Natural Computing Algorithms

By: Anthony Brabazon, Sean McGarraghy, Michael O'Neill

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Hardcover | 19 October 2015

At a Glance

Hardcover
576 Pages

Dimensions(cm)
16.8 x 24.3 x 3.5

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$50.75

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Introduction.- Introduction to Evolutionary Computing.- Genetic Algorithms.- Extending the Genetic Algorithm.- Evolution Strategies and Evolutionary Programming.- Differential Evolution.- Genetic Programming.- Particle Swarm Algorithms.- Ant Algorithms.- Honeybee Algorithms.- Other Social Algorithms.- Bacterial Foraging Algorithms.- Neural Networks for Supervised Learning.- Neural Networks for Unsupervised Learning.- Neuroevolution.- Artificial Immune Systems.- An Introduction to Developmental and Grammatical Computing.- Grammar-Based and Developmental Genetic Programming.- Grammatical Evolution.- TAG3P and Developmental TAG3P.- Genetic Regulatory Networks.- An Introduction to Physics-Inspired Computing.- Physics-Inspired Computing Algorithms.- Quantum-Inspired Evolutionary Algorithms.- Plant-Inspired Algorithms.- Chemistry-Inspired Algorithms.- Conclusions.- References.- Index.

Industry Reviews

"The book is very well organized. ... the book is not only suitable for beginners in natural computing, it can also serve as a valuable reference for experts. ... the book can be thought of not only as a collection of algorithms illustrating many methods and tools used in natural computing, but also as a textbook covering many aspects of the area which can be used in an introductory course on natural computing." (Miguel A. Guti©rrez-Naranjo, Mathematical Reviews, June, 2016)

"One interesting advantage of the volume is that it was prepared by and for scholars that are not necessarily in computer science. The book is definitely a good reference and a well-written and well-explained introduction to natural computing ... ." (Hector Zenil, Computing Reviews, April, 2016)

"I very much enjoyed reading this book and found it to be very comprehensive, well-structured, and well-written. It provides good coverage of natural computing approaches as well as a thorough description of each algorithm with its variants. ... suitable as a textbook for a graduate student course as well as a self-study guide for research students, since there are a good number of examples provided throughout. Furthermore, the algorithm descriptions, figures and tables facilitate the learning of the different concepts." (Simone A. Ludwig, Genetic Programming and Evolvable Machines, March, 2016)

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