LEADER 03584nam 22006615 450 001 9910254258203321 005 20200701030713.0 010 $a3-319-28503-3 024 7 $a10.1007/978-3-319-28503-0 035 $a(CKB)3710000000579345 035 $a(SSID)ssj0001616609 035 $a(PQKBManifestationID)16348190 035 $a(PQKBTitleCode)TC0001616609 035 $a(PQKBWorkID)14920327 035 $a(PQKB)10507327 035 $a(DE-He213)978-3-319-28503-0 035 $a(MiAaPQ)EBC6285116 035 $a(MiAaPQ)EBC5587926 035 $a(Au-PeEL)EBL5587926 035 $a(OCoLC)1066181158 035 $a(PPN)191705780 035 $a(EXLCZ)993710000000579345 100 $a20160120d2016 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aAdvances of Evolutionary Computation: Methods and Operators /$fby Erik Cuevas, Margarita Arimatea Díaz Cortés, Diego Alberto Oliva Navarro 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (XIV, 202 p. 48 illus., 43 illus. in color.) 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v629 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-319-28502-5 320 $aIncludes bibliographical references. 327 $aIntroduction -- A Swarm Global Optimization Algorithm Inspired in the Behavior of the Social-spider.-A States of Matter Algorithm for Global Optimization -- An Algorithm for Global Optimization Inspired by Collective Animal Behavior -- A Bio-inspired Evolutionary Algorithm: Allostatic Optimization -- Optimization Based on the Behavior of Locust Swarms. . 330 $aThe goal of this book is to present advances that discuss alternative Evolutionary Computation (EC) developments and non-conventional operators which have proved to be e?ective in the solution of several complex problems. The book has been structured so that each chapter can be read independently from the others. The book contains nine chapters with the following themes: 1) Introduction, 2) the Social Spider Optimization (SSO), 3) the States of Matter Search (SMS), 4) the collective animal behavior (CAB) algorithm, 5) the Allostatic Optimization (AO) method, 6) the Locust Search (LS) algorithm, 7) the Adaptive Population with Reduced Evaluations (APRE) method, 8) the multimodal CAB, 9) the constrained SSO method. 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v629 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 $a006.3823 700 $aCuevas$b Erik$4aut$4http://id.loc.gov/vocabulary/relators/aut$0761169 702 $aDíaz Cortés$b Margarita Arimatea$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aOliva Navarro$b Diego Alberto$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254258203321 996 $aAdvances of Evolutionary Computation: Methods and Operators$92533454 997 $aUNINA