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008			141213s2015 ii | s |||| 0|eng d
020			_a9788132221845
024	7		_a10.1007/978-81-322-2184-5 _2doi
040			_bspa _dES-MaUEC
050		4	_aQA402.5 _b2015 EB
245	1	0	_aEvolutionary Constrained Optimization _cedited by Rituparna Datta, Kalyanmoy Deb.
264		1	_aNew Delhi _bSpringer International Publishing _c2015
300			_a1 recurso en línea (XVI, 319 páginas 111 ilustraciones, 39 ilustraciones a color.)
336			_2rdacontent _aTexto (visual) _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
490	0		_aInfosys Science Foundation Series in Applied Sciences and Engineering, _x2363-4995
490	0		_aEngineering (Springer-11647)
505	0		_aA Critical Review of Adaptive Penalty Techniques in Evolutionary Computation -- Ruggedness Quantifying for Constrained Continuous Fitness Landscapes -- Trust Regions in Surrogate-Assisted Evolutionary Programming for Constrained Expensive Black-Box Optimization -- Ephemeral Resource Constraints in Optimization -- Incremental Approximation Models for Constrained Evolutionary Optimization -- Efficient Constrained Optimization by the ε Constrained Differential Evolution with Rough Approximation -- Analyzing the Behaviour of Multi-Recombinative Evolution Strategies Applied to a Conically Constrained Problem -- Locating Potentially Disjoint Feasible Regions of a Search Space with a Particle Swarm Optimizer -- Ensemble of Constraint Handling Techniques for Single Objective Constrained Optimization -- Evolutionary Constrained Optimization: A Hybrid Approach.
520	3		_aThis book makes available a self-contained collection of modern research addressing the general constrained optimization problems using evolutionary algorithms. Broadly the topics covered include constraint handling for single and multi-objective optimizations; penalty function based methodology; multi-objective based methodology; new constraint handling mechanism; hybrid methodology; scaling issues in constrained optimization; design of scalable test problems; parameter adaptation in constrained optimization; handling of integer, discrete and mix variables in addition to continuous variables; application of constraint handling techniques to real-world problems; and constrained optimization in dynamic environment. There is also a separate chapter on hybrid optimization, which is gaining lots of popularity nowadays due to its capability of bridging the gap between evolutionary and classical optimization. The material in the book is useful to researchers, novice, and experts alike. The book will also be useful for classroom teaching and future research.
988			_aEBSPRINGER_2018
650		7	_9145705 _aOptimización matemática
700	1		_aDatta, Rituparna. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _0http://id.loc.gov/authorities/names/no2015095427 _1http://viaf.org/viaf/316797706/
700	1		_aDeb, Kalyanmoy. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _0http://id.loc.gov/authorities/names/no92003315 _1http://viaf.org/viaf/22385537/ _1http://dbpedia.org/resource/Kalyanmoy_Deb
776	0	8	_iEdición impresa: _z9788132221852
776	0	8	_iEdición impresa: _z9788132221838
776	0	8	_iEdición impresa: _z9788132235057
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-81-322-2184-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2019 _dz _eIG _zSI