Jacob C. — Illustrating Evolutionary Computation with Mathematica :: Электронная библиотека попечительского совета мехмата МГУ

Главная Ex Libris Книги Журналы Статьи Серии Каталог Wanted Загрузка ХудЛит Справка Поиск по индексам Поиск Форум

Авторизация

Поиск по указателям

Jacob C. — Illustrating Evolutionary Computation with Mathematica

Обсудите книгу на научном форуме

Нашли опечатку?
Выделите ее мышкой и нажмите Ctrl+Enter

Название: Illustrating Evolutionary Computation with Mathematica

Автор: Jacob C.

Аннотация:

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:

Рубрика: Математика/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Год издания: 2001

Количество страниц: 578

Добавлена в каталог: 17.11.2013

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID

Предметный указатель

RNA chromosomes, silent mutations      117
RNA chromosomes, summary of encoding by RNA triplets      90
RNA chromosomes, translation of codons to proteins      88—90
RNA chromosomes, triplet encoding of amino acids      88
RNA chromosomes, triplet table over RNA alphabet      88—89
RNA chromosomes, visualization      91—92
RNA chromosomes, with nucleotide bases      91—92 94
Roll turtle move operations      461
Rozenberg, G.      439 486
Rules, $\verb"AntTracker"$ reduction rules      404 406
Rules, $\verb"labelToAminoAcid"$ rules      89 90
Rules, $\verb"StepSizeAdaptation"$ rule      226 227
Rules, $\verb"tripletToLabel"$ rules      89
Rules, ArtFlower L-rules      495—497 498 509
Rules, cellular automata      441 442 446
Rules, classifier rule      289
Rules, DOL-systems      450 451 452 454
Rules, hierarchical GP-rule systems      292
Rules, Hilbert rules      463
Rules, IL-systems      458 473
Schema theorem for GA      190—207
Schema theorem for GA, as fundamental theorem of GA      205
Schema theorem for GA, covariance and selection theorem vs      207
Schema theorem for GA, defining length of a schema      194
Schema theorem for GA, described      190
Schema theorem for GA, enumeration of all schema instances      179 199—202
Schema theorem for GA, experiments with schemata      194—202
Schema theorem for GA, extraction of schema instances      197
Schema theorem for GA, GA-deceptive problems      206
Schema theorem for GA, generating schema instances for a population      197
Schema theorem for GA, generating schema instances over an alphabet      196—197
Schema theorem for GA, generating schemata of fixed length      194—196
Schema theorem for GA, instances of a schema      193
Schema theorem for GA, inversion and      206
Schema theorem for GA, mutation resistance      205
Schema theorem for GA, order of a schema      194
Schema theorem for GA, overview      205—206
Schema theorem for GA, problems with      206—207
Schema theorem for GA, recombination survival      204—205
Schema theorem for GA, schema defined      193
Schema theorem for GA, schema fitness      203—204
Schema theorem for GA, schemata as building block filters      202
Schema theorem for GA, schemata defined      190
Schema theorem for GA, selection filter survival      203—204
Schema theorem for GA, survival of building blocks      202—206 575
Schemata      see schema theorem for GA
Schwefel, Hans — Paul      212 225 279
Search space for biomorphs      47
Second-order evolution      226 229
Selection      (see also evolution strategies)
Selection and reproduction schemes selection, $\verb"AntTracker"$ selection templates      407—408
Selection functions      161—167
Selection functions, $\verb"selectElite"$ function      166—167
Selection functions, $\verb"selectFitProp"$ function      163—164
Selection functions, $\verb"selectRankBased"$ function      165—166
Selection functions, $\verb"selectTournament"$ function      323—325
Selection functions, additional functions      167
Selection functions, elitist selection      166—167
Selection functions, fitness-proportionate selection      162—164
Selection functions, plus and comma strategies      162 171 190 191—192
Selection functions, random survival      162
Selection functions, rank-based selection      164—166
Selection functions, tournament selection (FSA)      323—325
Selection, as principle of evolution      2 6
Selection, cumulative      5—33
Selection, directional      34
Selection, elitist      166—167
Selection, EP selection and evolution scheme      322—325
Selection, ES graphical notation for      253
Selection, ES selection and reproduction schemes      252—256
Selection, evaluation and      59
Selection, fitness-proportionate      162—164
Selection, GA evolution schemes and      161—169
Selection, GA selection      168—169
Selection, GP selection      372—373
Selection, in evolutionary parameter optimization scheme      73
Selection, phenotypical      58—60
Selection, random selection on lists      351
Selection, rank-based      164—166
Selection, single-step      7
Selection, surviving the GA selection filter      203—204
Self — Reproduction special issue of Artificial Life journal      469
Selfish Gene, The      52
Sexual reproduction as GA principle      80—81
Sierpinski triangle      475
Silent mutations      1 17
Similarity, calculating between generated sets and reference structure      477—478
Similarity, Hamming distance for measuring      8
Similarity, measure for strings      14—16
Simple Genetic Algorithm, The      208
Simulated diploidy      99—101
Simulated diploidy, all possible allele combinations      100
Simulated diploidy, dominance and recessivity      99—100
Simulated diploidy, dominance relation      100
Simulated diploidy, extraction of dominant alleles      100—101
Simulated diploidy, table of dominant alleles      101
Simulated mimesis of butterflies      see butterfly mimesis simulation
Simulating Society      469
Sinc function      267
Single-step selection      7
Sipper, Moshe      469
Smith, John Maynard      52
States of FSA, adding      306—307 308
States of FSA, changing the initial state      304—305
States of FSA, deleting      307—309
States of FSA, final states      300
States of FSA, initial state      299—300
States of FSA, modifying the set of states      305—309
States of FSA, signals and      299
States, adaptations as      4—5
Steady state GA      169
Stochastic L-systems      474
String evolution example, analyzing the first string evolution      24
String evolution example, as "key experiment"      5—6
String evolution example, best individual      20
String evolution example, coarse adjustment vs. fine tuning      24

String evolution example, comparison of three-string evolutions      32—33
String evolution example, conservative parameter settings      25
String evolution example, decreased adaptation rate in final phase      24
String evolution example, evolution loop      21
String evolution example, Evolvica implementation      12—21
String evolution example, experiments      21—33
String evolution example, Hamming distances      8 9 10 20 25 27 28 32
String evolution example, illustrations of string evolution      22—23 28—30 32
String evolution example, increased mutation radius, effects of      25—28
String evolution example, increased mutation, effects of      28—32
String evolution example, initial population      16
String evolution example, mutation function      10—12
String evolution example, mutation radius      11—12 17 18—19
String evolution example, mutation rate      11 16—17 18—19
String evolution example, next generation      19—20 21
String evolution example, number decoding for strings      13—14
String evolution example, number encoding for strings      8—9 13
String evolution example, objective string      6
String evolution example, random string generation      16
String evolution example, selection and mutation algorithm      9—12
String evolution example, selection and mutation scheme      7—9
String evolution example, similarity measure for strings      14—16
String evolution example, single-step selection      7
String evolution example, starting a string evolution      22
String evolution example, string alphabet      6
String evolution example, task description      6
String evolution example, variation vector      16—17
String evolution illustrations, mutation radius: 2, mutation rate: 0.1      22—23 25 32
String evolution illustrations, mutation radius: 2, mutation rate: 0.2      28—31 32
String evolution illustrations, mutation radius: 4, mutation rate: 0.1      26—27 32
Structure space, genotypical      58—60
Structures as adaptive system components      60 61
Subtree mutation operator (GP)      431
Successor in L-systems      451
Superindividuals, problem of      164—165
Survival, adaptation as crucial factor for      5
Survival, GP evolution of balanced mobiles      390
Survival, implicit goals defined by      42 45
Survival, random      162
Survival, schema theorem for GA and      202—206
Symbolic expressions      see genetic programming on symbolic expressions
Szilard, A. L.      458
Table L-systems      474
Term structures      see genetic programming on symbolic expressions
Terms as tree structures      347—348
Theory of Evolution, The      52
TIERRA system      294
Time requirements for single-step selection      7
Todd, Stephen      53
Toth, Z.      487
Tournament selection (FSA)      323—325
Transactions on Evolutionary Computation      76
Transcription phase of cellular automata      446
Transitions in FSA      299
Transitions in FSA, adding      309—311
Transitions in FSA, changing input symbol for      313—314 315
Transitions in FSA, changing output symbol for      314—316
Transitions in FSA, changing source for      316—318
Transitions in FSA, changing target for      318—320
Transitions in FSA, deleting      311—313
Transitions in FSA, modifying the set of transitions      309—313
Transitions in FSA, modifying transitions      313—320
Transitions in FSA, network for added states      306—307
Translation phase of cellular automata      446
Tree structures, L-systems      466 467 468
Tree structures, symbolic expressions as      365—366
Tree structures, terms as      347—348
Tree-structured chromosomes      346—347
Triple sine (ES multimodal test function)      266—268
Triplet encoding of amino acids      88—90
Turing machine universal constructor      443
Turn turtle move operation      462
Turtle      462 463
Turtle drawing tool example      (see also Lindenmayer systems)
Turtle drawing tool example, $\verb"turtleInterpretation"$ function      459 464
Turtle drawing tool example, changing turtle orientation      460 462
Turtle drawing tool example, changing turtle position      459—460
Turtle drawing tool example, drawing graphical elements      462
Turtle drawing tool example, energetic turtle      519
Turtle drawing tool example, environmentally sensitive turtle interpretation      519
Turtle drawing tool example, Hilbert curve demo      463—465
Turtle drawing tool example, macro turtle commands      490—491
Turtle drawing tool example, modeling of branching structures      465—467 468
Turtle drawing tool example, position and orientation of a turtle      458 459
Turtle drawing tool example, turtle interpretation      458 519
Turtle drawing tool example, variation of attributes      462—465
Turtle, attributes      462—465
Turtle, interpretation      458 519
Typed GP      366—367
Uncertain Programming      208
Uniformly distributed random variables      227
Universal constructors      443
Unweaving the Rainbow      52
Update rules for cellular automata      441—442
V $\acute{a}$ nyi, R.      487
Variability      57—58
Variation vector      16—17
Variation, as adaptation step      63
Variation, genotypical      58—60
Variation, in evolutionary parameter optimization scheme      73
Videos on genetic programming      396
Visual Models of Plants Interacting with Their Environment      525
Vit $\acute{a}$ nyi, P.      486
Vogel, Sebastian      3
von Neumann neighborhood for cellular automata      441
von Neumann, John      80 443
Vose, Michael      208
Walsh, Michael      288 298 344
Wellin, Paul      469
Wildwood: The Evolution of L-System Plants for Virtual Environments      526
Wilson, S.      292
Wirth, Niklaus      396
Wolfram, Stephen      440 467
Word sequence for D0L-systems      450
Yaw turtle move operations      461
Young, David      3 52

1 2 3 4 5 6 7

О проекте