LEADER 04812nam 2201273z- 450 001 9910557631003321 005 20231214133406.0 035 $a(CKB)5400000000045104 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76746 035 $a(EXLCZ)995400000000045104 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNumerical and Evolutionary Optimization 2020 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 electronic resource (364 p.) 311 $a3-0365-1669-7 311 $a3-0365-1670-0 330 $aThis book was established after the 8th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications. 606 $aResearch & information: general$2bicssc 606 $aMathematics & science$2bicssc 610 $arobust optimization 610 $adifferential evolution 610 $aROOT 610 $aoptimization framework 610 $adrainage rehabilitation 610 $aoverflooding 610 $apipe breaking 610 $aVCO 610 $aCMOS differential pair 610 $aPVT variations 610 $aMonte Carlo analysis 610 $amulti-objective optimization 610 $aPareto Tracer 610 $acontinuation 610 $aconstraint handling 610 $asurrogate modeling 610 $amultiobjective optimization 610 $aevolutionary algorithms 610 $akriging method 610 $aensemble method 610 $aadaptive algorithm 610 $aliquid storage tanks 610 $abase excitation 610 $aartificial intelligence 610 $aMulti-Gene Genetic Programming 610 $acomputational fluid dynamics 610 $afinite volume method 610 $aJSSP 610 $aCMOSA 610 $aCMOTA 610 $achaotic perturbation 610 $afixed point arithmetic 610 $aFP16 610 $apseudo random number generator 610 $aincorporation of preferences 610 $amulti-criteria classification 610 $adecision-making process 610 $amulti-objective evolutionary optimization 610 $aoutranking relationships 610 $adecision maker profile 610 $aprofile assessment 610 $aregion of interest approximation 610 $aoptimization using preferences 610 $ahybrid evolutionary approach 610 $aforecasting 610 $aConvolutional Neural Network 610 $aLSTM 610 $aCOVID-19 610 $adeep learning 610 $atrust region methods 610 $amultiobjective descent 610 $aderivative-free optimization 610 $aradial basis functions 610 $afully linear models 610 $adecision making process 610 $acognitive tasks 610 $arecommender system 610 $aproject portfolio selection problem 610 $ausability evaluation 610 $amulti-objective portfolio optimization problem 610 $atrapezoidal fuzzy numbers 610 $adensity estimators 610 $asteady state algorithms 610 $aprotein structure prediction 610 $aHybrid Simulated Annealing 610 $aTemplate-Based Modeling 610 $astructural biology 610 $aMetropolis 610 $aoptimization 610 $alinear programming 610 $aenergy central 615 7$aResearch & information: general 615 7$aMathematics & science 700 $aQuiroz$b Marcela$4edt$01277553 702 $aSchütze$b Oliver$4edt 702 $aRuiz$b Juan Gabriel$4edt 702 $ade la Fraga$b Luis Gerardo$4edt 702 $aQuiroz$b Marcela$4oth 702 $aSchütze$b Oliver$4oth 702 $aRuiz$b Juan Gabriel$4oth 702 $ade la Fraga$b Luis Gerardo$4oth 906 $aBOOK 912 $a9910557631003321 996 $aNumerical and Evolutionary Optimization 2020$93011628 997 $aUNINA