Clearly, nature has been very effective in creating organisms that
are capable of protecting themselves against a wide variety of
pathogens such as bacteria, fungi, and parasites. The powerful
information-processing capabilities of the immune system, such as
feature extraction, pattern recognition, learning, memory, and its
distributive nature provide rich metaphors that researchers are
finding very useful for the development of computational models.
While some of these models are designed to give us a better
understanding of the immune system, other models are being
developed to solve complex real-world problems such as anomaly
detection, pattern recognition, data analysis (clustering),
function optimization, and computer security. Immunological
Computation: Theory and Applications is devoted to discussing
different immunological mechanisms and their relation to
information processing and problem solving. This unique volume
presents a compendium of up-to-date work related to immunity-based
techniques. After presenting the general abstractions of immune
elements and processes used in computational models, it then-
Reviews standard procedures, representations, and matching rules
that are used in all immunological computation models Covers the
details of one of the earliest and most well-known immune
algorithms, based on the negative selection (NS) process that
occurs in the thymus Examines promising immune models, including
those based on danger theory, cytokine network models, and
MHC-based models The text goes further to describe a wide variety
of applications, which include computer security, the detection and
analysis of anomalies and faults, robotics, and data mining among
others. To enhance understanding of this emerging field of study,
each chapter includes a summary, review questions, and exercis
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