Artificial Immune Systems: A New Computational Intelligence ApproachArtificial Immune Systems (AIS) are adaptive systems inspired by the biological immune system and applied to problem solving. This book provides an accessible introduction that will be suitable for anyone who is beginning to study or work in this area. It gives a clear definition of an AIS, sets out the foundations of the topic (including basic algorithms), and analyses how the immune system relates to other biological systems and processes. No prior knowledge of immunology is needed - all the essential background information is covered in the introductory chapters. Key features of the book include: - A discussion of AIS in the context of Computational Intelligence; - Case studies in Autonomous Navigation, Computer Network Security, Job-Shop Scheduling and Data Analysis =B7 An extensive survey of applications; - A framework to help the reader design and understand AIS; - A web site with additional resources including pseudocodes for immune algorithms, and links to related sites. Written primarily for final year undergraduate and postgraduate students studying Artificial Intelligence, Evolutionary and Biologically Inspired Computing, this book will also be of interest to industrial and academic researchers working in related areas. |
Contents
Fundamentals of the Immune System | 9 |
A Framework for Engineering Artificial Immune Systems | 53 |
A Survey of Artificial Immune Systems | 109 |
The Immune System in Context with Other Biological Systems | 161 |
The Evolution of Species and the Immune System | 183 |
Cognition and The Immune System | 193 |
AIS in Context with Other Computational Intelligence Paradigms | 203 |
Case Studies | 269 |
Conclusions and Future Trends | 299 |
Glossary of Biological Terms | 319 |
Pseudocode for Immune Algorithms | 337 |
Web Resources on AIS | 347 |
Common terms and phrases
activation adaptive immune response affinity maturation affinity measure antibody antibody molecules antigen application approach artificial immune systems artificial neural networks attribute strings B-cells behavior binding biological bitstrings capable Castro cells and molecules Chapter chromosomes clonal selection clone cognitive components corresponds Dasgupta detection detector diversity DNA computing dynamics elements environment epitope Equation evolution evolutionary algorithms Evolutionary Computation Forrest framework function fuzzy systems gene segments Genetic Algorithms gland Hamming shape-space hormones hybrid hypermutation idiotopes IEEE Immune Algorithm immune cells immune network model immune network theory immune sys immunology individuals input inspired interactions lymphocytes match mechanisms memory metadynamics metaphors mutation negative selection algorithm nervous system neuron niches nonself optimization organism parameters paratope pathogens pattern recognition Perelson perform population positive selection presented problem Proc proposed proteins receptors recognized repertoire robot Section selection algorithm signal somatic somatic hypermutation specific strategies structure T-cells Timmis tion types Varela Zuben