Nature inspired computation is an old idea, first proposed in the early fifties by Alan Turing, one of the founders of computer science. Turing suggested computational models of pattern formation in living systems based on systems of coupled reaction-diffusion equations giving rise to spatial patterns due to self-organization of substances in chemical concentrations. Since the pioneering work by Turing, many optimization algorithms stimulated by real-world features have gained great popularity and impact, thanks to their efficiency in solving nonlinear design problems. Nature-inspired computation has permeated into almost all areas of sciences, engineering and industries, from data mining to optimization, from computational intelligence to signal processing, from image analysis and vision systems to industrial applications. The book provides an introductory tour of the most popular nature inspired computational strategies. The book is subdivided in two parts, briefly describing the inspiration and motivation of natural processes and phenomena, main players, design principles, the scope of each branch, current trends and open problems. In the first section, attention is focused on Artificial and Spiking Neural Networks (Chapter 2), Evolutionary and Genetic Algorithms (Chapter 3), and Swarm Intelligence algorithms (Chapter 4). In the second section, we present the emergent knowledge and technologies in Multiscale Nature processes (Chapter 5), Quantum Computing and Quantum Cryptography (Chapter 6), Encryption and Secure Communication system (Chapter 7), Image processing and Vision systems (Chapter 8), and finally on Nanophotonics Information (Chapter 9).
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