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Jacob C. Ч Illustrating Evolutionary Computation with Mathematica
Jacob C. Ч Illustrating Evolutionary Computation with Mathematica



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Ќазвание: Illustrating Evolutionary Computation with Mathematica

јвтор: Jacob C.

јннотаци€:

An essential capacity of intelligence is the ability to learn. An artificially intelligent system that could learn would not have to be programmed for every eventuality; it could adapt to its changing environment and conditions just as biological systems do. Illustrating Evolutionary Computation with Mathematica introduces evolutionary computation to the technically savvy reader who wishes to explore this fascinating and increasingly important field. Unique among books on evolutionary computation, the book also explores the application of evolution to developmental processes in nature, such as the growth processes in cells and plants. If you are a newcomer to the evolutionary computation field, an engineer, a programmer, or even a biologist wanting to learn how to model the evolution and coevolution of plants, this book will provide you with a visually rich and engaging account of this complex subject.


язык: en

–убрика: ћатематика/

—татус предметного указател€: √отов указатель с номерами страниц

ed2k: ed2k stats

√од издани€: 2001

 оличество страниц: 578

ƒобавлена в каталог: 17.11.2013

ќперации: ѕоложить на полку | —копировать ссылку дл€ форума | —копировать ID
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ѕредметный указатель
Evolvica and EP, tournament selection      323Ч325
Evolvica and ES      260Ч265
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      213Ч215
Evolvica and ES, evaluation function      265
Evolvica and ES, evolution control function      261Ч263
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      263Ч265
Evolvica and ES, selection modes      262
Evolvica and ES, setting initial population      261
Evolvica and ES, starting a (1 + 5) ES      264Ч265
Evolvica and GA      169Ч173
Evolvica and GA, algorithm for GA evolution      171Ч173
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 199Ч202
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      196Ч197
Evolvica and GA, generating schemata of fixed length      194Ч196
Evolvica and GA, options to control the GA evolution function      172
Evolvica and GA, plus strategy      171
Evolvica and GA, schema theorem and      194Ч202
Evolvica and GA, starting an experiment      171Ч172
Evolvica and GA, summary of GA implementations      169Ч170
Evolvica and GP, $\verb"AntTracker"$ genetic operators      408
Evolvica and GP, comma strategy      374 376 390
Evolvica and GP, evaluating mobiles by balance      383Ч385 388Ч389 390
Evolvica and GP, evolution of balanced mobiles      388Ч390
Evolvica and GP, functional and terminal building blocks      361
Evolvica and GP, further information      435
Evolvica and GP, generating mobile structures      385Ч387
Evolvica and GP, GP chromosome recombination      363Ч365
Evolvica and GP, GP term mutation      368Ч371
Evolvica and GP, GP term recombination      363
Evolvica and GP, plus strategy      374 376
Evolvica and GP, recombination at root position      361Ч362
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      359Ч365
Evolvica and GP, visualizing mobiles      378Ч382
Evolvica string evolution implementation      12Ч21
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 18Ч19
Evolvica string evolution implementation, mutation rate      16Ч17 18Ч19
Evolvica string evolution implementation, next generation      19Ч20 21
Evolvica string evolution implementation, number decoding for strings      13Ч14
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      14Ч16
Evolvica string evolution implementation, variation vector      16Ч17
Example artificial life (AL), bibliographical notes      469Ч470
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      157Ч158
Expression, of individuals      59
Extraction of dominant alleles, chromosome interpretation      101Ч105
Extraction of dominant alleles, simulated diploidy      100Ч101
Extraction of schema instances      197
Feature space, phenotypical      58Ч60
Feedback, adaptive feedback components      60
Feedback, loop between genotypical and phenotypical levels      59Ч60
Finite state automata (FSA)      (see also evolutionary programming; evolutionary programming at work; mutation operators on FSA)
Finite state automata (FSA), $\verb"Automata"$ package for      300
Finite state automata (FSA), accommodation phase      302
Finite state automata (FSA), automata pruning      331Ч332 337
Finite state automata (FSA), automatic generation of      320Ч322
Finite state automata (FSA), compact, perfect predictors for periodic finite state automata (FSA), sequences      330Ч331
Finite state automata (FSA), computer programs as      298Ч303
Finite state automata (FSA), deriving models of the environment      298Ч299
Finite state automata (FSA), evaluating automaton prediction quality      328Ч331
Finite state automata (FSA), evolution experiment      331Ч338 339Ч341
Finite state automata (FSA), evolutionary programming of      288Ч289
Finite state automata (FSA), extensions of FSA evolution      338 341Ч342
Finite state automata (FSA), final states      300
Finite state automata (FSA), fitness-dependent mutation      341Ч342
Finite state automata (FSA), generating a population of FSA      321Ч322
Finite state automata (FSA), generating a random (Mealy) machine      320Ч321
Finite state automata (FSA), generating a random FSA      321
Finite state automata (FSA), initial state      299Ч300
Finite state automata (FSA), majority logic recombination      342
Finite state automata (FSA), Mealy machines      299Ч301
Finite state automata (FSA), mutation operators on      303Ч320
Finite state automata (FSA), perfect predictor      302 303
Finite state automata (FSA), predicting the environment with      301Ч303
Finite state automata (FSA), recombination and      342
Finite state automata (FSA), response to input signals      300Ч301
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, $\verb"AntTracker"$ fitness evolution and genome complexity      418Ч420
Fitness, breeding artificial flowers      499Ч500 503
Fitness, differential      58
Fitness, fitness-dependent EP mutation      341Ч342
Fitness, fitness-proportionate selection      162Ч164
Fitness, function for GP mobiles      384Ч385 388
Fitness, GP operator selection and      428
Fitness, Hamming distance as measure of      20
Fitness, in butterfly mimesis simulation      35Ч36
Fitness, L-system      477Ч479 481 482
Fitness, schema fitness      203Ч204
Fitness-proportionate selection      162Ч164
Fitness-proportionate selection, $\verb"selectFitProp"$ function      163Ч164
Fitness-proportionate selection, differential survival probability      162
Fitness-proportionate selection, roulette wheel analogy      162Ч164
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      474Ч486
Fractal structure evolution, calculating similarity between generated sets and reference structure      477Ч478
Fractal structure evolution, evaluation      477Ч479
Fractal structure evolution, fitness      477 479 561
Fractal structure evolution, fitness evolution      479 481Ч482
Fractal structure evolution, genetic operators      477 482Ч486
Fractal structure evolution, genotype and phenotype evolution      481Ч482
Fractal structure evolution, GP      475 476
Fractal structure evolution, of fractal L-system      479 480Ч481
Fractal structure evolution, path toward optimal L-system      484Ч486
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      84Ч88
GA chromosomes, compact output form      86
GA chromosomes, cross recombination      126Ч128
GA chromosomes, crossover of nonhomologous chromosomes      158Ч161
GA chromosomes, data structure      84
GA chromosomes, defined      83
GA chromosomes, deletion      154
GA chromosomes, diploid      96
GA chromosomes, diploidy and dominance on      95Ч105
GA chromosomes, duplication      156Ч158
GA chromosomes, haploid      83Ч95 96
GA chromosomes, interpretation      101Ч105
GA chromosomes, inversion      151 152
GA chromosomes, polyploid      83Ч105
GA chromosomes, recombination of      133Ч143
GA chromosomes, RNA      88Ч93
GA chromosomes, visualization      86 87
GA chromosomes, with alleles from the interval [0, 1]      94
GA chromosomes, with fragment loss      154Ч155
GA chromosomes, with real-value alleles      93Ч95
Gaardner, Jostein      21 22
Gaussian density      219
Gaussian distributed random numbers in ES mutation      219Ч222
Gaylord, Richard      469
GECCO (Genetic and Evolutionary Computation Conference)      76 209 280 295 397
Gene pool convergence, $\verb"AntTracker"$ evolution example      418
Gene pool convergence, compact gene groups and schema theorem      206
Gene pool convergence, mutation and      178
Gene pool convergence, recombination and      178Ч180
Gene pool diversity and adaptations      190
Gene pool sequences      64
General evolutionary algorithm scheme      63Ч75
General evolutionary algorithm scheme, algorithmic scheme of evolutionary parameter optimization      71Ч75
General evolutionary algorithm scheme, climbing in the fog example      64Ч75
General evolutionary algorithm scheme, encoding and decoding parameters      70Ч71
General evolutionary algorithm scheme, example optimization with GA      67Ч75
General evolutionary algorithm scheme, gene pool sequences      64
General evolutionary algorithm scheme, multimodal objective function      67Ч68 69Ч70
General evolutionary algorithm scheme, optimization on multimodal functions      64Ч67
General evolutionary algorithm scheme, reproductive plan      64 65
Genetic algorithms      79Ч209 (see also GA chromosomes)
Genetic Algorithms + Data Structures = Evolution Programs      208 396
Genetic algorithms at work      173Ч190
Genetic algorithms at work, decoding and evaluating genotypes      174Ч176
Genetic algorithms at work, effects of genetic operators      180Ч185
Genetic algorithms at work, GA evolution under variable environmental conditions      185Ч190 191Ч192
Genetic algorithms at work, recombination vs. mutation      176Ч180
Genetic algorithms at work, visualizing the genotypes      174 175
Genetic algorithms evolution schemes      168Ч169
Genetic algorithms evolution schemes, algorithm for GA evolution      171Ч173
Genetic algorithms evolution schemes, classical GA      168Ч169 170
Genetic algorithms evolution schemes, comma strategy      168 171 190 191
Genetic algorithms evolution schemes, further information      169
Genetic algorithms evolution schemes, GA selection      168Ч169
Genetic algorithms evolution schemes, plus strategy      168 171 190 192
Genetic algorithms evolution schemes, starting an experiment      171Ч172
Genetic algorithms evolution schemes, steady state GA      169
Genetic algorithms evolution schemes, under variable environmental conditions      185Ч190 191Ч192
Genetic algorithms evolution schemes, with elitist selection      169
Genetic Algorithms in Search, Optimization, and Machine Learning      207
Genetic algorithms mutation      105Ч121
Genetic algorithms mutation, diploid chromosomes      110Ч112
Genetic algorithms mutation, effect of mutation operator      180Ч184
Genetic algorithms mutation, effect on population structure      177Ч178
Genetic algorithms mutation, FSA mutation operators vs      303Ч304
Genetic algorithms mutation, haploid chromosomes      106Ч108
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      108Ч110
Genetic algorithms mutation, recombination vs. mutation      176Ч180
Genetic algorithms mutation, RNA chromosomes      112Ч117
Genetic algorithms mutation, schema theorem and      205
Genetic algorithms mutation, visualization using facial expressions      117Ч121 122
Genetic algorithms recombination      121Ч148
Genetic algorithms recombination, binary recombination      124Ч126
Genetic algorithms recombination, central role of      123
Genetic algorithms recombination, cross recombination of chromosomes      126Ч128
Genetic algorithms recombination, discrete recombination      123Ч124 126
Genetic algorithms recombination, effect of recombination operator      183 184
Genetic algorithms recombination, gene pool convergence effects      178Ч180
Genetic algorithms recombination, masked recombination      128Ч131 148 149
Genetic algorithms recombination, meiotic recombination of diploid chromosomes      143Ч146
Genetic algorithms recombination, multirecombination      131Ч133
Genetic algorithms recombination, mutation vs      176Ч180
Genetic algorithms recombination, of GA chromosomes      133Ч143
Genetic algorithms recombination, on lists      123Ч133
Genetic algorithms recombination, schema theorem and      204Ч205
Genetic algorithms recombination, with faces      146Ч148 149
Genetic algorithms, additional genetic operators      148Ч161
Genetic algorithms, analogy to observable mutative events      148
Genetic algorithms, applications      82
Genetic algorithms, bibliographical notes      207Ч209
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      158Ч161
Genetic algorithms, deletion operator      153Ч155
Genetic algorithms, described      79Ч80
Genetic algorithms, diploidy and dominance on GA chromosomes      95Ч105
Genetic algorithms, discrete encoding principle      80
Genetic algorithms, dualism principle      80 81
Genetic algorithms, duplication operator      155Ч158
Genetic algorithms, elementary building blocks principle      80 81Ч82
Genetic algorithms, evolution schemes      168Ч169 170
Genetic algorithms, evolutionary algorithms and      344
Genetic algorithms, GA recombination      121Ч148
Genetic algorithms, GA strategy      170
Genetic algorithms, GP and      345Ч346 358Ч359 367 372 373
Genetic algorithms, haploid GA chromosomes      83Ч95
Genetic algorithms, hierarchical      293
Genetic algorithms, inversion operator      150Ч153
Genetic algorithms, optimization example      67Ч75
Genetic algorithms, perpetual novelty and      79 81 105 121Ч122
Genetic algorithms, plus strategy      162 171 190 192
Genetic algorithms, point mutation on GA chromosomes      105Ч121
Genetic algorithms, polyploid GA chromosomes      83Ч105
Genetic algorithms, principles of      80Ч82
Genetic algorithms, program induction with      291
Genetic algorithms, reproduction and recombination principle      80Ч81
Genetic algorithms, schema theorem for      190Ч207
Genetic algorithms, selection and GA evolution schemes      161Ч169
Genetic algorithms, with Evolvica      169Ч173
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, $\verb"AntTracker"$ example      407 408 420Ч422
Genetic operators, ArtFlowers      497 498
Genetic operators, competition      427Ч428
Genetic operators, ES graphical notation for      253
Genetic operators, for LISP programs      292
Genetic operators, L-system      477 482Ч486
Genetic operators, operator weight adaptation      428 430
Genetic programming      345Ч397 399Ч435
Genetic Programming and Data Structures      396Ч397
Genetic Programming Conference      295
Genetic programming evolution scheme      371Ч377
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      371Ч372
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      372Ч373
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      377Ч392
Genetic programming in action, encoding mobiles      377Ч378
Genetic programming in action, generating mobile structures      385Ч388
Genetic programming in action, GP evolution of balanced mobiles      388Ч392 393Ч395
Genetic programming in action, graphical representation of mobiles      378Ч382
Genetic programming in action, mobile evaluation by balance      382Ч385 388Ч389 390
Genetic programming mutation      367Ч371
Genetic programming mutation, $\verb"AntTracker"$ mutation operator      408 421 422
Genetic programming mutation, advanced mutation operators      431Ч432
Genetic programming mutation, collapse subtree mutation operator      432
Genetic programming mutation, duplication operator      432
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