LEADER 03519nam 22006015 450 001 9910254354703321 005 20200630033108.0 010 $a3-319-51109-2 024 7 $a10.1007/978-3-319-51109-2 035 $a(CKB)3710000001006458 035 $a(DE-He213)978-3-319-51109-2 035 $a(MiAaPQ)EBC5576469 035 $a(MiAaPQ)EBC6306849 035 $a(Au-PeEL)EBL5576469 035 $a(OCoLC)1066198229 035 $a(Au-PeEL)EBL6306849 035 $a(OCoLC)1193120242 035 $a(PPN)197455522 035 $a(EXLCZ)993710000001006458 100 $a20161228d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEvolutionary Computation Techniques: A Comparative Perspective /$fby Erik Cuevas, Valentín Osuna, Diego Oliva 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XV, 222 p. 74 illus., 33 illus. in color.) 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v686 311 $a3-319-51108-4 327 $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. 330 $aThis book compares the performance of various evolutionary computation (EC) techniques when they are faced with complex optimization problems extracted from different engineering domains. Particularly focusing on recently developed algorithms, it is designed so that each chapter can be read independently. Several comparisons among EC techniques have been reported in the literature, however, they all suffer from one limitation: their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. In each chapter, a complex engineering optimization problem is posed, and then a particular EC technique is presented as the best choice, according to its search characteristics. Lastly, a set of experiments is conducted in order to compare its performance to other popular EC methods. 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v686 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 676 $a006.3 700 $aCuevas$b Erik$4aut$4http://id.loc.gov/vocabulary/relators/aut$0761169 702 $aOsuna$b Valentín$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aOliva$b Diego$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254354703321 996 $aEvolutionary Computation Techniques: A Comparative Perspective$91897190 997 $aUNINA