The biological immune system is a highly parallel and distributed adaptive system composed of a diverse range of innate and adaptive immune agents dedicated to protect organisms from infection. The immune agents form a complex and dynamic network, which using learning, memory, and associative retrieval are able to perform distributed cognitive tasks, as well as solve recognition and classification tasks. They learn to recognize relevant patterns, remember patterns that have been seen previously, and use combinatorics to construct pattern detectors efficiently. These remarkable information-processing abilities of the natural immune system provide important aspects in the field of computation. Artificial Immune Systems represent a maturing area of research that bridges the disciplines of immunology, biology, medical science, computer science, physics, mathematics and engineering. The scope of AIS ranges from modelling and simulation of the immune system through to immune-inspired algorithms in silico, in vitro and in vivo solutions. In recent years, algorithms inspired by theoretical immunology have been applied to a wide variety of domains, and the theoretical insight into aspects of artificial and real immune systems have been sought through mathematical and computational modelling, and analysis. In recent years, algorithms inspired by theoretical immunology have been applied to a wide variety of domains, including computer security, fault tolerance, data-mining and optimisation. Increasingly, theoretical insight into aspects of artificial and real immune systems has been sought through mathematical and computational modelling and analysis.