Artificial immune systems are highly distributed systems based on the principles of the natural system. This is a new and rapidly growing field offering powerful and robust information processing capabilities for solving complex problems. Like artificial neural networks, artificial immune systems can learn new information, recall previously learned information, and perform pattern recognition in a highly decentralized fashion. This volume provides an overview of the immune system from the computational viewpoint. It discusses computational models of the immune system and their applications, and provides a wealth of insights on immunological memory and the effects of viruses in immune response. It will be of professional interest to scientists, academics, vaccine designers, and practitioners.
Presents computational models of the immune system
Insights on how to engineer massively parallel adaptive complex systems
Overview of immunity-based methodologies and applications in solving difficult problems
A pioneering work on the emerging field of artificial immune systems
Dipankar Dasgupta
algorithms artificial neural network behavior cognition complex adaptive systems control information processing learning memory modeling neural networks optimization pattern recognition robot simulation