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008			170119s2017 sz a ob 001 0 eng d
020			_a3319521551
020			_a331952156X _q(electronic bk.)
020			_a9783319521558
020			_a9783319521565 _q(electronic bk.)
020			_z9783319521558 _q(print)
035			_a(OCoLC)969344131 _z(OCoLC)974651027 _z(OCoLC)981109364 _z(OCoLC)981775816 _z(OCoLC)1005780099 _z(OCoLC)1011953349
040			_aGW5XE _cGW5XE _dYDX _dOCLCF _dUAB _dNJR _dUPM _dIOG _dVT2 _dUWO _dESU _dJBG _dIAD _dICW _dICN _dOTZ _dOCLCQ _dU3W _dES-MaUEC _bspa
050		4	_aQA402.5 _bK736 2017 EB
100	1		_aKramer, Oliver _eautor _999376
245	1	0	_aGenetic algorithm essentials _cOliver Kramer.
264		1	_aCham, Switzerland _bSpringer _c2017
300			_a1 recurso en línea (ix, 92 páginas) _bilustraciones (algunas a color)
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aStudies in computational intelligence _x1860-949X _vvolume 679
500			_aSpringerLink _bSpringer Engineering eBooks 2017 English+International
504			_aIncluye referencias bibliográficas e índice
505	0		_aPart I: Foundations -- Introduction -- Genetic Algorithms -- Parameters -- Part II: Solution Spaces -- Multimodality -- Constraints -- Multiple Objectives -- Part III: Advanced Concepts -- Theory -- Machine Learning -- Applications -- Part IV: Ending -- Summary and Outlook -- Index -- References.
520	3		_aThis book introduces readers to genetic algorithms (GAs) with an emphasis on making the concepts, algorithms, and applications discussed as easy to understand as possible. Further, it avoids a great deal of formalisms and thus opens the subject to a broader audience in comparison to manuscripts overloaded by notations and equations. The book is divided into three parts, the first of which provides an introduction to GAs, starting with basic concepts like evolutionary operators and continuing with an overview of strategies for tuning and controlling parameters. In turn, the second part focuses on solution space variants like multimodal, constrained, and multi-objective solution spaces. Lastly, the third part briefly introduces theoretical tools for GAs, the intersections and hybridizations with machine learning, and highlights selected promising applications.
650		7	_aAlgoritmos genéticos _2embne _0(OCoLC)fst00939996 _0 _9667357
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-52156-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017B
998			_b02/2018 _dz _e- _zSI
999			_c95235 _d95235 _x1