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020			_a9789811680823
024	7		_a10.1007/978-981-16-8082-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA347.E96 _b2022 EB
245	0	0	_aDifferential Evolution : _bFrom Theory to Practice _cedited by B. Vinoth Kumar, Diego Oliva, P. N. Suganthan
250			_aFirst edition 2022
264		1	_aSingapore _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XIV, 381 páginas) _b116 ilustraciones, 89 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 _v1009
505	0		_aAnalysis of Structural Bias in Differential Evolution Configurations -- Spherical Model of Population Dynamics in Differential Evolution -- Reinforcement Learning-based Differential Evolution for Global Optimization -- Analytical Study on the Role of Scale Factor Parameter of Differential Evolution Algorithm on its Convergence Nature -- The Trap of Sisyphus Work in Differential Evolution and How to Avoid It -- Investigations on Distributed Differential Evolution Framework with Fault Tolerance Mechanisms -- Differential Evolution for Water Management Problems -- Sobol Sequence Based MOSADE Algorithm for Multi-Objective Design of Water Distribution Networks -- A Comparative Study on Parameter Estimation of Covid Epidemiological Models using Differential Evolution Algorithm -- Applications of Differential Evolution in Electric Power Systems -- Detection of Heavy Sandstorm Regions using Composite Differential Evolution Algorithm -- A Hybrid Artificial Differential Evolution Gorilla Troops Optimizer for High Dimensional Optimization Problems.
520			_aThis book addresses and disseminates state-of-the-art research and development of differential evolution (DE) and its recent advances, such as the development of adaptive, self-adaptive and hybrid techniques. Differential evolution is a population-based meta-heuristic technique for global optimization capable of handling non-differentiable, non-linear and multi-modal objective functions. Many advances have been made recently in differential evolution, from theory to applications. This book comprises contributions which include theoretical developments in DE, performance comparisons of DE, hybrid DE approaches, parallel and distributed DE for multi-objective optimization, software implementations, and real-world applications. The book is useful for researchers, practitioners, and students in disciplines such as optimization, heuristics, operations research and natural computing.
988			_aSpringer_Robotics_2022
650		7	_2embne _9467159 _aProgramación metaheurística
650		7	_2embne _9667195 _aComputación evolutiva
700	1		_aKumar, B. Vinoth _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aOliva, Diego _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aSuganthan, P. N. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9789811680816
776	0	8	_iPrinted edition: _z9789811680830
776	0	8	_iPrinted edition: _z9789811680847
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-16-8082-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b03/2022 _dz _esc _zSI