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catalog contributor b11758856.
catalog created "2000.".
catalog date "2000".
catalog date "2000.".
catalog dateCopyrighted "2000.".
catalog description "1 Principles of evolutionary computation 1 -- 1.2 Genes and chromosomes 2 -- 1.2.1 Gene structure and DNA transcription 2 -- 1.2.2 Gene expression as phenotypic traits 3 -- 1.2.3 Diploid and haploid genotypes 5 -- 1.3 Early EC research 5 -- 1.4 Basic evolutionary computation models 7 -- 1.4.1 Genetic algorithms 7 -- 1.4.2 Evolutionary programming 7 -- 1.4.3 Evolution strategies 8 -- 1.5 Other EC approaches 9 -- 1.5.1 Genetic programming 9 -- 1.5.2 Learning classifier systems 10 -- 1.6 Structure of an evolutionary algorithm 11 -- 1.6.1 Encoding solutions 11 -- 1.6.2 Selection and search operators 13 -- 1.6.3 Innovative vs. conservative operators 15 -- 1.6.4 Components of an EC algorithm 15 -- 1.7 Basic evolutionary algorithm 16 -- 2 Genetic algorithms 21 -- 2.2 Problem representation and fitness function 23 -- 2.2.1 Representation 23 -- 2.2.2".
catalog description "123 -- 6.14 General remarks about crossover within the framework of binary encoding 124 -- 7 Mutation operators and related topics 131 -- 7.2 Mutation with binary encoding 133 -- 7.2.1 Mutation rate 134 -- 7.2.2 Mutation rate values 134 -- 7.3 Strong and weak mutation operators 135 -- 7.3.1 Selecting a position for mutation 136 -- 7.3.2 Strong mutation operator 136 -- 7.3.3 Weak mutation operator 138 -- 7.3.4 Mutation within a unique chromosome 139 -- 7.4 Non-uniform mutation 139 -- 7.4.1 Time-dependent mutation rate 139 -- 7.5 Adaptive non-uniform mutation 142 -- 7.6 Self-adaptation of mutation rate 142 -- 7.6.1 Self-adaptation mechanism 143 -- 7.6.2 Local mutation probabilities 144 -- 7.7 Crossover vs. mutation 145 -- 7.8 Inversion operator 146 -- 7.9 Selection vs. variation operators 147 -- 7.10 Simple genetic algorithm revisited 148 -- 8".
catalog description "176 -- 8.10 Building block hypothesis and linkage problem 177 -- 8.10.1 Schema linkage 178 -- 8.11 Generalizations of schema theorem 180 -- 8.12 Deceptive functions 181 -- 9 Real-valued encoding 187 -- 9.2 Real-valued vectors 188 -- 9.3 Recombination operators for real-valued encoding 189 -- 9.3.1 Discrete recombination 190 -- 9.3.2 Continuous recombination 191 -- 9.3.3 Complete continuous recombination 192 -- 9.3.4 Convex (intermediate) recombination 192 -- 9.3.5 SBX operator 195 -- 9.3.6 Multiple-parent recombination 196 -- 9.3.7 Fitness-based recombination 197 -- 9.4 Mutation operators for real-valued encoding 199 -- 9.4.1 Uniform mutation 199 -- 9.4.2 Non-uniform mutation 202 -- 9.4.3 Normal perturbation-induced mutation 206 -- 9.4.4 Cauchy perturbation 208 -- 10 Hybridization, parameter setting, and adaptation 213 -- 10.2".
catalog description "239 -- 11.4 Mutation 242 -- 11.5 Computational models 243 -- 11.6 Generalizations of messy GAs 244 -- 11.7 Other adaptive representation approaches 245 -- 11.7.1 ARGOT system 246 -- 11.7.2 Dynamic parameter encoding 246 -- 11.8 Delta coding 247 -- 11.8.1 Real-valued delta coding 247 -- 11.8.2 Real-valued delta coding procedure 248 -- 11.8.3 Algorithm 250 -- 11.9 Diploidy and dominance 252 -- 11.9.1 Haploid and diploid chromosome structures revisited 252 -- 11.9.2 Dominance 253 -- 11.9.3 Diploidic representation 254 -- 11.9.4 Triallelic representation 254 -- 11.9.5 Quadrallelic representation 256 -- 11.9.6 Evolving dominance map 256 -- 11.9.7 Use of diploidy 257 -- 12 Evolution strategies and evolutionary programming 261 -- 12.2 Evolution strategies 261 -- 12.3 (1+1) strategy 263 -- 12.3.1 1/5 success rule 264 -- 12.3.2 Standard deviation adaptation".
catalog description "265 -- 12.3.3 Schwefel's version of the 1/5 success rule 266 -- 12.4 Multimembered evolution strategies 268 -- 12.4.1 Representation of individuals 269 -- 12.5 Standard mutation 270 -- 12.5.1 Standard mutation of the control parameters 270 -- 12.6 Genotypes including covariance matrix. Correlated mutation 272 -- 12.6.1 Covariance matrix for mutation 272 -- 12.6.2 Correlated mutations 273 -- 12.7 Cauchy perturbations 274 -- 12.7.1 Cauchy distribution 274 -- 12.7.2 Cauchy perturbation-induced mutation 274 -- 12.8 Evolutionary programming 275 -- 12.8.1 Sequential machine model 276 -- 12.8.2 Function optimization by evolutionary programming 278.".
catalog description "3.4.2 Takeover time 45 -- 3.4.3 Selection pressure and search progress 46 -- 3.5 Proportional selection 46 -- 3.5.1 Selection probability 46 -- 3.5.2 Proportional selection algorithm 48 -- 3.5.3 Premature and slow convergence 50 -- 3.5.4 Variants of proportional selection 52 -- 3.6 Truncation 54 -- 4 Selection based on scaling and ranking mechanisms 57 -- 4.2 Scale transformation 58 -- 4.3 Static scaling mechanisms 59 -- 4.3.1 Linear scaling 59 -- 4.3.2 Power law scaling 60 -- 4.3.3 Logarithmic scaling 60 -- 4.4 Dynamic scaling 61 -- 4.4.1 Sigma truncation 61 -- 4.4.2 Window scaling 62 -- 4.5 Nosiy fitness functions 63 -- 4.6 Fitness remapping for minimization problems 64 -- 4.7 Rank-based selection 65 -- 4.7.1 Linear ranking selection 66 -- 4.7.2 Nonlinear ranking 72 -- 4.8 Binary tournament 75 -- 4.8.1 Deterministic tournament 75 -- 4.8.2".
catalog description "Coevolutionary selection models 97 -- 5.10 Genetic drift 98 -- 6 Recombination operators within binary encoding 103 -- 6.2 One-point crossover 104 -- 6.2.1 Basic crossover operator 104 -- 6.2.2 Formal definition of crossover operator 106 -- 6.3 Two-point crossover 107 --s 6.4 N-point crossover 108 -- 6.5 Punctuated crossover 110 -- 6.6 Segmented crossover 112 -- 6.7 Shuffle crossover 113 -- 6.8 Uniform crossover 114 -- 6.8.1 Basic method 114 -- 6.8.2 Generalizations 115 -- 6.9 Other crossover operators and some comparisons 115 -- 6.9.1 Multi-parent and one-descendent operators 116 -- 6.9.2 Reduced surrogate 116 -- 6.9.3 Experimental and theoretical studies 117 -- 6.10 Crossover probability 118 -- 6.10.1 Setting crossover probability 120 -- 6.11 Mating 120 -- 6.12 N-point crossover algorithm revisited 121 -- 6.13 Selection for survival or replacement".
catalog description "Fitness function 24 -- 2.3 Search progress 25 -- 2.4 Basic elements of genetic algorithms 26 -- 2.5 Canonical genetic algorithm 28 -- 2.5.1 Representation 28 -- 2.5.2 Simple genetic algorithm 28 -- 2.5.3 Replacement strategies 30 -- 2.5.4 Initial population 31 -- 2.6 Schemata and building blocks 32 -- 2.6.1 Notions concerning schemata 33 -- 2.6.2 Building block hypothesis and schema theorem 36 -- 2.6.3 Implicit parallelism 36 -- 2.6.4 Genetic drift 37 -- 3 Basic selection schemes in evolutionary algorithms 39 -- 3.2 Selection purposes 40 -- 3.2.1 Mating pool 40 -- 3.2.2 Selection for recombination and selection for replacement 41 -- 3.3 Fitness function 42 -- 3.3.1 Fitness and scaling 42 -- 3.3.2 Implicit fitness evaluation 43 -- 3.3.3 Coevolutionary fitness evaluation 44 -- 3.4 Selection pressure and takeover time 44 -- 3.4.1 Selection pressure 44 --".
catalog description "Includes bibliographical references and index.".
catalog description "Probabilistic tournament 76 -- 4.8.3 Boltzmann tournament 77 -- 4.9 q-tournament selection 78 -- 4.9.1 Score-based tournament 78 -- 4.9.2 Local tournament 79 -- 4.9.3 Concluding remarks on tournament selection 80 -- 5 Further selection strategies 83 -- 5.2 Classification of selection strategies 84 -- 5.3 Elitist strategies 86 -- 5.4 Generation gap methods 87 -- 5.4.1 Overlapping and non-overlapping models 87 -- 5.4.2 Generation gap 88 -- 5.5 Steady-state evolutionary algorithms 89 -- 5.5.1 Basic steady-state model 89 -- 5.5.2 Generalized steady-state algorithm 90 -- 5.6 Generational elitist strategies in GAs 91 -- 5.7 Michalewicz selection 92 -- 5.8 Boltzmann selection 93 -- 5.8.1 Boltzmann selection by scaling 93 -- 5.8.2 Simulated annealing 95 -- 5.8.3 PRSA method 95 -- 5.9 Other selection methods 96 -- 5.9.1 Greedy over-selection 96 -- 5.9.2".
catalog description "Schema theorem, building blocks, and related topics 153 -- 8.2 Elements characterizing schemata 155 -- 8.3 Schema dynamics 157 -- 8.4 Effect of selection on schema dynamics 158 -- 8.4.1 Schema dynamics within selection 158 -- 8.4.2 Dynamics of above/below-average schema 161 -- 8.5 Effect of recombination on schema dynamics 163 -- 8.5.1 Schema disruption probability 163 -- 8.5.2 Actual disruption probability 165 -- 8.5.3 Survival probability 166 -- 8.6 Combined effect of selection and recombination on schema dynamics 166 -- 8.6.1 Schema dynamics within selection and crossover 167 -- 8.6.2 Qualitative results concerning schema dynamics 169 -- 8.7 Effect of mutation on schema dynamics 170 -- 8.8 Schema theorem 173 -- 8.8.1 Schema dynamics within selection and search operators 173 -- 8.8.2 Approximating schema dynamics 174 -- 8.8.3 Fundamental theorem 175 -- 8.9 Building block".
catalog description "Specialized representation and hybridization within GAs 214 -- 10.2.1 Specific representation 214 -- 10.2.2 Hybridization 215 -- 10.2.3 Use of specific encoding and hybridization 216 -- 10.3 Parameter setting and adaptive GAs 218 -- 10.3.1 Parameter setting in GAs 218 -- 10.3.2 Parameter setting and representation adaptation 219 -- 10.3.3 Adaptive fitness of a search operator 221 -- 10.4 Adaptive GAs 223 -- 10.4.1 Adaptation problem 223 -- 10.4.2 Adaptive techniques based on fuzzy logic control 225 -- 11 Adaptive representations: messy genetic algorithms, delta- coding, and diploidic representation 231 -- 11.2 Principles of messy genetic algorithms 233 -- 11.2.1 Variable-length encoding 233 -- 11.2.2 Linkage problem 234 -- 11.2.3 Messy encoding 236 -- 11.2.4 Incompleteness and ambiguity 237 -- 11.3 Recombination within messy genetic operators 239 -- 11.3.1 Recombination".
catalog extent "[12], 386 p. ;".
catalog identifier "0849305888 (alk.)".
catalog isPartOf "CRC Press international series on computational intelligence".
catalog issued "2000".
catalog issued "2000.".
catalog language "eng".
catalog publisher "Boca Raton, FL : CRC Press,".
catalog subject "006.3 21".
catalog subject "Evolutionary programming (Computer science)".
catalog subject "QA76.618. E882 2000".
catalog tableOfContents "1 Principles of evolutionary computation 1 -- 1.2 Genes and chromosomes 2 -- 1.2.1 Gene structure and DNA transcription 2 -- 1.2.2 Gene expression as phenotypic traits 3 -- 1.2.3 Diploid and haploid genotypes 5 -- 1.3 Early EC research 5 -- 1.4 Basic evolutionary computation models 7 -- 1.4.1 Genetic algorithms 7 -- 1.4.2 Evolutionary programming 7 -- 1.4.3 Evolution strategies 8 -- 1.5 Other EC approaches 9 -- 1.5.1 Genetic programming 9 -- 1.5.2 Learning classifier systems 10 -- 1.6 Structure of an evolutionary algorithm 11 -- 1.6.1 Encoding solutions 11 -- 1.6.2 Selection and search operators 13 -- 1.6.3 Innovative vs. conservative operators 15 -- 1.6.4 Components of an EC algorithm 15 -- 1.7 Basic evolutionary algorithm 16 -- 2 Genetic algorithms 21 -- 2.2 Problem representation and fitness function 23 -- 2.2.1 Representation 23 -- 2.2.2".
catalog tableOfContents "123 -- 6.14 General remarks about crossover within the framework of binary encoding 124 -- 7 Mutation operators and related topics 131 -- 7.2 Mutation with binary encoding 133 -- 7.2.1 Mutation rate 134 -- 7.2.2 Mutation rate values 134 -- 7.3 Strong and weak mutation operators 135 -- 7.3.1 Selecting a position for mutation 136 -- 7.3.2 Strong mutation operator 136 -- 7.3.3 Weak mutation operator 138 -- 7.3.4 Mutation within a unique chromosome 139 -- 7.4 Non-uniform mutation 139 -- 7.4.1 Time-dependent mutation rate 139 -- 7.5 Adaptive non-uniform mutation 142 -- 7.6 Self-adaptation of mutation rate 142 -- 7.6.1 Self-adaptation mechanism 143 -- 7.6.2 Local mutation probabilities 144 -- 7.7 Crossover vs. mutation 145 -- 7.8 Inversion operator 146 -- 7.9 Selection vs. variation operators 147 -- 7.10 Simple genetic algorithm revisited 148 -- 8".
catalog tableOfContents "176 -- 8.10 Building block hypothesis and linkage problem 177 -- 8.10.1 Schema linkage 178 -- 8.11 Generalizations of schema theorem 180 -- 8.12 Deceptive functions 181 -- 9 Real-valued encoding 187 -- 9.2 Real-valued vectors 188 -- 9.3 Recombination operators for real-valued encoding 189 -- 9.3.1 Discrete recombination 190 -- 9.3.2 Continuous recombination 191 -- 9.3.3 Complete continuous recombination 192 -- 9.3.4 Convex (intermediate) recombination 192 -- 9.3.5 SBX operator 195 -- 9.3.6 Multiple-parent recombination 196 -- 9.3.7 Fitness-based recombination 197 -- 9.4 Mutation operators for real-valued encoding 199 -- 9.4.1 Uniform mutation 199 -- 9.4.2 Non-uniform mutation 202 -- 9.4.3 Normal perturbation-induced mutation 206 -- 9.4.4 Cauchy perturbation 208 -- 10 Hybridization, parameter setting, and adaptation 213 -- 10.2".
catalog tableOfContents "239 -- 11.4 Mutation 242 -- 11.5 Computational models 243 -- 11.6 Generalizations of messy GAs 244 -- 11.7 Other adaptive representation approaches 245 -- 11.7.1 ARGOT system 246 -- 11.7.2 Dynamic parameter encoding 246 -- 11.8 Delta coding 247 -- 11.8.1 Real-valued delta coding 247 -- 11.8.2 Real-valued delta coding procedure 248 -- 11.8.3 Algorithm 250 -- 11.9 Diploidy and dominance 252 -- 11.9.1 Haploid and diploid chromosome structures revisited 252 -- 11.9.2 Dominance 253 -- 11.9.3 Diploidic representation 254 -- 11.9.4 Triallelic representation 254 -- 11.9.5 Quadrallelic representation 256 -- 11.9.6 Evolving dominance map 256 -- 11.9.7 Use of diploidy 257 -- 12 Evolution strategies and evolutionary programming 261 -- 12.2 Evolution strategies 261 -- 12.3 (1+1) strategy 263 -- 12.3.1 1/5 success rule 264 -- 12.3.2 Standard deviation adaptation".
catalog tableOfContents "265 -- 12.3.3 Schwefel's version of the 1/5 success rule 266 -- 12.4 Multimembered evolution strategies 268 -- 12.4.1 Representation of individuals 269 -- 12.5 Standard mutation 270 -- 12.5.1 Standard mutation of the control parameters 270 -- 12.6 Genotypes including covariance matrix. Correlated mutation 272 -- 12.6.1 Covariance matrix for mutation 272 -- 12.6.2 Correlated mutations 273 -- 12.7 Cauchy perturbations 274 -- 12.7.1 Cauchy distribution 274 -- 12.7.2 Cauchy perturbation-induced mutation 274 -- 12.8 Evolutionary programming 275 -- 12.8.1 Sequential machine model 276 -- 12.8.2 Function optimization by evolutionary programming 278.".
catalog tableOfContents "3.4.2 Takeover time 45 -- 3.4.3 Selection pressure and search progress 46 -- 3.5 Proportional selection 46 -- 3.5.1 Selection probability 46 -- 3.5.2 Proportional selection algorithm 48 -- 3.5.3 Premature and slow convergence 50 -- 3.5.4 Variants of proportional selection 52 -- 3.6 Truncation 54 -- 4 Selection based on scaling and ranking mechanisms 57 -- 4.2 Scale transformation 58 -- 4.3 Static scaling mechanisms 59 -- 4.3.1 Linear scaling 59 -- 4.3.2 Power law scaling 60 -- 4.3.3 Logarithmic scaling 60 -- 4.4 Dynamic scaling 61 -- 4.4.1 Sigma truncation 61 -- 4.4.2 Window scaling 62 -- 4.5 Nosiy fitness functions 63 -- 4.6 Fitness remapping for minimization problems 64 -- 4.7 Rank-based selection 65 -- 4.7.1 Linear ranking selection 66 -- 4.7.2 Nonlinear ranking 72 -- 4.8 Binary tournament 75 -- 4.8.1 Deterministic tournament 75 -- 4.8.2".
catalog tableOfContents "Coevolutionary selection models 97 -- 5.10 Genetic drift 98 -- 6 Recombination operators within binary encoding 103 -- 6.2 One-point crossover 104 -- 6.2.1 Basic crossover operator 104 -- 6.2.2 Formal definition of crossover operator 106 -- 6.3 Two-point crossover 107 --s 6.4 N-point crossover 108 -- 6.5 Punctuated crossover 110 -- 6.6 Segmented crossover 112 -- 6.7 Shuffle crossover 113 -- 6.8 Uniform crossover 114 -- 6.8.1 Basic method 114 -- 6.8.2 Generalizations 115 -- 6.9 Other crossover operators and some comparisons 115 -- 6.9.1 Multi-parent and one-descendent operators 116 -- 6.9.2 Reduced surrogate 116 -- 6.9.3 Experimental and theoretical studies 117 -- 6.10 Crossover probability 118 -- 6.10.1 Setting crossover probability 120 -- 6.11 Mating 120 -- 6.12 N-point crossover algorithm revisited 121 -- 6.13 Selection for survival or replacement".
catalog tableOfContents "Fitness function 24 -- 2.3 Search progress 25 -- 2.4 Basic elements of genetic algorithms 26 -- 2.5 Canonical genetic algorithm 28 -- 2.5.1 Representation 28 -- 2.5.2 Simple genetic algorithm 28 -- 2.5.3 Replacement strategies 30 -- 2.5.4 Initial population 31 -- 2.6 Schemata and building blocks 32 -- 2.6.1 Notions concerning schemata 33 -- 2.6.2 Building block hypothesis and schema theorem 36 -- 2.6.3 Implicit parallelism 36 -- 2.6.4 Genetic drift 37 -- 3 Basic selection schemes in evolutionary algorithms 39 -- 3.2 Selection purposes 40 -- 3.2.1 Mating pool 40 -- 3.2.2 Selection for recombination and selection for replacement 41 -- 3.3 Fitness function 42 -- 3.3.1 Fitness and scaling 42 -- 3.3.2 Implicit fitness evaluation 43 -- 3.3.3 Coevolutionary fitness evaluation 44 -- 3.4 Selection pressure and takeover time 44 -- 3.4.1 Selection pressure 44 --".
catalog tableOfContents "Probabilistic tournament 76 -- 4.8.3 Boltzmann tournament 77 -- 4.9 q-tournament selection 78 -- 4.9.1 Score-based tournament 78 -- 4.9.2 Local tournament 79 -- 4.9.3 Concluding remarks on tournament selection 80 -- 5 Further selection strategies 83 -- 5.2 Classification of selection strategies 84 -- 5.3 Elitist strategies 86 -- 5.4 Generation gap methods 87 -- 5.4.1 Overlapping and non-overlapping models 87 -- 5.4.2 Generation gap 88 -- 5.5 Steady-state evolutionary algorithms 89 -- 5.5.1 Basic steady-state model 89 -- 5.5.2 Generalized steady-state algorithm 90 -- 5.6 Generational elitist strategies in GAs 91 -- 5.7 Michalewicz selection 92 -- 5.8 Boltzmann selection 93 -- 5.8.1 Boltzmann selection by scaling 93 -- 5.8.2 Simulated annealing 95 -- 5.8.3 PRSA method 95 -- 5.9 Other selection methods 96 -- 5.9.1 Greedy over-selection 96 -- 5.9.2".
catalog tableOfContents "Schema theorem, building blocks, and related topics 153 -- 8.2 Elements characterizing schemata 155 -- 8.3 Schema dynamics 157 -- 8.4 Effect of selection on schema dynamics 158 -- 8.4.1 Schema dynamics within selection 158 -- 8.4.2 Dynamics of above/below-average schema 161 -- 8.5 Effect of recombination on schema dynamics 163 -- 8.5.1 Schema disruption probability 163 -- 8.5.2 Actual disruption probability 165 -- 8.5.3 Survival probability 166 -- 8.6 Combined effect of selection and recombination on schema dynamics 166 -- 8.6.1 Schema dynamics within selection and crossover 167 -- 8.6.2 Qualitative results concerning schema dynamics 169 -- 8.7 Effect of mutation on schema dynamics 170 -- 8.8 Schema theorem 173 -- 8.8.1 Schema dynamics within selection and search operators 173 -- 8.8.2 Approximating schema dynamics 174 -- 8.8.3 Fundamental theorem 175 -- 8.9 Building block".
catalog tableOfContents "Specialized representation and hybridization within GAs 214 -- 10.2.1 Specific representation 214 -- 10.2.2 Hybridization 215 -- 10.2.3 Use of specific encoding and hybridization 216 -- 10.3 Parameter setting and adaptive GAs 218 -- 10.3.1 Parameter setting in GAs 218 -- 10.3.2 Parameter setting and representation adaptation 219 -- 10.3.3 Adaptive fitness of a search operator 221 -- 10.4 Adaptive GAs 223 -- 10.4.1 Adaptation problem 223 -- 10.4.2 Adaptive techniques based on fuzzy logic control 225 -- 11 Adaptive representations: messy genetic algorithms, delta- coding, and diploidic representation 231 -- 11.2 Principles of messy genetic algorithms 233 -- 11.2.1 Variable-length encoding 233 -- 11.2.2 Linkage problem 234 -- 11.2.3 Messy encoding 236 -- 11.2.4 Incompleteness and ambiguity 237 -- 11.3 Recombination within messy genetic operators 239 -- 11.3.1 Recombination".
catalog title "Evolutionary computation/ D. Dumitrescu ... [et al.].".
catalog type "text".