LEADER 04559nam 22007095 450 001 9910254358203321 005 20200630081844.0 010 $a3-319-44003-9 024 7 $a10.1007/978-3-319-44003-3 035 $a(CKB)3710000000858551 035 $a(DE-He213)978-3-319-44003-3 035 $a(MiAaPQ)EBC5578904 035 $a(PPN)195511557 035 $a(EXLCZ)993710000000858551 100 $a20160823d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNEO 2015 $eResults of the Numerical and Evolutionary Optimization Workshop NEO 2015 held at September 23-25 2015 in Tijuana, Mexico /$fedited by Oliver Schütze, Leonardo Trujillo, Pierrick Legrand, Yazmin Maldonado 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XVI, 444 p. 198 illus., 107 illus. in color.) 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v663 311 $a3-319-44002-0 320 $aIncludes bibliographic references and index. 327 $aPart I Genetic Programming -- Part II Combinatorial Optimization -- Part IV Machine Learning and Real World Applications. 330 $aThis volume comprises a selection of works presented at the Numerical and Evolutionary Optimization (NEO) workshop held in September 2015 in Tijuana, Mexico. The development of powerful search and optimization techniques is of great importance in today?s world that requires researchers and practitioners to tackle a growing number of challenging real-world problems. In particular, there are two well-established and widely known fields that are commonly applied in this area: (i) traditional numerical optimization techniques and (ii) comparatively recent bio-inspired heuristics. Both paradigms have their unique strengths and weaknesses, allowing them to solve some challenging problems while still failing in others. The goal of the NEO workshop series is to bring together people from these and related fields to discuss, compare and merge their complimentary perspectives in order to develop fast and reliable hybrid methods that maximize the strengths and minimize the weaknesses of the underlying paradigms. Through this effort, we believe that the NEO can promote the development of new techniques that are applicable to a broader class of problems. Moreover, NEO fosters the understanding and adequate treatment of real-world problems particularly in emerging fields that affect us all such as health care, smart cities, big data, among many others. The extended papers the NEO 2015 that comprise this book make a contribution to this goal. 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v663 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aMathematical optimization 606 $aOptical data processing 606 $aBig data 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aOptimization$3https://scigraph.springernature.com/ontologies/product-market-codes/M26008 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22005 606 $aBig Data/Analytics$3https://scigraph.springernature.com/ontologies/product-market-codes/522070 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aMathematical optimization. 615 0$aOptical data processing. 615 0$aBig data. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aOptimization. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aBig Data/Analytics. 676 $a519.6 702 $aSchütze$b Oliver$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aTrujillo$b Leonardo$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLegrand$b Pierrick$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMaldonado$b Yazmin$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254358203321 996 $aNEO 2015$91921615 997 $aUNINA