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| Àâòîðèçàöèÿ |
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| Ïîèñê ïî óêàçàòåëÿì |
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| Jacob C. — Illustrating Evolutionary Computation with Mathematica |
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| Ïðåäìåòíûé óêàçàòåëü |
Evolvica and EP, tournament selection 323—325
Evolvica and ES 260—265
Evolvica and ES, best and random selection 264
Evolvica and ES, central control 262
Evolvica and ES, comma strategy 261
Evolvica and ES, ES chromosome representation 213—215
Evolvica and ES, evaluation function 265
Evolvica and ES, evolution control function 261—263
Evolvica and ES, evolution loop 261
Evolvica and ES, placing individuals at specific locations 264
Evolvica and ES, plus strategy 261
Evolvica and ES, sample ES evolution experiment 263—265
Evolvica and ES, selection modes 262
Evolvica and ES, setting initial population 261
Evolvica and ES, starting a (1 + 5) ES 264—265
Evolvica and GA 169—173
Evolvica and GA, algorithm for GA evolution 171—173
Evolvica and GA, chromosome data structure 84
Evolvica and GA, chromosome generation 84 85
Evolvica and GA, comma strategy 171
Evolvica and GA, enumeration of all schema instances 179 199—202
Evolvica and GA, extraction of schema instances 197
Evolvica and GA, generating schema instances for a population 197
Evolvica and GA, generating schema instances over an alphabet 196—197
Evolvica and GA, generating schemata of fixed length 194—196
Evolvica and GA, options to control the GA evolution function 172
Evolvica and GA, plus strategy 171
Evolvica and GA, schema theorem and 194—202
Evolvica and GA, starting an experiment 171—172
Evolvica and GA, summary of GA implementations 169—170
Evolvica and GP,  genetic operators 408
Evolvica and GP, comma strategy 374 376 390
Evolvica and GP, evaluating mobiles by balance 383—385 388—389 390
Evolvica and GP, evolution of balanced mobiles 388—390
Evolvica and GP, functional and terminal building blocks 361
Evolvica and GP, further information 435
Evolvica and GP, generating mobile structures 385—387
Evolvica and GP, GP chromosome recombination 363—365
Evolvica and GP, GP term mutation 368—371
Evolvica and GP, GP term recombination 363
Evolvica and GP, plus strategy 374 376
Evolvica and GP, recombination at root position 361—362
Evolvica and GP, recombination function 360
Evolvica and GP, recombination of two terms 361 362
Evolvica and GP, recombination on list of terms 363 364
Evolvica and GP, starting evolution experiments 374
Evolvica and GP, term recombination 359—365
Evolvica and GP, visualizing mobiles 378—382
Evolvica string evolution implementation 12—21
Evolvica string evolution implementation, best individual 20
Evolvica string evolution implementation, evolution loop 21
Evolvica string evolution implementation, fitness by Hamming distance 20
Evolvica string evolution implementation, initial population 16
Evolvica string evolution implementation, mutation radius 17 18—19
Evolvica string evolution implementation, mutation rate 16—17 18—19
Evolvica string evolution implementation, next generation 19—20 21
Evolvica string evolution implementation, number decoding for strings 13—14
Evolvica string evolution implementation, number encoding for strings 13
Evolvica string evolution implementation, random string generation 16
Evolvica string evolution implementation, similarity measure for strings 14—16
Evolvica string evolution implementation, variation vector 16—17
Example artificial life (AL), bibliographical notes 469—470
Exons 155
Expansion mutation operator (GP) 431
Experimentum crucis of evolution strategies 212
Exploring Three-Dimensional Design Worlds Using Lindenmayer Systems and Genetic Programming 487
Expression, as adaptation step 62
Expression, duplication and 157—158
Expression, of individuals 59
Extraction of dominant alleles, chromosome interpretation 101—105
Extraction of dominant alleles, simulated diploidy 100—101
Extraction of schema instances 197
Feature space, phenotypical 58—60
Feedback, adaptive feedback components 60
Feedback, loop between genotypical and phenotypical levels 59—60
Finite state automata (FSA) (see also evolutionary programming; evolutionary programming at work; mutation operators on FSA)
Finite state automata (FSA),  package for 300
Finite state automata (FSA), accommodation phase 302
Finite state automata (FSA), automata pruning 331—332 337
Finite state automata (FSA), automatic generation of 320—322
Finite state automata (FSA), compact, perfect predictors for periodic finite state automata (FSA), sequences 330—331
Finite state automata (FSA), computer programs as 298—303
Finite state automata (FSA), deriving models of the environment 298—299
Finite state automata (FSA), evaluating automaton prediction quality 328—331
Finite state automata (FSA), evolution experiment 331—338 339—341
Finite state automata (FSA), evolutionary programming of 288—289
Finite state automata (FSA), extensions of FSA evolution 338 341—342
Finite state automata (FSA), final states 300
Finite state automata (FSA), fitness-dependent mutation 341—342
Finite state automata (FSA), generating a population of FSA 321—322
Finite state automata (FSA), generating a random (Mealy) machine 320—321
Finite state automata (FSA), generating a random FSA 321
Finite state automata (FSA), initial state 299—300
Finite state automata (FSA), majority logic recombination 342
Finite state automata (FSA), Mealy machines 299—301
Finite state automata (FSA), mutation operators on 303—320
Finite state automata (FSA), perfect predictor 302 303
Finite state automata (FSA), predicting the environment with 301—303
Finite state automata (FSA), recombination and 342
Finite state automata (FSA), response to input signals 300—301
Finite state automata (FSA), states and signals 299
Finite state automata (FSA), transitions 299
Finite state automata (FSA), variable mutation step sizes 338 341
Finite state machines see finite state automata (FSA)
Fitness inheritance 58
Fitness, fitness evolution and genome complexity 418—420
Fitness, breeding artificial flowers 499—500 503
Fitness, differential 58
Fitness, fitness-dependent EP mutation 341—342
Fitness, fitness-proportionate selection 162—164
Fitness, function for GP mobiles 384—385 388
Fitness, GP operator selection and 428
Fitness, Hamming distance as measure of 20
Fitness, in butterfly mimesis simulation 35—36
Fitness, L-system 477—479 481 482
Fitness, schema fitness 203—204
Fitness-proportionate selection 162—164
Fitness-proportionate selection,  function 163—164
Fitness-proportionate selection, differential survival probability 162
Fitness-proportionate selection, roulette wheel analogy 162—164
Fogel, David 75 76 294 342
Fogel, Lawrence J. 3 288 297 298 299 342 344
Foundations of Genetic Algorithms workshops 209
Fractal structure evolution 474—486
Fractal structure evolution, calculating similarity between generated sets and reference structure 477—478
Fractal structure evolution, evaluation 477—479
Fractal structure evolution, fitness 477 479 561
Fractal structure evolution, fitness evolution 479 481—482
Fractal structure evolution, genetic operators 477 482—486
Fractal structure evolution, genotype and phenotype evolution 481—482
Fractal structure evolution, GP 475 476
Fractal structure evolution, of fractal L-system 479 480—481
Fractal structure evolution, path toward optimal L-system 484—486
Fractal structure evolution, quadratic Koch island 475 476
Friedberg, R. M. 287
FSA see finite state automata (FSA)
GA see genetic algorithms
GA chromosomes (see also binary GA chromosomes; diploid GA chromosomes; genetic algorithms; genetic algorithms recombination; haploid GA chromosomes; polyploid GA chromosomes)
GA chromosomes, binary 84—88
GA chromosomes, compact output form 86
GA chromosomes, cross recombination 126—128
GA chromosomes, crossover of nonhomologous chromosomes 158—161
GA chromosomes, data structure 84
GA chromosomes, defined 83
GA chromosomes, deletion 154
GA chromosomes, diploid 96
GA chromosomes, diploidy and dominance on 95—105
GA chromosomes, duplication 156—158
GA chromosomes, haploid 83—95 96
GA chromosomes, interpretation 101—105
| GA chromosomes, inversion 151 152
GA chromosomes, polyploid 83—105
GA chromosomes, recombination of 133—143
GA chromosomes, RNA 88—93
GA chromosomes, visualization 86 87
GA chromosomes, with alleles from the interval [0, 1] 94
GA chromosomes, with fragment loss 154—155
GA chromosomes, with real-value alleles 93—95
Gaardner, Jostein 21 22
Gaussian density 219
Gaussian distributed random numbers in ES mutation 219—222
Gaylord, Richard 469
GECCO (Genetic and Evolutionary Computation Conference) 76 209 280 295 397
Gene pool convergence, evolution example 418
Gene pool convergence, compact gene groups and schema theorem 206
Gene pool convergence, mutation and 178
Gene pool convergence, recombination and 178—180
Gene pool diversity and adaptations 190
Gene pool sequences 64
General evolutionary algorithm scheme 63—75
General evolutionary algorithm scheme, algorithmic scheme of evolutionary parameter optimization 71—75
General evolutionary algorithm scheme, climbing in the fog example 64—75
General evolutionary algorithm scheme, encoding and decoding parameters 70—71
General evolutionary algorithm scheme, example optimization with GA 67—75
General evolutionary algorithm scheme, gene pool sequences 64
General evolutionary algorithm scheme, multimodal objective function 67—68 69—70
General evolutionary algorithm scheme, optimization on multimodal functions 64—67
General evolutionary algorithm scheme, reproductive plan 64 65
Genetic algorithms 79—209 (see also GA chromosomes)
Genetic Algorithms + Data Structures = Evolution Programs 208 396
Genetic algorithms at work 173—190
Genetic algorithms at work, decoding and evaluating genotypes 174—176
Genetic algorithms at work, effects of genetic operators 180—185
Genetic algorithms at work, GA evolution under variable environmental conditions 185—190 191—192
Genetic algorithms at work, recombination vs. mutation 176—180
Genetic algorithms at work, visualizing the genotypes 174 175
Genetic algorithms evolution schemes 168—169
Genetic algorithms evolution schemes, algorithm for GA evolution 171—173
Genetic algorithms evolution schemes, classical GA 168—169 170
Genetic algorithms evolution schemes, comma strategy 168 171 190 191
Genetic algorithms evolution schemes, further information 169
Genetic algorithms evolution schemes, GA selection 168—169
Genetic algorithms evolution schemes, plus strategy 168 171 190 192
Genetic algorithms evolution schemes, starting an experiment 171—172
Genetic algorithms evolution schemes, steady state GA 169
Genetic algorithms evolution schemes, under variable environmental conditions 185—190 191—192
Genetic algorithms evolution schemes, with elitist selection 169
Genetic Algorithms in Search, Optimization, and Machine Learning 207
Genetic algorithms mutation 105—121
Genetic algorithms mutation, diploid chromosomes 110—112
Genetic algorithms mutation, effect of mutation operator 180—184
Genetic algorithms mutation, effect on population structure 177—178
Genetic algorithms mutation, FSA mutation operators vs 303—304
Genetic algorithms mutation, haploid chromosomes 106—108
Genetic algorithms mutation, homologous alleles 110
Genetic algorithms mutation, minor role of 81
Genetic algorithms mutation, new alleles introduced by 105
Genetic algorithms mutation, point mutation operator 106
Genetic algorithms mutation, polyploid chromosomes 108—110
Genetic algorithms mutation, recombination vs. mutation 176—180
Genetic algorithms mutation, RNA chromosomes 112—117
Genetic algorithms mutation, schema theorem and 205
Genetic algorithms mutation, visualization using facial expressions 117—121 122
Genetic algorithms recombination 121—148
Genetic algorithms recombination, binary recombination 124—126
Genetic algorithms recombination, central role of 123
Genetic algorithms recombination, cross recombination of chromosomes 126—128
Genetic algorithms recombination, discrete recombination 123—124 126
Genetic algorithms recombination, effect of recombination operator 183 184
Genetic algorithms recombination, gene pool convergence effects 178—180
Genetic algorithms recombination, masked recombination 128—131 148 149
Genetic algorithms recombination, meiotic recombination of diploid chromosomes 143—146
Genetic algorithms recombination, multirecombination 131—133
Genetic algorithms recombination, mutation vs 176—180
Genetic algorithms recombination, of GA chromosomes 133—143
Genetic algorithms recombination, on lists 123—133
Genetic algorithms recombination, schema theorem and 204—205
Genetic algorithms recombination, with faces 146—148 149
Genetic algorithms, additional genetic operators 148—161
Genetic algorithms, analogy to observable mutative events 148
Genetic algorithms, applications 82
Genetic algorithms, bibliographical notes 207—209
Genetic algorithms, binary encoding for 82
Genetic algorithms, classifier population adaptations 290
Genetic algorithms, comma strategy 162 171 190 191
Genetic algorithms, covariance and selection theorem 207
Genetic algorithms, crossover of nonhomologous chromosomes 158—161
Genetic algorithms, deletion operator 153—155
Genetic algorithms, described 79—80
Genetic algorithms, diploidy and dominance on GA chromosomes 95—105
Genetic algorithms, discrete encoding principle 80
Genetic algorithms, dualism principle 80 81
Genetic algorithms, duplication operator 155—158
Genetic algorithms, elementary building blocks principle 80 81—82
Genetic algorithms, evolution schemes 168—169 170
Genetic algorithms, evolutionary algorithms and 344
Genetic algorithms, GA recombination 121—148
Genetic algorithms, GA strategy 170
Genetic algorithms, GP and 345—346 358—359 367 372 373
Genetic algorithms, haploid GA chromosomes 83—95
Genetic algorithms, hierarchical 293
Genetic algorithms, inversion operator 150—153
Genetic algorithms, optimization example 67—75
Genetic algorithms, perpetual novelty and 79 81 105 121—122
Genetic algorithms, plus strategy 162 171 190 192
Genetic algorithms, point mutation on GA chromosomes 105—121
Genetic algorithms, polyploid GA chromosomes 83—105
Genetic algorithms, principles of 80—82
Genetic algorithms, program induction with 291
Genetic algorithms, reproduction and recombination principle 80—81
Genetic algorithms, schema theorem for 190—207
Genetic algorithms, selection and GA evolution schemes 161—169
Genetic algorithms, with Evolvica 169—173
Genetic and Evolutionary Computation Conference (GECCO) 76 209 280 295 397
Genetic L-system programming (GLP) 490 517 518
Genetic operators (see also specific operators)
Genetic operators, example 407 408 420—422
Genetic operators, ArtFlowers 497 498
Genetic operators, competition 427—428
Genetic operators, ES graphical notation for 253
Genetic operators, for LISP programs 292
Genetic operators, L-system 477 482—486
Genetic operators, operator weight adaptation 428 430
Genetic programming 345—397 399—435
Genetic Programming and Data Structures 396—397
Genetic Programming Conference 295
Genetic programming evolution scheme 371—377
Genetic programming evolution scheme, copy operator 372
Genetic programming evolution scheme, evaluation and best selection 373
Genetic programming evolution scheme, GA scheme vs 372
Genetic programming evolution scheme, initialization 371—372
Genetic programming evolution scheme, mutation operator 372
Genetic programming evolution scheme, notation 373
Genetic programming evolution scheme, operator application 373
Genetic programming evolution scheme, operator selection 372—373
Genetic programming evolution scheme, recombination operator 372
Genetic programming evolution scheme, starting evolution experiments 374
Genetic Programming II: Automatic Discovery of Reusable Programs 392 435
Genetic Programming III: Darwinian Invention and Problem Solving 396 435
Genetic programming in action 377—392
Genetic programming in action, encoding mobiles 377—378
Genetic programming in action, generating mobile structures 385—388
Genetic programming in action, GP evolution of balanced mobiles 388—392 393—395
Genetic programming in action, graphical representation of mobiles 378—382
Genetic programming in action, mobile evaluation by balance 382—385 388—389 390
Genetic programming mutation 367—371
Genetic programming mutation, mutation operator 408 421 422
Genetic programming mutation, advanced mutation operators 431—432
Genetic programming mutation, collapse subtree mutation operator 432
Genetic programming mutation, duplication operator 432
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