000 03377nam a22005415i 4500
001 978-3-319-51109-2
003 DE-He213
005 20220801222231.0
007 cr nn 008mamaa
008 161228s2017 sz | s |||| 0|eng d
020 _a9783319511092
_9978-3-319-51109-2
024 7 _a10.1007/978-3-319-51109-2
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aCuevas, Erik.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_960422
245 1 0 _aEvolutionary Computation Techniques: A Comparative Perspective
_h[electronic resource] /
_cby Erik Cuevas, Valentín Osuna, Diego Oliva.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXV, 222 p. 74 illus., 33 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v686
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 _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 0 _aComputational intelligence.
_97716
650 0 _aArtificial intelligence.
_93407
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aArtificial Intelligence.
_93407
700 1 _aOsuna, Valentín.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_960423
700 1 _aOliva, Diego.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_960424
710 2 _aSpringerLink (Online service)
_960425
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319511085
776 0 8 _iPrinted edition:
_z9783319511108
776 0 8 _iPrinted edition:
_z9783319845685
830 0 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v686
_960426
856 4 0 _uhttps://doi.org/10.1007/978-3-319-51109-2
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c80544
_d80544