficiency of those algorithms and aims to promote the discussion and exploration of new ideas. Topics of interests include (but not limited to):

  • The usage of HEA in forecasting applications.
  • Theoretical comparison of HEA and EA in forecasting applications.
  • Empirical comparison of of HEA and EA in forecasting applications.
  • Parameter determination by genetic algorithms with simulated
  • annealing algorithm (GA-SA) in forecasting applications.
  • Parameter determination by chaotic search with particle swarm
  • optimization algorithm (CPSO) in forecasting applications.
  • Parameter determination by chaotic search with genetic algorithms (CGA) in forecasting applications.
  • Other application of novel HEAs in forecasting applications.

Linkage in Evolutionary Computation

Genetic and evolutionary algorithms (GEAs) are powerful search methods based on the paradigm of evolution and widely applied to solve problems in many disciplines. In order to improve the performance and applicability, numerous sophisticated mechanisms have been introduced and integrated into GEAs in the past decades. One major category of these enhancing mechanisms is the concept of linkage, which models the relation between the decision variables with the genetic linkage observed in biological systems, and linkage learning techniques. Linkage learning connects the computational optimization methodologies and the natural evolution mechanisms. Not only can learning and adapting natural mechanisms enable us to design better computational methodologies; the insight gained by observing and analyzing the algorithmic behavior permits us to further understand biological systems, based on which GEAs are developed.

This special session aims at providing a forum for reviewing of current state-of-art linkage learning techniques, exchanging of ideas and viewpoints on linkage, as well as discussing the future directions. We invite researchers to submit their original and unpublished work including but not limited to the following topics:

  • Linkage in biological systems and computational algorithms
  • Linkage for discrete/continuous variables
  • Linkage processing, handling, and learning techniques
  • Identification and utilization of linkage
  • Adaptation of representation and/or operators for linkage
  • Theoretical aspects of linkage
  • Applications of the linkage concept
  • Position papers
  • Real-world applications

Memetic Algorithms for Hard to Solve Problems

One of the recent growing areas in Evolutionary Algorithm (EAs) research is Memetic Algorithms (MAs). MAs are population-based meta-heuristic search methods inspired by Darwinian principles of natural evolution and Dawkins notion of a meme defined as a unit of cultural evolution that is capable of local refinements. Recent studies on MAs have revealed their successes on a wide variety of real world problems. Particularly, they not only converge to high quality solutions, but also search more efficiently than their conventional counterparts. In diverse contexts, MAs are also commonly known as hybrid EAs, Baldwinian EAs, Lamarkian EAs, cultural algorithms and genetic local search.

The aim of this special session is to reflect the most recent advances in the field, and propose novel algorithmic implementations of MAs oriented towards specific problem which are hard to solve by classical optimization methods and popular meta-heuristics. A high emphasis will be given to the problems of balancing global and local search and on the techniques for obtaining an efficient coordination of the local search within an evolutionary framework. Both theoretical and empirical works are in the scope of this session. Some examples of the aforementioned hard to solve problems by means of Memetic Computing are:

  • Dynamic optimization problems
  • Noisy fitness landscapes
  • Computationally expensive optimization problems
  • Large scale problems
  • Multi-objective problems
  • Real-world applications

Parallel Bio-inspired Optimisation Methods: Algorithms and Applications

The use of computational models is becoming routine across a wide range of industries and applications. In the engineering design process, and scientific research, they are often used to find the best of a number of solutions as measured by some objective function(s). There is a growing demand for tools able to perform automatic optimisation to allow rigorous and systematic exploration of the model, particularly with high-dimensional parameter spaces.

This special session invites papers discussing recent advances in the development and application of bio-inspired optimisation algorithms to the field of computational science, optimisation, parallel and Grid computing. We encourage submission of papers describing new concepts and strategies, and systems and tools providing practical implementations, including hardware and software aspects. Papers describing methods of exploiting parallel computational resources on these algorithms are particularly encouraged.

In addition, we are interested in application papers discussing the power and applicability of these parallel methods to real-world problems in both well-established areas, such as computational engineering, and emerging fields such as computational biology.

Parameter Setting in Evolutionary Algorithms

Evolutionary computing researchers and practitioners know very well that choosing good parameter settings is essential for good performance of evolutionary algorithms. However, the defining attributes (e.g., the parent selection method) and the parameter values (e.g., the mutation rate) of evolutionary algorithms have been and often still are chosen in an ad hoc fashion, regularly on the basis of unverified conventions and beliefs. Adaptation of parameters and operators through tuning (off-line) or control (on-line) comprises a field of research that seeks to address this omission. As such, it is currently one of the most important and promising areas of research in evolutionary computation. The aim of this special session is to reflect the state-of-the-art in the field, and raise the awareness of this important area in the evolutionary computation community.

Topics of interest include, but are not limited to:

  • Parameter Tuning
  • Parameter Control
  • Adaptive Parameter Control
  • Deterministic Parameter Control
  • Self-Adaptation
  • Meta Algorithms
  • Adaption of Representation
  • Adaption of Variation Operators
  • Adaption of Selection Operators
  • Adaption of Fitness Function

PerAda: Adaptation Strategies for Pervasive Adaptation

  • Session Organisers: Ben Paechter, Emma Hart & A.E. Eiben
  • Call For Session Papers: PDF
  • Webpage: http://www.perada.eu

The field of Pervasive Adaptation - PerAda - is concerned with researching novel design paradigms for massive-scale pervasive information and communication systems which will enable a technology rich-future in which computing is truly ubiquitous. Such systems will operate in an ever-changing networked environment and will have to continuously and autonomously organise and adapt to highly dynamic and open technological and user contexts.

This workshop addresses the use of adaptation strategies in pervasive systems. Adaptation strategies, which may be bio-inspired, stochastic or otherwise, will need to operate over different time scales and speeds, ranging from short term adaptation to long-term evolution. This impacts the entire spectrum of research in Pervasive Systems in that it will imply changes in software, hardware, protocols and/or architecture at different levels of granularity and abstraction. Adaptation must occur at the level of individual devices as well as in 'tribes' of artefacts which are formed on an ad-hoc basis; this is compounded by that fact that the composition and location of systems is dynamic and continuously subject to change.

Evolution and adaptation in such environments poses a number of challenges. Evolution must occur within the boundaries imposed by ensuring trust and security in the networks, and further more, the potential for 'runaway evolution' must be addressed: any decentralised self- organising system which enables information or ideas to be propagated is vulnerable to being overcome by memes that are prevalent because of their ability or tendency to reproduce rather than because they are useful. A meme might be a piece of information, some code, a grouping or structure of entities etc. The high prevalence of some less useful applications and groupings in Facebook is an example of this. This behaviour in the system may result in an undesirable signal/noise ratio or ultimately to bandwidth saturation. New decentralised mechanisms need to be developed to prevent, monitor, evaluate and control these memes with a viral (but not necessarily malicious) nature.

Papers are welcomed which includes any aspect of adaptation in a Pervasive Environment. We welcome both technical and position papers. Examples of topics include, but are not limited to:

  • Evolution/Adaptation of hardware in pervasive systems
  • Evolution/Adaptation of software
  • Evolution of societies of artefacts
  • Runway evolution
  • Adaptation on multiple time-scales
  • Novel paradigms for continuous adaptation of devices
  • Exploitation of memory mechanisms for efficient adaptation
  • Human-intervention in adaptation and evolution of devices
  • Enabling self-* properties in pervasive systems.

Performance Assessment of Constrained / Bound Constrained Multi-Objective Optimization Algorithms

  • Session Organisers: Qingfu Zhang & P. N. Suganthan
  • Call For Session Papers: PDF

(This session is fo competition papers only)

Optimization for multiple conflicting objectives results in more than one optimal solutions known as Pareto- optimal solutions. Although one of these solutions is to be chosen eventually, the recent trend in evolutionary multi-objective optimization studies have focused on approximating the Pareto front by a set of solutions. Such a set of solutions can collectively provide a good insight to the different trade-off regions on the resulting efficient frontier, thereby aiding a better and more confident decision making.

Evolutionary multi-objective optimization (EMO) methodologies have been suggested since the eighties for this task. Since then a number of performance assessment methods has also been suggested. After more than 20 years of research and development of efficient EMO algorithms, we realize that it is time to evaluate the existing EMO methodologies on carefully chosen test problems which are scalable with respect to the objectives, the decision variables and constraints with complex Pareto shape in the decision space. The comparisons will be made for a limited number of overall evaluations, so that the existing or new algorithms can be evaluated for different functional abilities:

  1. convergence to Pareto front with diversity,
  2. to scale well on many objectives,
  3. to scale well on many variables,
  4. to perform well with bound and general constraints
  5. able to tackle complex Pareto shape in the parameter space
  6. able to tackle varying degree of linkages among variables.

PERPLEXUS: Pervasive reconfigurable platform for modelling complex systems

  • Session Organisers: Andres Upegui & Andres Perez-Uribe
  • Call For Session Papers: PDF

The simulation of large-scale complex systems requires huge amounts of computing resources. Even if the Moore law yet guarantees increasingly more powerful chips every year, the capacity of a single device is not always able to provide the optimal solution for running this kind of application. A new kind of distributed computing could appear in the near future, with the advent of a new era of computing, after the mainframe era, and the PC era. In this 3rd era of computing, we are likely to see a myriad of ubiquitous devices with large computing and sensory capacities in our environment.

Anticipating this new era of computing, we can imagine a solution for simulating complex systems, using the computational resources of ubiquitous devices in our environment. These devices will be adaptable and retargetable, for this, they will be made of custom reconfigurable devices endowed with adaptation capabilities that will enable the simulation of large-scale complex systems and the study of emergent complex behaviors in virtually unbounded wireless networks of computing modules.

The aim of this special session is to bring together hardware, middleware, and application developers of systems that exploit the pervasiveness of ubiquitous systems in order to model complex systems. This session will allow researchers to share experiences and identify theoretical and technical issues. Submitted papers may describe applications, computing models, modeling frameworks, or hardware platforms.

Techniques for Online and Distributed Evolutionary Computation

  • Session Organisers: Dr Daniele Miorandi, Dr Lidia Yamamoto, Dr Emma Hart & Dr Tina Yu
  • Call For Session Papers: PDF

Within the context of computing and communication the 'complexity ceiling' limits the ability to introduce innovation and to cope with changing user needs and demand. In order to overcome such limitations, it is desirable for many software and hardware systems to embed adaptation and evolution capabilities in the system fabric itself. By doing so, they would be able to work and perform well under an extreme variety of operating conditions, while at the same time easing system management tasks. Online evolution is difficult to achieve due to a variety of problems and challenges. These include the need to envisage extremely resilient evolutionary mechanisms (able to evolve without disrupting the system operations), the ability to devise new strategies (in response to external stimuli), and to operate in noisy environments. In other words, an effective on-line system needs to continuously provide evolvability to cope with an open- ended changing environment.

Extra challenges are also faced due to the interconnected and distributed nature of many systems. Such systems cope with only partial information (as single nodes/clusters may not be aware of the global system status), and in many cases with delayed information on the (estimated) fitness level of the current solution. In addition, distributed systems are often composed of heterogeneous devices, perhaps operating over different timescales and with different constraints, further increasing the challenges of achieving evolution in the global system.

Topics of interest include, but are not limited to:

  • resilient online and distributed EC mechanisms
  • robustness vs. evolvability in online environments
  • online evolution in artificial chemistries and chemical computing
  • online/distributed evolutionary optimization in uncertain environments
  • decentralized online evolutionary techniques
  • online/distributed evolution in robotics and embryonic circuits
  • online evolution in networked, distributed and pervasive systems

Theoretical Foundations of Evolutionary Computation

Evolutionary computation methods such as evolutionary algorithms or ant colony optimization have been shown to be very successful when dealing with real-world applications or problems from combinatorial optimization. The theoretical under- standing of these, in practice successful, algorithms is an important topic and has gained increasing interest in recent years. The aim of this special session is to bring together people working on theoretical aspects of evolutionary computation meth- ods. We invite submission concerning all kinds of theoretical analyses of evolutionary computation. Topics of interest include (but are not limited to):

  • population dynamics
  • approaches from statistical mechanics
  • runtime analysis
  • fitness landscapes and problem difficulty
  • self-adaptation

What is Computational Swarm Intelligence?

Swarm Intelligence (SI) algorithms, often inspired by communication and interaction between social agents such as ants or bees share much in common with EA's. However the precise relationship remains vague and ill-defined.

Are there any principles underlying and distinguishing the behaviour of swarm algorithms?

The session will gather experts in adjacent and seemingly related fields of

  • Swarm Intelligence
  • Artificial Immune Systems
  • Multi-agent systems

in order to discuss issues such as overlaps between algorithms, their compatibility, and their essential differences as problem solvers.

Previous research directions have looked towards Physics and, in particular, Biology for new ideas. This session proposes that Computational Swarm intelligence is an autonomous aggregate of techniques that so far have not been unified. We are looking for a mathematical, algorithmic framework which will enable us to understand and analyse these algorithms.

The aim of this workshop is NOT to make comparisons between techniques, but rather to make comparisons at a conceptual level.

The session will seek to define the metaheuristics of Swarm Intelligence algorithms. A common framework is desirable for a number of reasons, including the following:

  • Better understanding of the limits of application of SI techniques.
  • Hope to further the analysis of all algorithms by finding overarching principles.
  • Suggestions for novel and hybrid algorithms may result from the clash and the synthesis of ideas.

The workshop aims at addressing such issues from an explicitly theoretical rather than a heuristic perspective. The following contributions are welcomed:

  • Position papers and reports of work in progress
  • Papers proposing and advancing the metaheuristics of popular SI techniques such as PSO, ACO and SDS
  • Contributions from adjacent fields e.g AIS, Multi-Agent Systems

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Special Session Proposals
1st September 2008
Paper Submissions
1st November 2008
14th November 2008
Tutorial Proposals
1st December 2008
Notification of Acceptance
16th January 2009
6th February 2009
Final Paper Submission
16th February 2009
27th February 2009
Conference Starts
18th May 2009