MARC Record from marc_columbia

LEADER: 13099cam a2200601 i 4500
001 16622347
005 20220627133421.0
006 m o d
007 cr cnu---unuuu
008 180702s2000 flu ob 001 0 eng d
035 $a(OCoLC)on1042329072
035 $a(NNC)16622347
040 $aN$T$beng$erda$epn$cN$T$dOCLCO$dYDX$dEBLCP$dNLE$dOTZ$dTYFRS$dOCLCA$dUKAHL$dOCLCQ$dK6U$dOCLCO$dOCLCQ$dOCLCO
020 $a9781420041965$q(electronic bk.)
020 $a1420041967$q(electronic bk.)
020 $z0849305888
020 $z9780849305887
035 $a(OCoLC)1042329072
050 4 $aQA76.618$b.E882 2000eb
072 7 $aCOM$x000000$2bisacsh
082 04 $a006.3$222
084 $a54.72$2bcl
084 $aST 285$2rvk
084 $aST 301$2rvk
084 $aDAT 001f$2stub
049 $aZCUA
245 00 $aEvolutionary computation /$cD. Dumitrescu [and others].
264 1 $aBoca Raton, FL :$bCRC Press,$c2000.
300 $a1 online resource (386 pages)
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
490 1 $aCRC Press international series on computational intelligence
504 $aIncludes bibliographical references and index.
505 00 $g1$tPrinciples of evolutionary computation$g1 --$g1.2$tGenes and chromosomes$g2 --$g1.2.1$tGene structure and DNA transcription$g2 --$g1.2.2$tGene expression as phenotypic traits$g3 --$g1.2.3$tDiploid and haploid genotypes$g5 --$g1.3$tEarly EC research$g5 --$g1.4$tBasic evolutionary computation models$g7 --$g1.4.1$tGenetic algorithms$g7 --$g1.4.2$tEvolutionary programming$g7 --$g1.4.3$tEvolution strategies$g8 --$g1.5$tOther EC approaches$g9 --$g1.5.1$tGenetic programming$g9 --$g1.5.2$tLearning classifier systems$g10 --$g1.6$tStructure of an evolutionary algorithm$g11 --$g1.6.1$tEncoding solutions$g11 --$g1.6.2$tSelection and search operators$g13 --$g1.6.3$tInnovative vs. conservative operators$g15 --$g1.6.4$tComponents of an EC algorithm$g15 --$g1.7$tBasic evolutionary algorithm$g16 --$g2$tGenetic algorithms$g21 --$g2.2$tProblem representation and fitness function$g23 --$g2.2.1$tRepresentation$g23 --$g2.2.2$tFitness function$g24 --$g2.3$tSearch progress$g25 --$g2.4$tBasic elements of genetic algorithms$g26 --$g2.5$tCanonical genetic algorithm$g28 --$g2.5.1$tRepresentation$g28 --$g2.5.2$tSimple genetic algorithm$g28 --$g2.5.3$tReplacement strategies$g30 --$g2.5.4$tInitial population$g31 --$g2.6$tSchemata and building blocks$g32 --$g2.6.1$tNotions concerning schemata$g33 --$g2.6.2$tBuilding block hypothesis and schema theorem$g36 --$g2.6.3$tImplicit parallelism$g36 --$g2.6.4$tGenetic drift$g37 --$g3$tBasic selection schemes in evolutionary algorithms$g39 --$g3.2$tSelection purposes$g40 --$g3.2.1$tMating pool$g40 --$g3.2.2$tSelection for recombination and selection for replacement$g41 --$g3.3$tFitness function$g42 --$g3.3.1$tFitness and scaling$g42 --$g3.3.2$tImplicit fitness evaluation$g43 --$g3.3.3$tCoevolutionary fitness evaluation$g44 --$g3.4$tSelection pressure and takeover time$g44 --$g3.4.1$tSelection pressure$g44 --$g3.4.2$tTakeover time$g45 --$g3.4.3$tSelection pressure and search progress$g46 --$g3.5$tProportional selection$g46 --$g3.5.1$tSelection probability$g46 --$g3.5.2$tProportional selection algorithm$g48 --$g3.5.3$tPremature and slow convergence$g50 --$g3.5.4$tVariants of proportional selection$g52 --$g3.6$tTruncation$g54 --$g4$tSelection based on scaling and ranking mechanisms$g57 --$g4.2$tScale transformation$g58 --$g4.3$tStatic scaling mechanisms$g59 --$g4.3.1$tLinear scaling$g59 --$g4.3.2$tPower law scaling$g60 --$g4.3.3$tLogarithmic scaling$g60 --$g4.4$tDynamic scaling$g61 --$g4.4.1$tSigma truncation$g61 --$g4.4.2$tWindow scaling$g62 --$g4.5$tNosiy fitness functions$g63 --$g4.6$tFitness remapping for minimization problems$g64 --$g4.7$tRank-based selection$g65 --$g4.7.1$tLinear ranking selection$g66 --$g4.7.2$tNonlinear ranking$g72 --$g4.8$tBinary tournament$g75 --$g4.8.1$tDeterministic tournament$g75 --$g4.8.2$tProbabilistic tournament$g76 --$g4.8.3$tBoltzmann tournament$g77 --$g4.9$tq-tournament selection$g78 --$g4.9.1$tScore-based tournament$g78 --$g4.9.2$tLocal tournament$g79 --$g4.9.3$tConcluding remarks on tournament selection$g80 --$g5$tFurther selection strategies$g83 --$g5.2$tClassification of selection strategies$g84 --$g5.3$tElitist strategies$g86 --$g5.4$tGeneration gap methods$g87 --$g5.4.1$tOverlapping and non-overlapping models$g87 --$g5.4.2$tGeneration gap$g88 --$g5.5$tSteady-state evolutionary algorithms$g89 --$g5.5.1$tBasic steady-state model$g89 --$g5.5.2$tGeneralized steady-state algorithm$g90 --$g5.6$tGenerational elitist strategies in GAs$g91 --$g5.7$tMichalewicz selection$g92 --$g5.8$tBoltzmann selection$g93 --$g5.8.1$tBoltzmann selection by scaling$g93 --$g5.8.2$tSimulated annealing$g95 --$g5.8.3$tPRSA method$g95 --$g5.9$tOther selection methods$g96 --$g5.9.1$tGreedy over-selection$g96 --$g5.9.2$tCoevolutionary selection models$g97 --$g5.10$tGenetic drift$g98 --$g6$tRecombination operators within binary encoding$g103 --$g6.2$tOne-point crossover$g104 --$g6.2.1$tBasic crossover operator$g104 --$g6.2.2$tFormal definition of crossover operator$g106 --$g6.3$tTwo-point crossover$g107 --s$g6.4$tN-point crossover$g108 --$g6.5$tPunctuated crossover$g110 --$g6.6$tSegmented crossover$g112 --$g6.7$tShuffle crossover$g113 --$g6.8$tUniform crossover$g114 --$g6.8.1$tBasic method$g114 --$g6.8.2$tGeneralizations$g115 --$g6.9$tOther crossover operators and some comparisons$g115 --$g6.9.1$tMulti-parent and one-descendent operators$g116 --$g6.9.2$tReduced surrogate$g116 --$g6.9.3$tExperimental and theoretical studies$g117 --$g6.10$tCrossover probability$g118 --$g6.10.1$tSetting crossover probability$g120 --$g6.11$tMating$g120 --$g6.12$tN-point crossover algorithm revisited$g121 --$g6.13$tSelection for survival or replacement$g123 --$g6.14$tGeneral remarks about crossover within the framework of binary encoding$g124 --$g7$tMutation operators and related topics$g131 --$g7.2$tMutation with binary encoding$g133 --$g7.2.1$tMutation rate$g134 --$g7.2.2$tMutation rate values$g134 --$g7.3$tStrong and weak mutation operators$g135 --$g7.3.1$tSelecting a position for mutation$g136 --$g7.3.2$tStrong mutation operator$g136 --$g7.3.3$tWeak mutation operator$g138 --$g7.3.4$tMutation within a unique chromosome$g139 --$g7.4$tNon-uniform mutation$g139 --$g7.4.1$tTime-dependent mutation rate$g139 --$g7.5$tAdaptive non-uniform mutation$g142 --$g7.6$tSelf-adaptation of mutation rate$g142 --$g7.6.1$tSelf-adaptation mechanism$g143 --$g7.6.2$tLocal mutation probabilities$g144 --$g7.7$tCrossover vs. mutation$g145 --$g7.8$tInversion operator$g146 --$g7.9$tSelection vs. variation operators$g147 --$g7.10$tSimple genetic algorithm revisited$g148 --$g8$tSchema theorem, building blocks, and related topics$g153 --$g8.2$tElements characterizing schemata$g155 --$g8.3$tSchema dynamics$g157 --$g8.4$tEffect of selection on schema dynamics$g158 --$g8.4.1$tSchema dynamics within selection$g158 --$g8.4.2$tDynamics of above/below-average schema$g161 --$g8.5$tEffect of recombination on schema dynamics$g163 --$g8.5.1$tSchema disruption probability$g163 --$g8.5.2$tActual disruption probability$g165 --$g8.5.3$tSurvival probability$g166 --$g8.6$tCombined effect of selection and recombination on schema dynamics$g166 --$g8.6.1$tSchema dynamics within selection and crossover$g167 --$g8.6.2$tQualitative results concerning schema dynamics$g169 --$g8.7$tEffect of mutation on schema dynamics$g170 --$g8.8$tSchema theorem$g173 --$g8.8.1$tSchema dynamics within selection and search operators$g173 --$g8.8.2$tApproximating schema dynamics$g174 --$g8.8.3$tFundamental theorem$g175 --$g8.9$tBuilding block$g176 --$g8.10$tBuilding block hypothesis and linkage problem$g177 --$g8.10.1$tSchema linkage$g178 --$g8.11$tGeneralizations of schema theorem$g180 --$g8.12$tDeceptive functions$g181 --$g9$tReal-valued encoding$g187 --$g9.2$tReal-valued vectors$g188 --$g9.3$tRecombination operators for real-valued encoding$g189 --$g9.3.1$tDiscrete recombination$g190 --$g9.3.2$tContinuous recombination$g191 --$g9.3.3$tComplete continuous recombination$g192 --$g9.3.4$tConvex (intermediate) recombination$g192 --$g9.3.5$tSBX operator$g195 --$g9.3.6$tMultiple-parent recombination$g196 --$g9.3.7$tFitness-based recombination$g197 --$g9.4$tMutation operators for real-valued encoding$g199 --$g9.4.1$tUniform mutation$g199 --$g9.4.2$tNon-uniform mutation$g202 --$g9.4.3$tNormal perturbation-induced mutation$g206 --$g9.4.4$tCauchy perturbation$g208 --$g10$tHybridization, parameter setting, and adaptation$g213 --$g10.2$tSpecialized representation and hybridization within GAs$g214 --$g10.2.1$tSpecific representation$g214 --$g10.2.2$tHybridization$g215 --$g10.2.3$tUse of specific encoding and hybridization$g216 --$g10.3$tParameter setting and adaptive GAs$g218 --$g10.3.1$tParameter setting in GAs$g218 --$g10.3.2$tParameter setting and representation adaptation$g219 --$g10.3.3$tAdaptive fitness of a search operator$g221 --$g10.4$tAdaptive GAs$g223 --$g10.4.1$tAdaptation problem$g223 --$g10.4.2$tAdaptive techniques based on fuzzy logic control$g225 --$g11$tAdaptive representations: messy genetic algorithms, delta- coding, and diploidic representation$g231 --$g11.2$tPrinciples of messy genetic algorithms$g233 --$g11.2.1$tVariable-length encoding$g233 --$g11.2.2$tLinkage problem$g234 --$g11.2.3$tMessy encoding$g236 --$g11.2.4$tIncompleteness and ambiguity$g237 --$g11.3$tRecombination within messy genetic operators$g239 --$g11.3.1$tRecombination$g239 --$g11.4$tMutation$g242 --$g11.5$tComputational models$g243 --$g11.6$tGeneralizations of messy GAs$g244 --$g11.7$tOther adaptive representation approaches$g245 --$g11.7.1$tARGOT system$g246 --$g11.7.2$tDynamic parameter encoding$g246 --$g11.8$tDelta coding$g247 --$g11.8.1$tReal-valued delta coding$g247 --$g11.8.2$tReal-valued delta coding procedure$g248 --$g11.8.3$tAlgorithm$g250 --$g11.9$tDiploidy and dominance$g252 --$g11.9.1$tHaploid and diploid chromosome structures revisited$g252 --$g11.9.2$tDominance$g253 --$g11.9.3$tDiploidic representation$g254 --$g11.9.4$tTriallelic representation$g254 --$g11.9.5$tQuadrallelic representation$g256 --$g11.9.6$tEvolving dominance map$g256 --$g11.9.7$tUse of diploidy$g257 --$g12$tEvolution strategies and evolutionary programming$g261 --$g12.2$tEvolution strategies$g261 --$g12.3$t(1+1) strategy$g263 --$g12.3.1$t1/5 success rule$g264 --$g12.3.2$tStandard deviation adaptation$g265 --$g12.3.3$tSchwefel's version of the 1/5 success rule$g266 --$g12.4$tMultimembered evolution strategies$g268 --$g12.4.1$tRepresentation of individuals$g269 --$g12.5$tStandard mutation$g270 --$g12.5.1$tStandard mutation of the control parameters$g270 --$g12.6$tGenotypes including covariance matrix. Correlated mutation$g272 --$g12.6.1$tCovariance matrix for mutation$g272 --$g12.6.2$tCorrelated mutations$g273 --$g12.7$tCauchy perturbations$g274 --$g12.7.1$tCauchy distribution$g274 --$g12.7.2$tCauchy perturbation-induced mutation$g274 --$g12.8$tEvolutionary programming$g275 --$g12.8.1$tSequential machine model$g276 --$g12.8.2$tFunction optimization by evolutionary programming$g278.
588 0 $aPrint version record.
520 3 $aRapid advances in evolutionary computation have opened up a world of applications-a world rapidly growing and evolving. Decision making, neural networks, pattern recognition, complex optimization/search tasks, scheduling, control, automated programming, and cellular automata applications all rely on evolutionary computation. Evolutionary Computation presents the basic principles of evolutionary computing: genetic algorithms, evolution strategies, evolutionary programming, genetic programming, learning classifier systems, population models, and applications. It includes detailed coverage of binary and real encoding, including selection, crossover, and mutation, and discusses the (m+l) and (m, l) evolution strategy principles. The focus then shifts to applications: decision strategy selection, training and design of neural networks, several approaches to pattern recognition, cellular automata, applications of genetic programming, and more.
650 0 $aEvolutionary programming (Computer science)
650 6 $aProgrammation évolutive.
650 7 $aCOMPUTERS$xGeneral.$2bisacsh
650 7 $aEvolutionary programming (Computer science)$2fast$0(OCoLC)fst00917347
650 7 $aEvolution$2gnd
650 7 $aEvolutionärer Algorithmus$2gnd
650 7 $aInformationstheorie$2gnd
650 17 $aKunstmatige intelligentie.$2gtt
650 17 $aEvolutie.$2gtt
650 17 $aAlgoritmen.$2gtt
655 4 $aElectronic books.
700 1 $aDumitrescu, D.$q(Dumitru),$d1949-
776 08 $iPrint version:$tEvolutionary computation.$dBoca Raton, FL : CRC Press, 2000$z0849305888$w(DLC) 00030348$w(OCoLC)43894173
830 0 $aCRC Press international series on computational intelligence.
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio16622347$zTaylor & Francis eBooks
852 8 $blweb$hEBOOKS