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

Differentiation in cells      439
Diploid GA chromosomes, defined      96
Diploid GA chromosomes, general structure      96
Diploid GA chromosomes, generating and interpreting      97—98 101—105
Diploid GA chromosomes, meiotic recombination      143—146
Diploid GA chromosomes, point mutation on      110—112
Diploid GA chromosomes, visualization      97—98 104 105 112
Diploid GA chromosomes, with dominant alleles      103—104
Diploidy and dominance on GA chromosomes      95—105
Diploidy and dominance on GA chromosomes, chromosome interpretation      101—105
Diploidy and dominance on GA chromosomes, extraction of dominant alleles      101—105
Diploidy and dominance on GA chromosomes, general structure of di- and -ploid chromosomes      96—97
Diploidy and dominance on GA chromosomes, generating and interpreting di- and -ploid chromosomes      97—99
Diploidy and dominance on GA chromosomes, simulation of dominant and recessive alleles      99—101
Diploidy, defined      95
Diploidy, meiosis and      95—96
Diploidy, of higher animals and plants      95—96
Directional selection for mimesis      34
Discovery of Evolution, The      52
Discrete encoding as GA principle      80
Discrete ES recombination      232—234
Discrete ES recombination, global      238—239
Discrete ES recombination, local      235
Discrete ES recombination, local multirecombination      235 247—249
Discrete ES recombination, multirecombination on parameter lists      233—234
Discrete GA recombination      123—124
Dispersion provided by mutations      177
Diversification of EP      338—344
Diversification of EP, application domains      343
Diversification of EP, combinatorial and parameter optimization      342
Diversification of EP, extensions of FSA evolution      338 341—342
Diversification of EP, fitness-dependent mutation      341—342
Diversification of EP, majority logic recombination      342
Diversification of EP, recent diversification and evolution      342—344
Diversification of EP, recombination and      342
Diversification of EP, strong causality      342—343
Diversification of EP, variable mutation step sizes      338 341
DOL-systems      450 453
Dominance, defined      99
Dominance, extraction of dominant alleles      100—105
Dominance, GA chromosome interpretation      101—105
Dominance, index-dependent      104—105
Dominance, interpretation of diploid chromosomes and      96
Dominance, recessivity and      99—100
Drawing operations for turtle      462
Dualism, as GA principle      80 81
Dualism, of individuals      60
Duplication GA operator      155—158
Duplication GA operator, chromosome with duplicated subsequence      157
Duplication GA operator, crosswise restitution and      155 156
Duplication GA operator, duplication defined      155
Duplication GA operator, effect of      183 184—185 555
Duplication GA operator, examples of duplication      157
Duplication GA operator, gene expression and      157—158
Duplication GA operator, on GA chromosomes      156—158
Duplication operator, $\verb"AntTracker"$       408 420 421
Duplication operator, ArtFlowers      498
Duplication operator, GA      155—158
Duplication operator, GP      431
Eldredge, Niles      52
Electromagnetic Optimization by Genetic Algorithms      208
Elementary building blocks as GA principle      80 81—82
Elitist selection      166—167
Elitist selection, $\verb"selectElite"$ function      166—167
Elitist selection, GA evolution scheme with      169
Embryonic development (ontogeny)      3—4
Emergence Order: From Chaos to Order      208
Encapsulation operator      408 420—421
Encoding, binary strings for genotypes      70 82
Encoding, binary vs. real numbers      84—85
Encoding, discrete, as GA principle      80
Encoding, evaluation function for binary-encoded numbers      176
Encoding, IL-systems      472—474
Encoding, number encoding for strings      8—9 13
Encoding, redundant, in nature      90
Encoding, triplet encoding of amino acids      88—90
Enumeration of schema instances      179 199—202
Environment, $\verb"AntTracker"$ response to environmental signals      402
Environment, $\verb"AntTracker"$ two-dimensional environment      400 401
Environment, as adaptive system component      61
Environment, constraints in      60
Environment, deriving models of      298—299
Environment, GA evolution under variable conditions      185—190 191—192
Environment, intelligent agents as predictors      299
Environment, interaction as adaptation step      62
Environment, predicting with FSAs      301—303
EP      see evolutionary programming
Erf function      220—221
Error function in ES mutation      220—221
ES      see evolution strategies
ES chromosomes      (see also evolution strategies)
ES chromosomes, binary recombination      231
ES chromosomes, defined      213
ES chromosomes, discrete recombination      232—234
ES chromosomes, generating      214 215
ES chromosomes, global multirecombination      249—250
ES chromosomes, global recombination      234 237—243
ES chromosomes, in Evolvica      213—215
ES chromosomes, intermediate recombination      232—234
ES chromosomes, local binary recombination      245—247
ES chromosomes, local multirecombination      247—249
ES chromosomes, local recombination      234 235—237
ES chromosomes, multiple global and local recombination      250—252
ES chromosomes, multirecombination in Mathematica      243—245
ES chromosomes, mutation of object parameters      218 222—224
ES chromosomes, mutation with step size adaptation      225—230
ES chromosomes, recombination in Mathematica      231—232
ES chromosomes, visualizing      214—215
ES chromosomes, with object and strategy parameters      213
Evaluation function, as adaptation step      62
Evaluation function, as adaptive system component      61
Evaluation function, ES evolution      265
Evaluation, $\verb"AntTracker"$ example      405—406
Evaluation, breeding artificial flowers      499—500
Evaluation, for best selection in GP      373
Evaluation, in evolutionary parameter optimization scheme      72—73
Evaluation, L-system      477—479 514
Evaluation, of EP automaton prediction quality      328—331
Evaluation, of genotypes in GA      174—176
Evaluation, of GP mobiles by balance      382—385 388—389 390
Evaluation, of program genomes      285
Evolution      52
Evolution and Optimum Seeking      279
Evolution of Complexity, The      51
Evolution of fractal structures      see fractal structure evolution
Evolution of Parallel Cellular Machines      469
Evolution of plant ecosystems      519—525
Evolution strategies      21 1—280
Evolution strategies at work      266—279
Evolution strategies at work, ascent of all peaks      272—279
Evolution strategies at work, climbing example with three populations      268—270
Evolution strategies at work, meta-evolution of three subpopulations      271—272 273
Evolution strategies at work, optimization of multimodal functions      266
Evolution strategies at work, sinc function      267
Evolution strategies at work, triple sine (multimodal test function)      266—268
Evolution strategies mutation      216—231
Evolution strategies mutation, ascent by mutation example      216—218
Evolution strategies mutation, density function      219—220
Evolution strategies mutation, error function      220—221
Evolution strategies mutation, further information      231
Evolution strategies mutation, generation function for normally distributed random numbers      221—222
Evolution strategies mutation, heuristics for step size adaptations      226—230
Evolution strategies mutation, meta-evolution      225 258—260
Evolution strategies mutation, mutated vector of object parameters      218
Evolution strategies mutation, mutation operator defined      218
Evolution strategies mutation, mutative step size adaptation (MSA)      225
Evolution strategies mutation, normally distributed random numbers      219—222
Evolution strategies mutation, of object parameters      218—224
Evolution strategies mutation, role in optimization      216—218
Evolution strategies mutation, second-order evolution      226 229

Evolution strategies mutation, small vs. large variations      222—224
Evolution strategies mutation, with step size adaptation      218 225—230
Evolution strategies recombination      231—252
Evolution strategies recombination, binary      231
Evolution strategies recombination, discrete      232—234
Evolution strategies recombination, examples      245—252
Evolution strategies recombination, global      234 237—243
Evolution strategies recombination, global multirecombination      238 249—250
Evolution strategies recombination, in Mathematica      231—232
Evolution strategies recombination, intermediate      232—234
Evolution strategies recombination, local      234 235—237
Evolution strategies recombination, local binary      245—247
Evolution strategies recombination, local multirecombination      235 247—249
Evolution strategies recombination, local vs. global      234
Evolution strategies recombination, multiple global and local      250—252
Evolution strategies recombination, multirecombination in Mathematica      243—245
Evolution strategies recombination, multirecombination on parameter lists      233—234
Evolution strategies selection and reproduction schemes      252—256
Evolution strategies selection and reproduction schemes, ( $\mu$ + $\lambda$ ) and ( $\mu$ , $\lambda$ ) evolution strategies      255—256
Evolution strategies selection and reproduction schemes, (1 + $\lambda$ ) and (1, $\lambda$ ) evolution strategies      252—255
Evolution strategies selection and reproduction schemes, (1 + 1) evolution strategies      253—254
Evolution strategies selection and reproduction schemes, ascent by mutation example      216—218
Evolution strategies selection and reproduction schemes, comma strategy      254—255
Evolution strategies selection and reproduction schemes, ES notation extensions      257—258
Evolution strategies selection and reproduction schemes, evolution strategies with recombination      256
Evolution strategies selection and reproduction schemes, graphical notation of basic elements for description      253
Evolution strategies selection and reproduction schemes, plus strategy      254
Evolution strategies, ( $\mu$ + $\lambda$ ) and ( $\mu$ , $\lambda$ ) evolution strategies      255—256
Evolution strategies, (1 + $\lambda$ ) and (1, $\lambda$ ) evolution strategies      252—255
Evolution strategies, (1 + 1) evolution strategies      253—254
Evolution strategies, bibliographical notes      279—280
Evolution strategies, comma strategy      254—255 261 274
Evolution strategies, evolution control function      261—263
Evolution strategies, evolutionary algorithms and      344
Evolution strategies, experimentum crucis of      212
Evolution strategies, GP vs      358—359 373
Evolution strategies, graphical notation of basic elements for description      253
Evolution strategies, meta-evolution strategies      225 258—260
Evolution strategies, mutation      216—231
Evolution strategies, natural evolution and      211—212
Evolution strategies, notation extensions      257—258
Evolution strategies, plus strategy      254 261 277 557
Evolution strategies, recombination      231—252
Evolution strategies, representation of individuals      213—215
Evolution strategies, selection and reproduction schemes      252—256
Evolution strategies, vectors of real numbers in      213
Evolution strategies, with Evolvica      260—265
Evolution theory      52
Evolution, artificial intelligence and      3
Evolution, as development      3
Evolution, as meta-learning      79
Evolution, as nature’s programming method      283
Evolution, as reproductive plan      64 65
Evolution, bibliographical notes      51—53
Evolution, coevolution of plant species      519—520 522—524 525
Evolution, definitions of      203
Evolution, development vs      3
Evolution, evolving vs. programming      284—286
Evolution, inheritance and      3
Evolution, interactive design by      42 45
Evolution, objective of      42
Evolution, of finite automata      288—289
Evolution, ontogenetic      3 4
Evolution, phylogenetic      4
Evolution, programming by      283—295
Evolution, second-order      226 229
Evolution, selection-mutation principle      2 6
Evolution, simplified formal model of      57—60
Evolution, string evolution example      5—33
Evolution, variational      1
Evolution: Society, Science and the Universe      52
Evolutionary algorithms      344 399 472
Evolutionary Algorithms and Emergent Intelligence      435
Evolutionary algorithms for optimization      57—77
Evolutionary algorithms for optimization, adaptation steps      62—63
Evolutionary algorithms for optimization, bibliographical notes      75—77
Evolutionary algorithms for optimization, example optimization with genetic evolutionary algorithms for optimization, algorithms      67—75
Evolutionary algorithms for optimization, general scheme      63—75
Evolutionary algorithms for optimization, main components of adaptive systems      61
Evolutionary algorithms for optimization, optimization on multimodal functions      64—67
Evolutionary algorithms for optimization, optimization through adaptive structures      60—63
Evolutionary algorithms for optimization, simplified formal model of evolution      57—60
Evolutionary Algorithms in Theory and Practice      76
Evolutionary Art and Computers      53
Evolutionary Biology of Plants, The      525
Evolutionary Computation journal      76
Evolutionary computation resources      75—77
Evolutionary Computation-Towards a New Philosophy of Machine Intelligence      76
Evolutionary Computation: The Fossil Record      75—76 294
Evolutionary design      52—53
Evolutionary Design by Computers      52 53
Evolutionary parameter optimization scheme      71—75
Evolutionary parameter optimization scheme, interpretation and evaluation steps      72—73
Evolutionary parameter optimization scheme, reproduction steps      73
Evolutionary parameter optimization scheme, selection step      73
Evolutionary parameter optimization scheme, variation step      73
Evolutionary programming      297—344 (see also finite state automata (FSA))
Evolutionary programming at work      328—338 339—341
Evolutionary programming at work, automata pruning      331—332 337
Evolutionary programming at work, comma strategy      331
Evolutionary programming at work, compacting the predictors      332 335—336
Evolutionary programming at work, detailed look at selected automata      332 336—337 339—341
Evolutionary programming at work, evaluating automaton prediction quality      328—330
Evolutionary programming at work, FSA evolution experiment      331—338 339—341
Evolutionary programming at work, original EP experiments      337
Evolutionary programming at work, plus strategy      331
Evolutionary Programming Conference series      289 294—295
Evolutionary programming selection and evolution scheme      322—325
Evolutionary programming selection and evolution scheme, basic selection scheme      322—323
Evolutionary programming selection and evolution scheme, tournament selection      323—325
Evolutionary programming, application domains      343
Evolutionary programming, automatic generation of FSA      320—322
Evolutionary programming, bibliographical notes      344
Evolutionary programming, combinatorial and parameter optimization      342
Evolutionary programming, comma strategy      325—326 331
Evolutionary programming, computer programs as FSA      298—303
Evolutionary programming, development of      297—298
Evolutionary programming, diversification of      338—344
Evolutionary programming, evolutionary algorithms and      344
Evolutionary programming, fitness-dependent mutation      341—342
Evolutionary programming, majority logic recombination      342
Evolutionary programming, mutation operators on FSA      303—320
Evolutionary programming, of finite automata      288—289
Evolutionary programming, plus strategy      325—326 331
Evolutionary programming, recombination and      288 342
Evolutionary programming, reproduction scheme      288
Evolutionary programming, selection and evolution scheme      322—325
Evolutionary programming, strong causality      342—343
Evolutionary programming, variable mutation step sizes      338 341
Evolutionary programming, with Evolvica      325—328
Evolutionsstrategie      94 279
Evolvica and EP      325—328
Evolvica and EP, adding an FSA state      307 308
Evolvica and EP, adding an FSA transition      310—311
Evolvica and EP, changing a transition’s input symbol      313—314 315
Evolvica and EP, changing a transition’s output symbol      315—316 559
Evolvica and EP, changing a transition’s source      317—318
Evolvica and EP, changing a transition’s target      320 321
Evolvica and EP, comma strategy      325—326 331
Evolvica and EP, deleting an FSA state      309
Evolvica and EP, deleting an FSA transition      312—313
Evolvica and EP, EP scheme      325
Evolvica and EP, EP scheme options      325
Evolvica and EP, evolution function      326—328
Evolvica and EP, generating a population of FSA      321—322
Evolvica and EP, generating a random (Mealy) machine      320—321
Evolvica and EP, generating a random FSA      321
Evolvica and EP, mutation function      304—305
Evolvica and EP, plus strategy      325—326 331
Evolvica and EP, starting an experiment      326

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