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

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Jacob C. — Illustrating Evolutionary Computation with Mathematica

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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.

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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

Meta-evolution strategies, climbing meta-mountains      260
Meta-evolution strategies, island model and      258—260
Meta-evolution strategies, meta-evolution and strategy parameter      225
Meta-evolution strategies, population islands      260
Meta-evolution strategies, subpopulations as meta-individuals      258
Meta-evolution strategies, with subpopulations      258—260
Meta-evolution strategies, with three subpopulations      271—272 273
Meta-learning, evolution as      79
Michalewicz, Marek      526
Michalewicz, Zbigniew      208 396
Michielssen, E.      208 569
Mimesis      (see also butterfly mimesis simulation)
Mimesis, butterfly color adaptations      33—34
Mimesis, butterfly mimesis simulation      34—42
Mimesis, color adaptations      33
Mimesis, defined      33
Mimesis, directional selection for      34
Mimesis, industrialization and      33—34
Mimesis, melanistic moths      34
Mimesis, phytomimesis      33
Minimization vs. maximization optimization      69
Mitchell, Melanie      207
Mobiles (GP), $\verb"mobileExprForm"$ function      386—387
Mobiles (GP), $\verb"mobileFitness"$ function      384—385 388
Mobiles (GP), $\verb"mobileFunctions"$ function      385—386
Mobiles (GP), $\verb"mobileTermForm"$ function      387
Mobiles (GP), $\verb"mobileTerminals"$ function      385 386
Mobiles (GP), $\verb"mobileWeight"$ function      383
Mobiles (GP), encoding      377—378
Mobiles (GP), evaluation by balance      382—385 388—389 390
Mobiles (GP), evolution of balanced mobiles      388—392 393—395
Mobiles (GP), generating structures      385—388
Mobiles (GP), graphical representation of      378—382
Mobiles (GP), mobile bars      377—378
Mobiles (GP), well-balanced      377
Mock, Kenrick      526
Model of evolution      57—60
Model of evolution, differential fitness      58
Model of evolution, fitness inheritance      58
Model of evolution, genotypical variation      58—60
Model of evolution, phenotypical selection      58—60
Model of evolution, variability      57—58
Modeling adaptive systems, adaptation steps      62—63
Modeling Collective Phenomena in the Sciences      526
Modeling Nature      469
Modern Cellular Automata: Theory and Applications      468
Modularization of programs, GP      432
Moore neighborhood for cellular automata      441
Morphogenesis, agents of      439
Morphogenesis, in nature      439
Moths, melanistic      34
Move operations for turtle      459—462
MSA      see mutative step size adaptation (MSA) in ES
Multimodal functions, constrained optimization      69
Multimodal functions, ES optimization of      266
Multimodal functions, global maximum      68
Multimodal functions, local maxima      68—69
Multimodal functions, multimodality and function theory      266
Multimodal functions, noisy objective function      67 68
Multimodal functions, objective function      67—68 69—70 186 188
Multimodal functions, optimization on      64—67
Multimodal functions, triple sinc (ES test function)      266—268
Multimodal search spaces, defined      64
Multimodal search spaces, example      64 66
Multimodal search spaces, gradient-following strategies in      66—67
Multioperator GP      426
Multirecombination of ES chromosomes, discrete      233—235
Multirecombination of ES chromosomes, global      249—250
Multirecombination of ES chromosomes, in Mathematica      243—245
Multirecombination of ES chromosomes, intermediate      233—235
Multirecombination of ES chromosomes, local      247—249
Multirecombination of ES chromosomes, on parameter lists      233—234
Multirecombination of GA chromosomes      131—133
Multirecombination of GA chromosomes, binary multirecombination      132
Multirecombination of GA chromosomes, discrete componentwise      126
Multirecombination of GA chromosomes, recombination mask      126
Mutated assembler programs      287—288
Mutated assembler programs, automatic programming vs. random search      287—288
Mutated assembler programs, success counter and adaptive mutation      287
Mutation      (see also evolution strategies mutation; genetic algorithms mutation; genetic programming mutation; mutation operators on FSA)
Mutation operator      (see also evolution strategies mutation; genetic algorithms mutation; genetic programming mutation; mutation operators on FSA)
Mutation operator, $\verb"AntTracker"$       408 421 422
Mutation operator, ArtFlowers      498
Mutation operator, L-system      477
Mutation operator, on FSA      303—320
Mutation operators on FSA      303—320
Mutation operators on FSA, adding a state      306—307 308
Mutation operators on FSA, adding a transition      309—311
Mutation operators on FSA, changing a transition’s input symbol      313—314 315
Mutation operators on FSA, changing a transition’s output symbol      314—316
Mutation operators on FSA, changing a transition’s source      316—318
Mutation operators on FSA, changing a transition’s target      318—320
Mutation operators on FSA, changing the initial state      304—305
Mutation operators on FSA, deleting a state      307—309
Mutation operators on FSA, deleting a transition      311—313
Mutation operators on FSA, fitness-dependent mutation      341—342
Mutation operators on FSA, GA mutation operators vs      303—304
Mutation operators on FSA, keeping the FSA deterministic      307
Mutation operators on FSA, modifying the set of states      305—309
Mutation operators on FSA, modifying the set of transitions      309—313
Mutation operators on FSA, modifying transitions      313—320
Mutation operators on FSA, variable step sizes      338 341
Mutation radius, increase, effects in string evolution      25—28
Mutation radius, small vs. large      11—12 18—19
Mutation radius, string evolution example      11—12 17 18—19 25—28
Mutation rate, increase, effects in string evolution      28—32
Mutation rate, small vs. large      18—19
Mutation rate, string evolution example      11 16—17 18—19 28—32
Mutation step sizes in EP      338 341
Mutation, as principle of evolution      2 6
Mutation, breeding artificial flowers      504—509
Mutation, dispersion provided by      177
Mutation, effect on population structure      177—178
Mutation, fitness inheritance and      58
Mutation, in cellular automata      446—449
Mutation, increase, effects in string evolution      25—32
Mutation, mutation operators on FSA      303—320
Mutation, operation for strings      10—12
Mutation, point mutation on GA chromosomes      81 105—121
Mutation, silent      1 17
Mutation, small changes vs. large changes      11—12 18—19 222—224
Mutative step size adaptation (MSA) in ES      225—230
Mutative step size adaptation (MSA) in ES, $\verb"Mutation"$ function for      226
Mutative step size adaptation (MSA) in ES, as second-order evolution      226 229
Mutative step size adaptation (MSA) in ES, defined      225
Mutative step size adaptation (MSA) in ES, heuristics for step size adaptations      226—230
Mutative step size adaptation (MSA) in ES, meta-evolution and      225 258—260
Nature, evolution as nature’s programming method      283
Nature, evolution strategies and      211—212 571
Nature, morphogenesis in      439
Nature, paradigms in      1
Nature, redundant triplet coding for proteins in      90
Neighborhoods for cellular automata      440—441
Neural networks, as adaptive systems      61 399
Neural networks, as AI bottom-up approach      297
Neural networks, described      1
Niklas, Karl      525
Nonhomologous GA chromosomes      158—161
Normalized representation of GA chromosomes      87
Normally distributed random numbers in ES mutation      219—222
Notation for GP evolution scheme      373
Notation for GP evolution scheme, for EP evolution scheme      325
Notation for GP evolution scheme, for ES evolution scheme      261
Notation for GP evolution scheme, for GA evolution scheme      171
Number decoding for strings      13—14
Number encoding for strings      8—9 13
Numerical Optimization of Computer Models      279
Objective function, evaluation function example      176
Objective function, multimodal      67—68 69—70 186 188
Objective of evolution      42

Ochoa, Gabriela      487
Ontogeny      3—4
Operators, adaptive system      61
Operators, genetic      see genetic operators
Optimization, constrained      69
Optimization, ES mutation role in      216—218
Optimization, evolutionary algorithms for      57—77
Optimization, evolutionary parameter optimization scheme      71—75
Optimization, GA example      67—76
Optimization, minimization vs. maximization      69
Optimization, of ES multimodal functions      266
Optimization, on multimodal functions      64—67
Optimization, through adaptive structures      60—63
Order of a schema      194
Origin of Species, The      51
Owens, A. J.      288 298 299 344
Papert, Simon      458
Parallel Problem Solving from Nature (PPSN)      77 280
Parallel rewriting      449
Pattern of Evolution, The      52
Peano curve      475
Permutation operator, $\verb"AntTracker"$       408 421 422
Permutation operator, ArtFlowers      498
Permutation operator, GP      431
Permutation operator, L-system      477
Perpetual novelty      79 81 105 121—122
Phenotype evolution      481—482
Phenotypical selection, feedback loop with genotypical variation      59—60
Phenotypical selection, overview      58—60
Phenotypical structures in GA      80 81
Phylogeny      4
Phytomimesis      33
Pitch turtle move operations      461
Plant ecosystem evolution      519—525 (see also artificial plant evolution)
Plant evolution, artificial      see artificial plant evolution
Plants to Ecosystems      526
Plus strategy, for EP evolution      325—326 331
Plus strategy, for ES evolution      254 261 277
Plus strategy, for GA evolution      168 171 190 192
Plus strategy, for GP evolution      373 374 376
Point mutation on GA chromosomes      105—121
Point mutation on GA chromosomes, diploid chromosomes      110—112
Point mutation on GA chromosomes, haploid chromosomes      106—108
Point mutation on GA chromosomes, homologous alleles      110
Point mutation on GA chromosomes, minor role of      81
Point mutation on GA chromosomes, new alleles introduced by      105
Point mutation on GA chromosomes, point mutation operator      106
Point mutation on GA chromosomes, polyploid chromosomes      108—110
Point mutation on GA chromosomes, probability      108
Point mutation on GA chromosomes, recombination vs. mutation      176—180
Point mutation on GA chromosomes, RNA chromosomes      112—117
Point mutation on GA chromosomes, schema theorem and mutation      205
Point mutation on GA chromosomes, visualization using facial expressions      117—121 122
Point mutation operator, GA      106
Point mutation operator, GP      431
Polyploid GA chromosomes      83—105
Polyploid GA chromosomes, defined      96—97 108
Polyploid GA chromosomes, diploidy and dominance on GA chromosomes      95—105
Polyploid GA chromosomes, form of      137
Polyploid GA chromosomes, general structure      96—97
Polyploid GA chromosomes, generating and interpreting      98—99
Polyploid GA chromosomes, haploid GA chromosomes      83—95
Polyploid GA chromosomes, point mutation on      108—110
Polyploid GA chromosomes, recombination      133 137—143
Polyploid GA chromosomes, visualization      98—99
Populations, adaptation of individuals vs      4—5
Populations, generating schema instances for      197
Populations, genotype and phenotype spaces      58—60
Populations, reproductive plan      64 65
Populations, subpopulations as meta-individuals      258
PPSN (Parallel Problem Solving from Nature)      77 280
Predecessor in L-systems      451
Prediction quality of automata, evaluating      328—331
Predictor FSA      302 303
Prisoner’s dilemma strategies      292
Probabilities, differential survival      162
Probabilities, for butterfly selection      36
Probabilities, operator weights adaptation      427—430
Probabilities, point mutation probability      108
Processes, adaptations as      5
Productions of L-systems      450 452 456 473
Program induction with genetic algorithms      291
Programming by evolution      283—295 (see also evolutionary programming; genetic programming)
Programming by evolution, assembler vs. higher programming languages      287
Programming by evolution, bibliographical notes      294—295
Programming by evolution, classifier systems      289—290
Programming by evolution, evaluation of program genomes      285
Programming by evolution, evolution of finite automata      288—289
Programming by evolution, evolving vs. programming      284—286
Programming by evolution, genetic programming on symbolic programming by evolution, expressions      292—294
Programming by evolution, genetic programming with linear genomes      291—292
Programming by evolution, hierarchically structured programs      285
Programming by evolution, induction of programs by evolutionary algorithms      285
Programming by evolution, mutated assembler programs      287—288
Programming by evolution, problems of      283—284 286
Programming by evolution, program induction with genetic algorithms      291
Programming by evolution, program representation problem      286
Programming by evolution, recombination and      288
Programming by evolution, TIERRA system      294
Prusinkiewicz, Przemyslaw      458 471 486 490 524 525
Pseudo code for term-structured GP      349
Quadratic Koch island      475 476
Quinton, R. E.      458
Rahmat — Samii, Y.      208
Random string generation      16
Random survival      162
Rank-based selection      164—166
Rank-based selection, $\verb"selectRankBased"$ function      165—166
Rank-based selection, problem of superindividuals      164—165
Ray, Thomas      294
Realistic Modeling and Rendering of Plant Ecosystems      526
Rearrangement of relative gene positions      152—153
Recessivity and dominance      99—100
Rechenberg, Ingo      211 212 226—227 228 229 252 279 342
Recombination      (see also evolution strategies recombination; genetic algorithms recombination; genetic programming recombination)
Recombination mask      126
Recombination of GA chromosomes      133—143
Recombination of GA chromosomes, gene ordering      133
Recombination of GA chromosomes, haploid chromosomes      135—137
Recombination of GA chromosomes, meiotic recombination of diploid chromosomes      143—146
Recombination of GA chromosomes, polyploid chromosomes      133 137—143
Recombination operator, $\verb"AntTracker"$       408 420 421
Recombination operator, ArtFlowers      498
Recombination operator, L-system      477
Recombination, $\verb"AntTracker"$ operator      408 420 421
Recombination, as GA principle      80—81
Recombination, breeding artificial flowers      510—513
Recombination, EP and      288 342
Recombination, GA      121—148
Recombination, GP      358—367 390
Recombination, L-system operator      477
Recombination, model of evolution and      58
Recombination, mutation vs      176—180
Recursive construction of GP terms      350—351
Reduction division simulation      143—146
Reproduction      (see also evolution strategies selection and reproduction schemes)
Reproduction, as GA principle      80—81
Reproduction, EP reproduction scheme      288
Reproduction, ES selection and reproduction schemes      252—256
Reproduction, GA reproduction scheme      167—173
Reproduction, GP reproduction scheme      371—379
Reproduction, in evolutionary parameter optimization scheme      73
Reproductive plan      64 65
Ribosomes, translation of codons to amino acids by      88
Ridley, Mark      3 52 57
RNA chromosomes      88—93
RNA chromosomes, compact output form      94
RNA chromosomes, extraction of alleles      116
RNA chromosomes, point mutation on      112—117
RNA chromosomes, ribosome program      92—93

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