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151008s2015 gw | s |||| 0|eng d |
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|a 9783662436318
|9 978-3-662-43631-8
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|a 10.1007/978-3-662-43631-8
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|a 004.0151
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|a Brabazon, Anthony.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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|a Natural Computing Algorithms
|h [electronic resource] /
|c by Anthony Brabazon, Michael O'Neill, Seán McGarraghy.
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|a 1st ed. 2015.
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg :
|b Imprint: Springer,
|c 2015.
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|a XX, 554 p. 164 illus., 22 illus. in color.
|b online resource.
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|a text
|b txt
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|a computer
|b c
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|a online resource
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|a text file
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|a Natural Computing Series,
|x 2627-6461
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|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.
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|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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|a Computer science.
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|a Computational intelligence.
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|a Artificial intelligence.
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|a Operations research.
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|a Management science.
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|a Social sciences-Mathematics.
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|a Theory of Computation.
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|a Computational Intelligence.
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|a Artificial Intelligence.
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|a Operations Research, Management Science .
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|a Operations Research and Decision Theory.
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650 |
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|a Mathematics in Business, Economics and Finance.
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|a O'Neill, Michael.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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1 |
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|a McGarraghy, Seán.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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2 |
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|a SpringerLink (Online service)
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773 |
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|t Springer Nature eBook
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|i Printed edition:
|z 9783662436301
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776 |
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|i Printed edition:
|z 9783662436325
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|i Printed edition:
|z 9783662501160
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|a Natural Computing Series,
|x 2627-6461
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|u https://doi.uam.elogim.com/10.1007/978-3-662-43631-8
|z Texto Completo
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|a ZDB-2-SCS
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|a Computer Science (SpringerNature-11645)
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|a Computer Science (R0) (SpringerNature-43710)
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