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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
Meta-evolution strategies, climbing meta-mountains      260
Meta-evolution strategies, island model and      258Ч260
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      258Ч260
Meta-evolution strategies, with three subpopulations      271Ч272 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      33Ч34
Mimesis, butterfly mimesis simulation      34Ч42
Mimesis, color adaptations      33
Mimesis, defined      33
Mimesis, directional selection for      34
Mimesis, industrialization and      33Ч34
Mimesis, melanistic moths      34
Mimesis, phytomimesis      33
Minimization vs. maximization optimization      69
Mitchell, Melanie      207
Mobiles (GP), $\verb"mobileExprForm"$ function      386Ч387
Mobiles (GP), $\verb"mobileFitness"$ function      384Ч385 388
Mobiles (GP), $\verb"mobileFunctions"$ function      385Ч386
Mobiles (GP), $\verb"mobileTermForm"$ function      387
Mobiles (GP), $\verb"mobileTerminals"$ function      385 386
Mobiles (GP), $\verb"mobileWeight"$ function      383
Mobiles (GP), encoding      377Ч378
Mobiles (GP), evaluation by balance      382Ч385 388Ч389 390
Mobiles (GP), evolution of balanced mobiles      388Ч392 393Ч395
Mobiles (GP), generating structures      385Ч388
Mobiles (GP), graphical representation of      378Ч382
Mobiles (GP), mobile bars      377Ч378
Mobiles (GP), well-balanced      377
Mock, Kenrick      526
Model of evolution      57Ч60
Model of evolution, differential fitness      58
Model of evolution, fitness inheritance      58
Model of evolution, genotypical variation      58Ч60
Model of evolution, phenotypical selection      58Ч60
Model of evolution, variability      57Ч58
Modeling adaptive systems, adaptation steps      62Ч63
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      459Ч462
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      68Ч69
Multimodal functions, multimodality and function theory      266
Multimodal functions, noisy objective function      67 68
Multimodal functions, objective function      67Ч68 69Ч70 186 188
Multimodal functions, optimization on      64Ч67
Multimodal functions, triple sinc (ES test function)      266Ч268
Multimodal search spaces, defined      64
Multimodal search spaces, example      64 66
Multimodal search spaces, gradient-following strategies in      66Ч67
Multioperator GP      426
Multirecombination of ES chromosomes, discrete      233Ч235
Multirecombination of ES chromosomes, global      249Ч250
Multirecombination of ES chromosomes, in Mathematica      243Ч245
Multirecombination of ES chromosomes, intermediate      233Ч235
Multirecombination of ES chromosomes, local      247Ч249
Multirecombination of ES chromosomes, on parameter lists      233Ч234
Multirecombination of GA chromosomes      131Ч133
Multirecombination of GA chromosomes, binary multirecombination      132
Multirecombination of GA chromosomes, discrete componentwise      126
Multirecombination of GA chromosomes, recombination mask      126
Mutated assembler programs      287Ч288
Mutated assembler programs, automatic programming vs. random search      287Ч288
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      303Ч320
Mutation operators on FSA      303Ч320
Mutation operators on FSA, adding a state      306Ч307 308
Mutation operators on FSA, adding a transition      309Ч311
Mutation operators on FSA, changing a transitionТs input symbol      313Ч314 315
Mutation operators on FSA, changing a transitionТs output symbol      314Ч316
Mutation operators on FSA, changing a transitionТs source      316Ч318
Mutation operators on FSA, changing a transitionТs target      318Ч320
Mutation operators on FSA, changing the initial state      304Ч305
Mutation operators on FSA, deleting a state      307Ч309
Mutation operators on FSA, deleting a transition      311Ч313
Mutation operators on FSA, fitness-dependent mutation      341Ч342
Mutation operators on FSA, GA mutation operators vs      303Ч304
Mutation operators on FSA, keeping the FSA deterministic      307
Mutation operators on FSA, modifying the set of states      305Ч309
Mutation operators on FSA, modifying the set of transitions      309Ч313
Mutation operators on FSA, modifying transitions      313Ч320
Mutation operators on FSA, variable step sizes      338 341
Mutation radius, increase, effects in string evolution      25Ч28
Mutation radius, small vs. large      11Ч12 18Ч19
Mutation radius, string evolution example      11Ч12 17 18Ч19 25Ч28
Mutation rate, increase, effects in string evolution      28Ч32
Mutation rate, small vs. large      18Ч19
Mutation rate, string evolution example      11 16Ч17 18Ч19 28Ч32
Mutation step sizes in EP      338 341
Mutation, as principle of evolution      2 6
Mutation, breeding artificial flowers      504Ч509
Mutation, dispersion provided by      177
Mutation, effect on population structure      177Ч178
Mutation, fitness inheritance and      58
Mutation, in cellular automata      446Ч449
Mutation, increase, effects in string evolution      25Ч32
Mutation, mutation operators on FSA      303Ч320
Mutation, operation for strings      10Ч12
Mutation, point mutation on GA chromosomes      81 105Ч121
Mutation, silent      1 17
Mutation, small changes vs. large changes      11Ч12 18Ч19 222Ч224
Mutative step size adaptation (MSA) in ES      225Ч230
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      226Ч230
Mutative step size adaptation (MSA) in ES, meta-evolution and      225 258Ч260
Nature, evolution as natureТs programming method      283
Nature, evolution strategies and      211Ч212 571
Nature, morphogenesis in      439
Nature, paradigms in      1
Nature, redundant triplet coding for proteins in      90
Neighborhoods for cellular automata      440Ч441
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      158Ч161
Normalized representation of GA chromosomes      87
Normally distributed random numbers in ES mutation      219Ч222
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      13Ч14
Number encoding for strings      8Ч9 13
Numerical Optimization of Computer Models      279
Objective function, evaluation function example      176
Objective function, multimodal      67Ч68 69Ч70 186 188
Objective of evolution      42
Ochoa, Gabriela      487
Ontogeny      3Ч4
Operators, adaptive system      61
Operators, genetic      see genetic operators
Optimization, constrained      69
Optimization, ES mutation role in      216Ч218
Optimization, evolutionary algorithms for      57Ч77
Optimization, evolutionary parameter optimization scheme      71Ч75
Optimization, GA example      67Ч76
Optimization, minimization vs. maximization      69
Optimization, of ES multimodal functions      266
Optimization, on multimodal functions      64Ч67
Optimization, through adaptive structures      60Ч63
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 121Ч122
Phenotype evolution      481Ч482
Phenotypical selection, feedback loop with genotypical variation      59Ч60
Phenotypical selection, overview      58Ч60
Phenotypical structures in GA      80 81
Phylogeny      4
Phytomimesis      33
Pitch turtle move operations      461
Plant ecosystem evolution      519Ч525 (see also artificial plant evolution)
Plant evolution, artificial      see artificial plant evolution
Plants to Ecosystems      526
Plus strategy, for EP evolution      325Ч326 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      105Ч121
Point mutation on GA chromosomes, diploid chromosomes      110Ч112
Point mutation on GA chromosomes, haploid chromosomes      106Ч108
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      108Ч110
Point mutation on GA chromosomes, probability      108
Point mutation on GA chromosomes, recombination vs. mutation      176Ч180
Point mutation on GA chromosomes, RNA chromosomes      112Ч117
Point mutation on GA chromosomes, schema theorem and mutation      205
Point mutation on GA chromosomes, visualization using facial expressions      117Ч121 122
Point mutation operator, GA      106
Point mutation operator, GP      431
Polyploid GA chromosomes      83Ч105
Polyploid GA chromosomes, defined      96Ч97 108
Polyploid GA chromosomes, diploidy and dominance on GA chromosomes      95Ч105
Polyploid GA chromosomes, form of      137
Polyploid GA chromosomes, general structure      96Ч97
Polyploid GA chromosomes, generating and interpreting      98Ч99
Polyploid GA chromosomes, haploid GA chromosomes      83Ч95
Polyploid GA chromosomes, point mutation on      108Ч110
Polyploid GA chromosomes, recombination      133 137Ч143
Polyploid GA chromosomes, visualization      98Ч99
Populations, adaptation of individuals vs      4Ч5
Populations, generating schema instances for      197
Populations, genotype and phenotype spaces      58Ч60
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      328Ч331
Predictor FSA      302 303
PrisonerТs dilemma strategies      292
Probabilities, differential survival      162
Probabilities, for butterfly selection      36
Probabilities, operator weights adaptation      427Ч430
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      283Ч295 (see also evolutionary programming; genetic programming)
Programming by evolution, assembler vs. higher programming languages      287
Programming by evolution, bibliographical notes      294Ч295
Programming by evolution, classifier systems      289Ч290
Programming by evolution, evaluation of program genomes      285
Programming by evolution, evolution of finite automata      288Ч289
Programming by evolution, evolving vs. programming      284Ч286
Programming by evolution, genetic programming on symbolic programming by evolution, expressions      292Ч294
Programming by evolution, genetic programming with linear genomes      291Ч292
Programming by evolution, hierarchically structured programs      285
Programming by evolution, induction of programs by evolutionary algorithms      285
Programming by evolution, mutated assembler programs      287Ч288
Programming by evolution, problems of      283Ч284 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 Ч Samii, Y.      208
Random string generation      16
Random survival      162
Rank-based selection      164Ч166
Rank-based selection, $\verb"selectRankBased"$ function      165Ч166
Rank-based selection, problem of superindividuals      164Ч165
Ray, Thomas      294
Realistic Modeling and Rendering of Plant Ecosystems      526
Rearrangement of relative gene positions      152Ч153
Recessivity and dominance      99Ч100
Rechenberg, Ingo      211 212 226Ч227 228 229 252 279 342
Recombination      (see also evolution strategies recombination; genetic algorithms recombination; genetic programming recombination)
Recombination mask      126
Recombination of GA chromosomes      133Ч143
Recombination of GA chromosomes, gene ordering      133
Recombination of GA chromosomes, haploid chromosomes      135Ч137
Recombination of GA chromosomes, meiotic recombination of diploid chromosomes      143Ч146
Recombination of GA chromosomes, polyploid chromosomes      133 137Ч143
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      80Ч81
Recombination, breeding artificial flowers      510Ч513
Recombination, EP and      288 342
Recombination, GA      121Ч148
Recombination, GP      358Ч367 390
Recombination, L-system operator      477
Recombination, model of evolution and      58
Recombination, mutation vs      176Ч180
Recursive construction of GP terms      350Ч351
Reduction division simulation      143Ч146
Reproduction      (see also evolution strategies selection and reproduction schemes)
Reproduction, as GA principle      80Ч81
Reproduction, EP reproduction scheme      288
Reproduction, ES selection and reproduction schemes      252Ч256
Reproduction, GA reproduction scheme      167Ч173
Reproduction, GP reproduction scheme      371Ч379
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      88Ч93
RNA chromosomes, compact output form      94
RNA chromosomes, extraction of alleles      116
RNA chromosomes, point mutation on      112Ч117
RNA chromosomes, ribosome program      92Ч93
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