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008			210628s2021 sz | s |||| 0|eng d
020			_a9783030746407
024	7		_a10.1007/978-3-030-74640-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA402.5 _b2021 EB
100	1		_aJin, Yaochu _d1966- _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9681795
245	1	0	_aData-Driven Evolutionary Optimization : _bIntegrating Evolutionary Computation, Machine Learning and Data Science _cby Yaochu Jin, Handing Wang, Chaoli Sun.
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Pulishing _c2021
300			_a1 recurso en línea (XXV, 393 páginas) _b159 ilustraciones, 76 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aStudies in Computational Intelligence _x1860-9503 _v975
490	0		_aIntelligent Technologies and Robotics (SpringerNature-42732)
490	0		_aIntelligent Technologies and Robotics (R0) (SpringerNature-43728)
505	0		_aIntroduction to Optimization -- Classical Optimization Algorithms -- Evolutionary and Swarm Optimization -- Introduction to Machine Learning -- Data-Driven Surrogate-Assisted Evolutionary Optimization -- Multi-Surrogate-Assisted Single-Objective Optimization -- Surrogate-Assisted Multi-Objective Evolutionary Optimization.
520	3		_aIntended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available. This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.
988			_aSpringer_Robotics_2021
650		7	_2embne _9145705 _aOptimización matemática
700			_aWang, Handing _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9681796
700			_aSun, Chaoli _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9681797
776	0	8	_iPrinted edition: _z9783030746391
776	0	8	_iPrinted edition: _z9783030746414
776	0	8	_iPrinted edition: _z9783030746421
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-74640-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE