Nature-inspired computation / Mario D'Acunto.

Format:

Book

Author/Creator:

D'Acunto, Mario, author.

Contributor:

D'Acunto, Mario.

Series:

Computer science, technology and applications.

Computer Science, Technology and Applications

Language:

English

Subjects (All):

Biocomputers.

Biocomplexity--Computer simulation.

Biocomplexity.

Physical Description:

1 online resource (191 p.)

Place of Publication:

New York, [New York] : Nova Publishers, 2015.

Language Note:

English

Summary:

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 computat

Contents:

NATURE-INSPIRED COMPUTATION; NATURE-INSPIRED COMPUTATION; Library of Congress Cataloging-in-Publication Data; Contents; Preface; Acknowledgments; About the Author; Chapter 1: Introduction: Taking Inspiration from Nature to Solve Computation Problems; 1.1. Current Issues in Nature-Inspired Computation; References; Chapter 2: Artificial and Spiking Neural Networks; 2.1. Information Processing in; Spiking Neurons; 2.2. Unsupervised and Supervised Learning; References; Chapter 3: Genetic and Evolutionary Computation; 3.1. Evolutionary Programming; 3.2. Genetic Programming; References

Chapter 4: Machine Learning and Swarm Intelligence4.1. Swarm-inspired Algorithms; 4.1.1. Particle Swarm Optimization (PSO); 4.1.2. Ant Algorithms; 4.1.3. Bee Algorithms; 4.1.4. Firefly Algorithms; 4.1.5. Cuckoo Search; 4.1.6. Bat Algorithms; 4.1.7. Flower Pollination Algorithm; 4.2. Swarm Intelligence, Data Mining and Knowledge Discovery; References; Chapter 5: Nanostructures Inspiring Computation; 5.1. Nanoscale Self-Assembling Computation: The Case of DNA; 5.2. Self-assembled Molecular Circuits; 5.3. Quantum Dots Networks and Logic Computation; 5.4. Fractals for Computation Models

ReferencesChapter 6: Quantum Computing; 6.1. Basic Properties of Quantum Mechanics; 6.2. Quantum Cryptography; 6.3. Quantum Machine Learning; References; Chapter 7: Encryption Using Chaos, Noise and Oscillator Synchronization; 7.1. Secure Communication Form Noise; 7.2. Encryption by Coupling Functions; References; Chapter 8: Nature-Inspired Computation for Image Analysis and Vision Systems; 8.1. Genetic and Evolutionary Computation for Image Processing and Analysis; 8.2. Reaction-Diffusion Processes for Image Analysis and Vision Systems

8.2.1. Reaction-Diffusion System for Stereo Disparity Detection8.3. Diffusion-based Process Image Analysis Algorithms; References; Chapter 9: Nano-Optics and Nanophotonics Information: Computation Inspired by Light-Matter Interaction at Nanoscale; 9.1. Optics and Supercomputing; 9.2. Solving Hamiltonian Paths with Optical Computation; 9.3. Nanophotonic Computation Paradigm; 9.3.1. Hierarchical Information Photonics; References; Index

Notes:

Description based upon print version of record.

Includes bibliographical references at the end of each chapters and index.

Description based on print version record.

ISBN:

1-63482-476-8