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| Back T., Fogel D.B., Michalewicz Z. — Evolutionary computation (Vol. 2. Advanced algorithms and operators) |
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| Предметный указатель |
Partitioning 50
Path planning 232
Payoff matrix 226
Penalty functions 41—48 79 176
Penalty functions, adaptive 43
Penalty functions, dynamic 44 45
Penalty functions, future directions 47
Penalty functions, static 43
Performance measures 128
Peripheral isolates 105
Permutations, coding 9
Phenotypes 81 111
Phenotypic mating restriction 96 97
Phenotypic sharing 88
Phenotypic-level recombination 157 158
Planning problems 9
Population sizing 89 134—141 158
Population structures 254 255
Positional bias 154
Predator-prey coevolution 228
Predicted square error (PSE) criterion 17
Predictive least squares (PLS) criterion 17
Preferability 33 34
Preference vector 34
Premature convergence 106
Process control 232
Productivity 154
Proportional selection 1 128 247
Punctuated crossover method 206
Punctuated equilibria 101—124
Punctuated equilibria, theory 106 107 120 121
q-ary 248
q-fold binary tournament selection 249 250
Quicksort 248
Random number generators 239—242
Real-valued vectors 157
Rechenberg's 1/5 success rule 172 175
Recombination operators 153 205—207
Recombination operators, adaptive techniques 160—164
Recombination operators, computation time 251
Recombination operators, predictive techniques 159 160
Recombination operators, static techniques 159
Recombination parameters 152—169
Recombination rate 156
Recombination techniques 129
Recombinative bias 155
Reduced surrogate recombination 156
Reduction strategy 112
Relative fitness 12 13
Relative fitness functions 225
Repair algorithms 56—61
Representation see "Specific representations"
Restricted mating 96—98
Restricted mating policy 94 95
Roulette wheel 247
Routing algorithms 112 113
Rule-based systems 161
Saturday morning problem 18
Scalability 255
Scheduling problems 9 50
Schema bias 155
Schema sampling 136—139
| Schemata 135 136 153 154
Search spaces 81
Search spaces, binary 198
Search spaces, continuous 189
Segregated genetic algorithm 72 73
Select 229
Selection 178
Selection methods 219
Selection operators 242
Selection operators, computation time 247—250
Selective self-adaptation 205
Self-adaptation 170 178 185 188—211 214
Self-adaptation, mutation parameters for 143 144
Self-adaptation, parameter control 189
Self-adaptive mutation 173
Self-adaptive parameter control 180
Self-adjusting algorithms 184
Sequential genetic algorithm (SGA) 113 114 119 120
Shared fitness 87—89
Sharing functions 87 88
Shifting balance 121
Shifting balance, theory 103 105
SIMD (single instruction, multiple data) system 99 127 128 254 257
Simulated annealing (SA) 212
Single instruction multiple data (SIMD) system 99 127 128 254 257
Single program, multiple data (SPMD) system 127 128
Speciation 93—100
Specific mate recognition system (SMRS) 104
sphere model 196 199
SPMD (single program, multiple data) system 127 128
Stasis 106
Stochastic universal sampling (SUS) 248
Strategy parameters 188
STROGANOFF 23
Subpopulations 105 118 119
Switch box 109
Symbioorganisms 227
Symbiosis 233
System identification problems 23
Tabu search (TS) 212 213
Tag bits 97—99 162
Tag-template method 95 96
Target vector approach 29
Taxon-exemplar scheme 94 95
Tix Tax 227
Toggle 229
Tournament selection 128
Tournament selection with continuously updated sharing 89
Tournament selection, q-fold binary 249 250
Tournaments 29 227
Transmission function in fitness domain 160
Transportation problem 62—64
Traveling saleman problem (TSP) 9 49 50 65—68 76 158 165 217 219 244 256
tree structures 23
Uniform recombination 154
UPDATEd 229 230
Variable epoch lengths 116
Vector-evaluated genetic algorithm (VEGA) 30 31 70
VLSI circuit design problem 108—113
Weighted sphere model 199
Weighted-sum approach 26 27
WSI-based GA machine 257
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