Artificial Immune Systems

1. Front Matter

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2. Immune System Modeling

   1. Agent Based Modeling of Lung Metastasis-Immune System Competition

      • Marzio Pennisi, Francesco Pappalardo, Santo Motta

      Pages 1-3

   2. Using UML to Model EAE and Its Regulatory Network

      • Mark Read, Jon Timmis, Paul S. Andrews, Vipin Kumar

      Pages 4-6

   3. Non-deterministic Explanation of Immune Responses: A Computer Model

      • Anastasio Salazar-Bañuelos

      Pages 7-10

   4. Dendritic Cell Trafficking: From Immunology to Engineering

      • Emma Hart, Despina Davoudani

      Pages 11-13

   5. A Hybrid Agent Based and Differential Equation Model of Body Size Effects on Pathogen Replication and Immune System Response

      • Soumya Banerjee, Melanie E. Moses

      Pages 14-18

   6. Nonself Detection in a Two-Component Cellular Frustrated System

      • F. Vistulo de Abreu, P. Mostardinha

      Pages 19-21

   7. Questions of Function: Modelling the Emergence of Immune Response

      • Tom Hebbron, Jason Noble

      Pages 22-26

   8. Object-Oriented Refactoring of Existing Immune Models

      • Hugues Bersini

      Pages 27-40

   9. Mathematical Model of HIV Superinfection and Comparative Drug Therapy

      • Anil Sorathiya, Pietro Liò, Luca Sguanci

      Pages 41-53

3. Theoretical Aspects of Artificial Immune Systems

   1. Exploration of the Dendritic Cell Algorithm Using the Duration Calculus

      • Feng Gu, Julie Greensmith, Uwe Aickelin

      Pages 54-66

   2. On AIRS and Clonal Selection for Machine Learning

      • Chris McEwan, Emma Hart

      Pages 67-79

   3. A Theoretical Analysis of Immune Inspired Somatic Contiguous Hypermutations for Function Optimization

      • Thomas Jansen, Christine Zarges

      Pages 80-94

   4. Comparing Different Aging Operators

      • Thomas Jansen, Christine Zarges

      Pages 95-108

   5. Efficient Algorithms for String-Based Negative Selection

      • Michael Elberfeld, Johannes Textor

      Pages 109-121

   6. T Cell Receptor Signalling Inspired Kernel Density Estimation and Anomaly Detection

      • Nick D. L. Owens, Andy Greensted, Jon Timmis, Andy Tyrrell

      Pages 122-135

4. Applied Artificial Immune Systems

   1. An Immuno-engineering Approach for Anomaly Detection in Swarm Robotics

      • HuiKeng Lau, Iain Bate, Jon Timmis

      Pages 136-150

   2. An Immune-Inspired Approach to Qualitative System Identification of the Detoxification Pathway of Methylglyoxal

      • Wei Pang, George M. Coghill

      Pages 151-164

   3. Application of AIS Based Classification Algorithms to Detect Overloaded Areas in Power System Networks

      • N. C. Woolley, J. V. Milanović

      Pages 165-177

   4. Artificial Immune System Applied to the Multi-stage Transmission Expansion Planning

      • Leandro S. Rezende, Armando M. Leite da Silva, Leonardo de Mello Honório

      Pages 178-191

Arti?cial immune systems (AIS) is a diverse and maturing area of research that bridges the disciplines of immunology and engineering. The scope of AIS ranges fromimmune-inspiredalgorithmsandengineeringsolutionsinsoftwareandha- ware, to the understanding of immunology through modeling and simulation of immune system concepts. AIS algorithms have been applied to a wide variety of applications, including computer security, fault tolerance, data mining and optimization. In addition, theoretical aspects of arti?cial and real immune s- tems have been the subject of mathematical and computational models and simulations. The 8th InternationalConference on AIS (ICARIS 2009)built on the success of previous years, providing a forum for a diverse group of AIS researchers to present and discuss their latest results and advances. After two years outside Europe, ICARIS 2009 returned to England, the venue for the ?rst ICARIS back in 2002. This year’s conference was located in the historic city of York, and was held in St. William’s College, the conference venue of York Minster, northern Europe’s largest Gothic cathedral.

• AIS
• Kernel
• anomaly detection
• artificial intelligence
• bioinformatics
• bioinspired algorithms
• classification
• distributed AI
• evolutionary computation
• gene expression
• genetic algorithms
• machine learning
• modeling
• multi-agent system
• robotics
• algorithm analysis and problem complexity

• Department of Computer Science, University of York, Heslington, UK

  Paul S. Andrews

• Departments of Computer Science and Electronics, University of York, Heslington, UK

  Jon Timmis

• Department of Electronics, University of York, Heslington, UK

  Nick D. L. Owens, Andy M. Tyrrell

• School of Computer Science, University of Nottingham, Nottingham, UK

  Uwe Aickelin

• School of Computing, Napier University, Edinburgh, UK

  Emma Hart

• Institute of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, UK

  Andrew Hone

Policies and ethics