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020 _a3319511092
_q(electronic bk.)
020 _a9783319511092
_q(electronic bk.)
020 _z3319511084
020 _z9783319511085
035 _a(OCoLC)967722334
_z(OCoLC)970753052
_z(OCoLC)971048098
_z(OCoLC)971091161
_z(OCoLC)974649734
_z(OCoLC)981103427
_z(OCoLC)1005757360
_z(OCoLC)1012044346
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_bspa
050 4 _aTA347.E96
_bC848 2017 EB
100 1 _aCuevas, Erik.
_944351
245 1 0 _aEvolutionary computation techniques :
_ba comparative perspective
_cErik Cuevas, Valentín Osuna, Diego Oliva.
264 1 _aCham
_bSpringer
_c2017
300 _a1 recurso en línea
336 _aTexto
_btxt
_2rdacontent
337 _aelectrónico
_bc
_2rdamedia
338 _arecurso electrónico
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 0 _aStudies in computational intelligence
_vvolume 686
500 _aSpringerLink
_bSpringer Engineering eBooks 2017 English+International
504 _aIncluye referencias bibliográficas
505 0 _aPreface -- Introduction -- Multilevel segmentation in digital images -- Multi-Circle detection on images -- Template matching -- Motion estimation -- Photovoltaic cell design -- Parameter identification of induction motors -- White blood cells Detection in images -- Estimation of view transformations in images -- Filter Design.
520 3 _aThis book compares the performance of various evolutionary computation (EC) techniques when they are faced with complex optimization problems extracted from different engineering domains. Particularly focusing on recently developed algorithms, it is designed so that each chapter can be read independently. Several comparisons among EC techniques have been reported in the literature, however, they all suffer from one limitation: their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. In each chapter, a complex engineering optimization problem is posed, and then a particular EC technique is presented as the best choice, according to its search characteristics. Lastly, a set of experiments is conducted in order to compare its performance to other popular EC methods.
650 7 _aComputación evolutiva
_2embne
_0(OCoLC)fst00917338
_0
_9667195
700 1 _aOliva, Diego.
700 1 _aOsuna, Valentín.
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-51109-2
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988 _aEBOOK, asignarmaterias, EBSPRINGER_2017B
998 _b02/2018
_dz
_e-
_zSI
999 _c95152
_d95152
_x1