LEADER 02930nam 2200481 450 001 9910483597603321 005 20210303103224.0 010 $a3-030-58100-4 024 7 $a10.1007/978-3-030-58100-8 035 $a(CKB)4100000011491449 035 $a(MiAaPQ)EBC6367934 035 $a(DE-He213)978-3-030-58100-8 035 $a(PPN)25808832X 035 $a(EXLCZ)994100000011491449 100 $a20210303d2021 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMetaheuristic computation $ea performance perspective /$fErik Cuevas, Primitivo Diaz, Octavio Camarena 205 $a1st ed. 2021. 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$dİ2021 215 $a1 online resource (XIV, 269 p. 93 illus., 31 illus. in color.) 225 1 $aIntelligent Systems Reference Library ;$vVolume 195 311 $a3-030-58099-7 327 $aIntroductory concepts of metaheuristic computation -- Introductory concepts of metaheuristic computation -- A metaheuristic methodology based on fuzzy logic principles. 330 $aThis book is primarily intended for undergraduate and postgraduate students of Science, Electrical Engineering, or Computational Mathematics. Metaheuristic search methods are so numerous and varied in terms of design and potential applications; however, for such an abundant family of optimization techniques, there seems to be a question which needs to be answered: Which part of the design in a metaheuristic algorithm contributes more to its better performance? Several works that compare the performance among metaheuristic approaches have been reported in the literature. Nevertheless, they suffer from one of the following limitations: (A)Their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. (B) Their conclusions consider only the comparison of their final results which cannot evaluate the nature of a good or bad balance between exploration and exploitation. The objective of this book is to compare the performance of various metaheuristic techniques when they are faced with complex optimization problems extracted from different engineering domains. The material has been compiled from a teaching perspective. 410 0$aIntelligent systems reference library ;$vVolume 195. 606 $aArtificial intelligence 615 0$aArtificial intelligence. 676 $a006.3 700 $aCuevas$b Erik$0761169 702 $aDiaz$b Primitivo 702 $aCamarena$b Octavio 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483597603321 996 $aMetaheuristic computation$92853734 997 $aUNINA LEADER 01771oam 2200517I 450 001 9910711895203321 005 20190123152243.0 035 $a(CKB)5470000002487125 035 $a(OCoLC)1083217454 035 $a(OCoLC)995470000002487125 035 $a(EXLCZ)995470000002487125 100 $a20190123j198910 ua 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNumerical methods in Markov chain modeling /$fBernard Phillipe, Youcef Saad, William J. Stewart 210 1$a[Moffett Field, California] :$cResearch Institute for Advanced Computer Science, NASA Ames Research Center,$dOctober 1989. 215 $a1 online resource (41 pages) $cillustrations 225 1 $aNASA/CR ;$v188908 225 1 $aRIACS technical report ;$v89.39 300 $a"October 1989." 300 $a"NASA cooperative agreement number NCC 2-387." 320 $aIncludes bibliographical references (pages 29-30). 606 $aMarkov chain models$2nasat 606 $aHomogenous linear systems$2nasat 606 $aDirect methods$2nasat 606 $aPreconditioned power iterations$2nasat 606 $aGMRES$2nasat 615 7$aMarkov chain models. 615 7$aHomogenous linear systems. 615 7$aDirect methods. 615 7$aPreconditioned power iterations. 615 7$aGMRES. 700 $aPhillipe$b Bernard$01423089 702 $aSaad$b Y. 702 $aStewart$b William J. 712 02$aResearch Institute for Advanced Computer Science (U.S.), 801 0$bGPO 801 1$bGPO 801 2$bGPO 906 $aBOOK 912 $a9910711895203321 996 $aNumerical methods in Markov chain modeling$93549405 997 $aUNINA