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008			190601s2020 gw | fo |||| 0|eng d
020			_a9783030187644
024	7		_a10.1007/978-3-030-18764-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ335 _b2020 EB
245	0	0	_aHigh-Performance Simulation-Based Optimization _cedited by Thomas Bartz-Beielstein, Bogdan Filipič, Peter Korošec, El-Ghazali Talbi
250			_aFirst Edition 2020
264		1	_aCham _bSpringer International Publishing _c2020
300			_a1 recurso en línea (XIII, 291 páginas) _b71 ilustraciones, 47 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 _x1860-949X _v833
490	0		_aIntelligent Technologies and Robotics (SpringerNature-42732)
490	0		_aIntelligent Technologies and Robotics (R0) (SpringerNature-43728)
505	0		_aInfill Criteria for Multiobjective Bayesian Optimization -- Many-Objective Optimization with Limited Computing Budget -- Multi-Objective Bayesian Optimization for Engineering Simulation -- Automatic Configuration of Multi-Objective Optimizers and Multi-Objective Configuration -- Optimization and Visualization in Many-Objective Space Trajectory Design -- Simulation Optimization through Regression or Kriging Metamodels -- Towards Better Integration of Surrogate Models and Optimizers -- Surrogate-Assisted Evolutionary Optimization of Large Problems -- Overview and Comparison of Gaussian Process-Based Surrogate Models for Mixed Continuous and Discrete Variables: Application on Aerospace Design Problems -- Open Issues in Surrogate-Assisted Optimization -- A Parallel Island Model for Hypervolume-Based Many-Objective Optimization -- Many-Core Branch-and-Bound for GPU Accelerators and MIC Coprocessors.
520	3		_aThis book presents the state of the art in designing high-performance algorithms that combine simulation and optimization in order to solve complex optimization problems in science and industry, problems that involve time-consuming simulations and expensive multi-objective function evaluations. As traditional optimization approaches are not applicable per se, combinations of computational intelligence, machine learning, and high-performance computing methods are popular solutions. But finding a suitable method is a challenging task, because numerous approaches have been proposed in this highly dynamic field of research. That's where this book comes in: It covers both theory and practice, drawing on the real-world insights gained by the contributing authors, all of whom are leading researchers. Given its scope, if offers a comprehensive reference guide for researchers, practitioners, and advanced-level students interested in using computational intelligence and machine learning to solve expensive optimization problems.
988			_aSpringer_Robotics_2020
650		7	_2embne _aInteligencia artificial _9413115
650		7	_2embne _aAprendizaje automático _9166090
700	1		_aBartz-Beielstein, Thomas _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aFilipič, Bogdan _eeditor literario _0(orcid)0000-0003-4428-4255 _1https://orcid.org/0000-0003-4428-4255 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aKorošec, Peter _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aTalbi, El-Ghazali _eeditor literario _0(orcid)0000-0003-4549-1010 _1https://orcid.org/0000-0003-4549-1010 _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink
773	0		_tSpringer Nature eBook
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-18764-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b11/2020 _dz _eb _zSI