The field of natural computing has been the focus of a substantial research effort in recent decades. One particular strand of this research concerns the development of computational algorithms using metaphorical inspiration from systems and phenomena that occur in the natural world. These naturally inspired computing algorithms have proven to be successful problem-solvers across domains as diverse as management science, bioinformatics, finance, marketing, engineering, architecture and design.
This book is a comprehensive introduction to natural computing algorithms, suitable for academic and industrial researchers and for undergraduate and graduate courses on natural computing in computer science, engineering and management science.
Author(s): Anthony Brabazon, Michael O'Neill, Seán McGarraghy
Series: Natural Computing Series
Edition: 1st
Publisher: Springer-Verlag Berlin Heidelberg
Year: 2015
Language: English
Pages: 554
Tags: Theory of Computation; Computational Intelligence; Artificial Intelligence (incl. Robotics); Operations Research, Management Science; Operation Research/Decision Theory; Quantitative Finance
Front Matter....Pages I-XX
Introduction....Pages 1-13
Front Matter....Pages 15-15
Introduction to Evolutionary Computing....Pages 17-20
Genetic Algorithm....Pages 21-42
Extending the Genetic Algorithm....Pages 43-71
Evolution Strategies and Evolutionary Programming....Pages 73-82
Differential Evolution....Pages 83-93
Genetic Programming....Pages 95-114
Front Matter....Pages 115-115
Particle Swarm Algorithms....Pages 117-140
Ant Algorithms....Pages 141-170
Other Foraging Algorithms....Pages 171-186
Bacterial Foraging Algorithms....Pages 187-199
Other Social Algorithms....Pages 201-218
Front Matter....Pages 219-219
Neural Networks for Supervised Learning....Pages 221-259
Neural Networks for Unsupervised Learning....Pages 261-280
Neuroevolution....Pages 281-298
Front Matter....Pages 299-299
Artificial Immune Systems....Pages 301-332
Front Matter....Pages 333-333
An Introduction to Developmental and Grammatical Computing....Pages 335-343
Grammar-Based and Developmental Genetic Programming....Pages 345-356
Grammatical Evolution....Pages 357-373
Tree-Adjoining Grammars and Genetic Programming....Pages 375-381
Front Matter....Pages 333-333
Genetic Regulatory Networks....Pages 383-389
Front Matter....Pages 391-391
An Introduction to Physically Inspired Computing....Pages 393-415
Physically Inspired Computing Algorithms....Pages 417-437
Quantum Inspired Evolutionary Algorithms....Pages 439-452
Front Matter....Pages 453-453
Plant-Inspired Algorithms....Pages 455-477
Chemically Inspired Algorithms....Pages 479-498
Front Matter....Pages 499-499
Looking Ahead....Pages 501-504
Back Matter....Pages 505-554
