國立虎尾科技大學 |

Natural computing algorithms

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Natural computing algorithms/ by Anthony Brabazon, Michael O'Neill, Sean McGarraghy.
作者:	Brabazon, Anthony.
其他作者:	McGarraghy, Sean.
出版者:	Berlin, Heidelberg :Imprint: Springer, : 2015.,
面頁冊數:	xx, 554 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
標題:	Quantitative Finance. -
電子資源:	http://dx.doi.org/10.1007/978-3-662-43631-8
ISBN:	9783662436318

Natural computing algorithms
Brabazon, Anthony.

Natural computing algorithms[electronic resource] /by Anthony Brabazon, Michael O'Neill, Sean McGarraghy. - Berlin, Heidelberg :Imprint: Springer,2015. - xx, 554 p. :ill., digital ;24 cm. - Natural computing series,1619-7127. - Natural computing series..

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.

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.

ISBN: 9783662436318

Standard No.: 10.1007/978-3-662-43631-8doiSubjects--Topical Terms:

669372
Quantitative Finance.

LC Class. No.: QA76.9.N37

Dewey Class. No.: 006.38

Natural computing algorithms
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505 0 $a 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.
520 $a 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.
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