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999 _c200434381
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008 151008s2015 gw | s |||| 0|eng d
020 _a9783662436318
_z978-3-662-43631-8
024 7 _a10.1007/978-3-662-43631-8
_2doi
040 _aTR-AnTOB
_beng
_cTR-AnTOB
_erda
050 4 _aQA75.5-76.95
072 7 _aUY
_2bicssc
072 7 _aCOM014000
_2bisacsh
072 7 _aUY
_2thema
072 7 _aUYA
_2thema004.0151
_223
100 1 _aBrabazon, Anthony.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aNatural Computing Algorithms /
_cby Anthony Brabazon, Michael O'Neill, Seán McGarraghy.
250 _a1st ed. 2015.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2015.
300 _a1 online resource
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 0 _aNatural Computing Series,
_x1619-7127
505 0 _aIntroduction -- 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 _aThe 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.
650 0 _aInformation theory.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aOperations research.
650 0 _aFinance.
650 1 4 _aTheory of Computation.
_0http://scigraph.springernature.com/things/product-market-codes/I16005
650 2 4 _aComputational Intelligence.
_0http://scigraph.springernature.com/things/product-market-codes/T11014
650 2 4 _aArtificial Intelligence.
_0http://scigraph.springernature.com/things/product-market-codes/I21000
650 2 4 _aOperations Research, Management Science.
_0http://scigraph.springernature.com/things/product-market-codes/M26024
650 2 4 _aOperations Research/Decision Theory.
_0http://scigraph.springernature.com/things/product-market-codes/521000
650 2 4 _aQuantitative Finance.
_0http://scigraph.springernature.com/things/product-market-codes/M13062
700 1 _aO'Neill, Michael.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aMcGarraghy, Seán.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
856 4 0 _uhttps://doi.org/10.1007/978-3-662-43631-8
_3Springer eBooks
_zOnline access link to the resource
942 _2lcc
_cEBK
041 _aeng