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.

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

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Предметный указатель

Genetic programming mutation, expansion mutation operator      432
Genetic programming mutation, for balanced mobiles      390
Genetic programming mutation, GP chromosomes      370—371
Genetic programming mutation, GP terms      367—371
Genetic programming mutation, hoist operator      431
Genetic programming mutation, permutation operator      431
Genetic programming mutation, point mutation operator      431
Genetic programming mutation, subtree mutation operator      432
Genetic programming mutation, tree structures      369—370
Genetic programming on symbolic expressions      292—294 347—358
Genetic programming on symbolic expressions, arithmetic expression examples      356—358
Genetic programming on symbolic expressions, building block example      350
Genetic programming on symbolic expressions, closure property of GP terms      356—357
Genetic programming on symbolic expressions, completeness of GP building blocks      357
Genetic programming on symbolic expressions, defining building blocks through patterns      354—355
Genetic programming on symbolic expressions, generating terms      350—356
Genetic programming on symbolic expressions, generating terms with variable numbers of arguments      355—356
Genetic programming on symbolic expressions, GP terms      349
Genetic programming on symbolic expressions, graphing terms as tree structures      353—354 355
Genetic programming on symbolic expressions, linear vs. hierarchical program structures      292—293
Genetic programming on symbolic expressions, patterns for function symbols      35 1
Genetic programming on symbolic expressions, problem-specific building blocks      348 357
Genetic programming on symbolic expressions, recursive construction of GP terms      350—351
Genetic programming on symbolic expressions, requirements for program building blocks      356—358
Genetic programming on symbolic expressions, symbolic expressions as trees      365—366
Genetic programming on symbolic expressions, term-structured GP expressions      348—350
Genetic programming on symbolic expressions, terms as tree structures      347—348
Genetic programming on symbolic expressions, terms vs. symbolic expressions      349—350 365
Genetic programming on symbolic expressions, typed GP      366—367
Genetic programming on term structures      see genetic programming on symbolic expressions
Genetic Programming Problem Solver      396
Genetic programming recombination      358—367
Genetic programming recombination, $\verb"AntTracker"$ example      408 420 421
Genetic programming recombination, crossover of identical genomes      511 520
Genetic programming recombination, for balanced mobiles      390
Genetic programming recombination, GP chromosome recombination      363—365
Genetic programming recombination, GP crossover problems      365—367
Genetic programming recombination, GP term recombination      363
Genetic programming recombination, performance measure for recombination operators      429—430
Genetic programming recombination, recombination at root position      361—362
Genetic programming recombination, recombination of two terms      361 362
Genetic programming recombination, recombination on list of terms      363 364
Genetic programming recombination, term crossover      358—359 365—367
Genetic programming recombination, term recombination in Evolvica      359—365
Genetic programming with linear genomes      291—292
Genetic programming, $\verb"AntTracker"$ example      399—427
Genetic programming, adaptive operator weights      427—430
Genetic programming, adequate representation of computer programs      345
Genetic programming, advanced operators and functionality      430—434
Genetic programming, advanced, at work      399—435
Genetic programming, architecture-altering operations      432 434
Genetic programming, automatically defined functions      432—433
Genetic programming, automatically defined iterations and loops      434
Genetic programming, bibliographical notes      392 396—397 435
Genetic programming, binary (BGP)      291
Genetic programming, comma strategy      373 374 376 390
Genetic programming, competition among genetic operators      427—428
Genetic programming, compiling GP (CGP)      292
Genetic programming, defined      293
Genetic programming, ES vs      358—359 373
Genetic programming, evolution scheme      371—377
Genetic programming, evolutionary algorithms and      344
Genetic programming, fractal evolution      475—476
Genetic programming, GA and      327 345—346 358—359 367 373
Genetic programming, in action      377—392
Genetic programming, modularization of programs      432
Genetic programming, multioperator      426
Genetic programming, mutation of program structures      367—371
Genetic programming, on symbolic expressions (term structures)      291 292—294 347—358
Genetic programming, operator selection      428
Genetic programming, performance measure for recombination operators      429—430
Genetic programming, performance measure for unary operators      429
Genetic programming, plus strategy      373 374 376
Genetic programming, recombination of program structures      358—367
Genetic programming, term crossover      358—359 365—367
Genetic programming, terms vs. symbolic expressions      349—350 365
Genetic programming, tree-structured chromosomes in      346—347
Genetic programming, typed      366—367
Genetic programming, with linear genomes      291—292
Genetic Programming: An Introduction      396 435
Genetic Programming: On the Programming of Computers by Means of Natural Selection      293 392
Genomes, biomorph structure      45
Genomes, control programs in      283
Genomes, evaluation of program genomes      285
Genomes, evolution of genome expressions      504 506—509
Genotypes, as adaptive components      60
Genotypes, binary string representation of      70
Genotypes, fractal structure evolution      481 482
Genotypes, one-way road of      59—60
Genotypes, visualization in GA      174 175
Genotypical structures in GA      80 81
Genotypical variation, feedback loop with phenotypical selection      59—60
Genotypical variation, overview      58—60
Gerharz, Rudi      489
Global ES recombination      237—243
Global ES recombination, discrete or intermediate      238—239
Global ES recombination, local vs      234
Global ES recombination, multiple global and local      250—252
Global ES recombination, multirecombination      238 249—250
Global ES recombination, recombination diagram      241—243
Global ES recombination, selecting elements to recombine      240—241
Global ES recombination, three-stage scheme of multiple recombination      240
Global maximum      68
GLP (genetic L-system programming)      490 517 518
Goldberg, David E.      82 207
Goodwin, Brian      51
GP      see genetic programming
GP chromosomes, recombination      364—365 (see also genetic programming recombination)
GP chromosomes, structure of      363
GP chromosomes, tree-structured      346—347 (see also genetic programming on symbolic expressions)
GP conference      397
Gradient-following strategy      66 67
Growth programs in ontogeny      4
Hamilton, P.      486
Hamilton, William      347
Hamming distances, described      8
Hamming distances, string evolution examples      8 9 10 20 25 27 28 32
Hanan, Jim      458 486
Handbook of Evolutionary Computation      75
Handbook of Genetic Algorithms, The      208
Haploid GA chromosomes      83—95
Haploid GA chromosomes, binary chromosomes      84—88 106—108
Haploid GA chromosomes, chromosomes with real-value alleles      93—95
Haploid GA chromosomes, defined      96 106
Haploid GA chromosomes, deletion      153—154
Haploid GA chromosomes, indexed      84 150
Haploid GA chromosomes, mutation on binary chromosomes      106—108
Haploid GA chromosomes, recombination      135—137 138—139
Haploid GA chromosomes, RNA chromosomes      88—93
Heterogeneity of traits      57—58
Heuristic programming      297
Hicklin, J. E      292
Hidden Order: How Adaptation Builds Complexity      208
Hilbert curve demo for turtle      463 465
Hoffmeister, F.      229
Hoist operator (GP)      431
Holland, John H.      79 81 123 190 208 283 286 289
How Nature Works      51
How the Leopard Changed Its Spots      51
ICGA (International Conference on Genetic Algorithms)      208—209
IL-systems      456—458 472 474
Index-dependent dominance      104—105
Indexed haploid GA chromosomes      84 150
Individuals, adaptation of populations vs      4—5
Individuals, dualism of      60
Individuals, expression of      59
Individuals, subpopulations as meta-individuals      258
Individuals, superindividuals      164—165
Industrialization and mimesis      33—34
Inheritance, evolution and      3

Inheritance, fitness inheritance      58
Inheritance, recombination and      58
Instances of a schema, $\verb"instances"$ function      196—197
Instances of a schema, defined      193
Instances of a schema, enumeration of      179 199—202
Instances of a schema, extraction of      197
Instances of a schema, generating for a population      197
Instances of a schema, generating over an alphabet      196—197
Intelligence Through Simulated Evolution      344
Intelligent agents as predictors about environment      299
Interactive evolution of biomorphs      47—51
Interactive evolution, design by      42 45
Intermediate recombination      232—234
Intermediate recombination, global      238—239
Intermediate recombination, local      235
Intermediate recombination, local multirecombination      247—249
Intermediate recombination, multirecombination on parameter lists      233—234
International Conference on Genetic Algorithms (ICGA)      208—209
Interpretation, $\verb"AntTracker"$ example      401—404 406
Interpretation, GA chromosome interpretation      97—99 101—105
Interpretation, in evolutionary parameter optimization scheme      72—73
Interpretation, turtle interpretation      458 464 519
Introduction to Artificial Life      469
Introduction to Genetic Algorithms, An      207
Introns      155 402—403
Inversion GA operator      150—153
Inversion GA operator, $\verb"Inversion"$ function      151 152
Inversion GA operator, effect of      183 184
Inversion GA operator, generalized inversion      151—152
Inversion GA operator, inversion effects on natural chromosomes      150
Inversion GA operator, schema theorem and      206
Iterated rewriting in L-systems      452 453
Iterations, automatically defined in GP      434
Jacob, C.      524 526
Jefferson, D.      399 400
Jump turtle move operations      460 567
K $\acute{o}$ kai, G.      487
Koch curve      475
Koza, John      293 345 347 392 396 486
L-Systems      see Lindenmayer systems
Lamarck, Jean Baptiste de      4
Langdon, William      396
Langton, Christopher      399 444 449 470
Latham, William      53
Levy, Steven      435 469
Lindenmayer systems      449—468 471 487
Lindenmayer systems, commands      466
Lindenmayer systems, commands      465 466
Lindenmayer systems, commands      465 466
Lindenmayer systems, Anabaena catenula growth simulation      451—453
Lindenmayer systems, animation sequence for visualization      464—465
Lindenmayer systems, bibliographical notes      486—487 524—526
Lindenmayer systems, Bit strings encoding      472 473
Lindenmayer systems, bracketed system for tree structure      466—467 468
Lindenmayer systems, cell layer growth      451 453 456
Lindenmayer systems, cellular automata vs      455—456
Lindenmayer systems, changing turtle orientation      460 462
Lindenmayer systems, changing turtle position      459—460
Lindenmayer systems, constraints for building blocks      474
Lindenmayer systems, context-free      450—456
Lindenmayer systems, context-sensitive      456 458 473
Lindenmayer systems, derivability      450—451
Lindenmayer systems, deterministic (D0L)      450 455
Lindenmayer systems, drawing graphic elements      462
Lindenmayer systems, encoding IL-systems      472 474
Lindenmayer systems, evolutionary algorithms for inference      472
Lindenmayer systems, evolutionary influence of      471—487
Lindenmayer systems, for artificial plant evolution      489—490
Lindenmayer systems, fractal structure evolution      474—486
Lindenmayer Systems, Fractals, and Plants      486
Lindenmayer systems, functions required for inference      472
Lindenmayer systems, genetic L-system programming (GLP)      490 517 518
Lindenmayer systems, genetic operators      477
Lindenmayer systems, Hilbert curve demo      463 465
Lindenmayer systems, IL-systems      456—458 472—474
Lindenmayer systems, implicit description of spatial structures      458—465
Lindenmayer systems, inference problem      471—472
Lindenmayer systems, iterated rewriting      452 453
Lindenmayer systems, language of DOL-systems      450
Lindenmayer systems, modeling of branching structures      465—467 468
Lindenmayer systems, of flowering plant      490 491
Lindenmayer systems, one L-systems      456
Lindenmayer systems, parallel rewriting of strings      449
Lindenmayer systems, parametrized      453 455
Lindenmayer systems, parametrized tree system      467
Lindenmayer systems, position and orientation of a turtle      458 459
Lindenmayer systems, predecessor      451
Lindenmayer systems, productions      450 452 456 473
Lindenmayer systems, repair and translation      473
Lindenmayer systems, rules      450 451 452 454 458 473
Lindenmayer systems, signal interactions      457
Lindenmayer systems, stochastic L-systems      474
Lindenmayer systems, substitution principle of      439
Lindenmayer systems, successor      451
Lindenmayer systems, table L-systems      474
Lindenmayer systems, templates for encoding      474
Lindenmayer systems, tL-systems      456—457
Lindenmayer systems, zL-systems      450—456
Lindenmayer Systems: Structure, Languages, and Growth Functions      486
Lindenmayer, Aristid      439 449 471 486 490 524
Linear genomes, genetic programming with      291—292
Lintermann, Bernd      526
LISP programs, genetic operators for      292
LISP programs, Mathematica and      293
Liu, Baoding      208
Local ES recombination      235—237
Local ES recombination, binary      245—247
Local ES recombination, discrete or intermediate      235
Local ES recombination, global vs      234
Local ES recombination, multiple global and local      250—252
Local ES recombination, multirecombination      235 247—249
Local maxima      66 67 68—69
Loops, automatically defined in GP      434
Loops, between genotypical and phenotypical levels      59—60
Loops, data paths      443—444
Loops, duplication of signals at T crossings      443 444
Loops, evolution loop      21
Loops, implicit for $\verb"AntTracker"$ sensors      403
Loops, in cellular automata      444 449
Loops, instructions      446
Loops, life-cycle of loops      446
Loops, loops      444 449
Loops, mutated loops      446 447—449
Loops, storage elements, loops as      444
Loops, transcription phase      446
Loops, translation phase      446
Lychnis coronaria      see artificial plant evolution; breeding artificial flowers
Macro turtle commands      490—491
Masked GA recombination      128—131
Masked GA recombination, by example      129
Masked GA recombination, generation of masks      129—130
Masked GA recombination, multipoint crossover      130—131
Masked GA recombination, one-point crossover      130
Masked GA recombination, using faces      148 149
Mathematica, $\verb"Automata"$ package      300
Mathematical Models for Cellular Interaction in Development      486
Maximization vs. minimization optimization      69
Mayr, E.      1
Mealy machines      see finite state automata (FSA)
Mech, Radomir      525
Meiosis, cell division phase      143 144
Meiosis, diploidy and      95—96
Meiosis, genetic algorithms and      80—81
Meiosis, recombination of diploid GA chromosomes      143—146
Meiotic recombination of diploid GA chromosomes      143—146
Meiotic recombination of diploid GA chromosomes, generation of two gametes      143
Meiotic recombination of diploid GA chromosomes, meiotic recombination and division      145—146
Meiotic recombination of diploid GA chromosomes, merging      145
Meiotic recombination of diploid GA chromosomes, recombination mimicking meiosis      145
Melanistic moths      34

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