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008 170213s2015 gw | s |||| 0|eng d
020 _a9783662436318
_9978-3-662-43631-8
024 7 _a10.1007/978-3-662-43631-8
_2doi
035 _ato000561448
040 _aSpringer
_cSpringer
_dRU-ToGU
050 4 _aQA75.5-76.95
072 7 _aUY
_2bicssc
072 7 _aUYA
_2bicssc
072 7 _aCOM014000
_2bisacsh
072 7 _aCOM031000
_2bisacsh
082 0 4 _a004.0151
_223
100 1 _aBrabazon, Anthony.
_eauthor.
_9318765
245 1 0 _aNatural Computing Algorithms
_helectronic resource
_cby Anthony Brabazon, Michael O'Neill, Seán McGarraghy.
250 _a1st ed. 2015.
260 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2015.
300 _aXX, 554 p. 164 illus., 22 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 1 _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 _aComputer Science.
_9155490
650 0 _aOperations research.
_9303058
650 0 _aDecision making.
_9294514
650 0 _acomputers.
_9460101
650 0 _aArtificial intelligence.
_9274099
650 0 _aEconomics, Mathematical.
_9304111
650 0 _aManagement science.
_9459954
650 0 _aComputational Intelligence.
_9307538
650 1 4 _aComputer Science.
_9155490
650 2 4 _aTheory of Computation.
_9303507
650 2 4 _aComputational Intelligence.
_9307538
650 2 4 _aArtificial Intelligence (incl. Robotics).
_9274102
650 2 4 _aOperations Research, Management Science.
_9353130
650 2 4 _aOperation Research/Decision Theory.
_9411428
650 2 4 _aQuantitative Finance.
_9304891
700 1 _aO'Neill, Michael.
_eauthor.
_9318766
700 1 _aMcGarraghy, Seán.
_eauthor.
_9468411
710 2 _aSpringerLink (Online service)
_9143950
773 0 _tSpringer eBooks
830 0 _aNatural Computing Series,
_9318767
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-662-43631-8
912 _aZDB-2-SCS
999 _c415784