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999			_c94488 _d94488
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003			ES-MaUEC
005			20230102112615.0
006			m o d
007			cr cnu|||unuuu
008			160811t20162017sz a o 000 0 eng d
020			_a3319429787 _q(electronic bk.)
020			_a9783319429786 _q(electronic bk.)
020			_z9783319429779 _q(print)
035			_a(OCoLC)956505383 _z(OCoLC)959031742
040			_aN$T _cN$T _dIDEBK _dOCLCQ _dEBLCP _dGW5XE _dN$T _dOCLCF _dDEBBG _dIDB _dUAB _dIOG _dMERER _dESU _dZ5A _dOCLCQ _dJBG _dIAD _dICW _dYDX _dAZU _dICN _dOTZ _dOCLCQ _dU3W _dES-MaUEC _bspa
050		4	_aQA402.5 _b2017 EB
245	0	0	_aRecent advances in evolutionary multi-objective optimization _cSlim Bechikh, Rituparna Datta, Abhishek Gupta, editors
264		1	_aSwitzerland _bSpringer _c[2016]
264		4	_c2017
300			_a1 recurso en línea (xii, 179 páginas) _bilustraciones (algunas a color)
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aAdaptation, learning, and optimization _x1867-4534 _vvolume 20
500			_aSpringerLink
505	0		_aMulti-objective Optimization: Classical and Evolutionary Approaches -- Dynamic Multi-objective Optimization using Evolutionary Algorithms: A Survey -- Evolutionary Bilevel Optimization: An Introduction and Recent Advances -- Many-objective Optimization using Evolutionary Algorithms: A Survey -- On the Emerging Notion of Evolutionary Multitasking: A Computational Analog of Cognitive Multitasking -- Practical Applications in Constrained Evolutionary Multi-objective Optimization.
520	3		_aThis book covers the most recent advances in the field of evolutionary multiobjective optimization. With the aim of drawing the attention of up-andcoming scientists towards exciting prospects at the forefront of computational intelligence, the authors have made an effort to ensure that the ideas conveyed herein are accessible to the widest audience. The book begins with a summary of the basic concepts in multi-objective optimization. This is followed by brief discussions on various algorithms that have been proposed over the years for solving such problems, ranging from classical (mathematical) approaches to sophisticated evolutionary ones that are capable of seamlessly tackling practical challenges such as non-convexity, multi-modality, the presence of multiple constraints, etc. Thereafter, some of the key emerging aspects that are likely to shape future research directions in the field are presented. These include:< optimization in dynamic environments, multi-objective bilevel programming, handling high dimensionality under many objectives, and evolutionary multitasking. In addition to theory and methodology, this book describes several real-world applications from various domains, which will expose the readers to the versatility of evolutionary multi-objective optimization.
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017A
650		7	_9145705 _aOptimización matemática _2embne
700	1		_aBechikh, Slim, _eeditor literario
700	1		_aDatta, Rituparna, _eeditor literario
700	1		_aGupta, Abhishek K., _eeditor literario
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-42978-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2018 _dz _e- _zSI