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Back T., Fogel D.B., Michalewicz Z. — Evolutionary computation (Vol. 1. Basic algorithms and operators)
Back T., Fogel D.B., Michalewicz Z. — Evolutionary computation (Vol. 1. Basic algorithms and operators)



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Название: Evolutionary computation (Vol. 1. Basic algorithms and operators)

Авторы: Back T., Fogel D.B., Michalewicz Z.

Аннотация:

The first volume provides a very broad coverage of the "evolutionary" literature. Reading this first volume will probably save you a lot of time. The evolutionary literature actually becomes quite large these days. The focus of this first volume is on broad coverage, not details although some chapters are already quite advanced.
If you need a fast coverage of the literature in evolutionary computation, this is the book. Pointers to all decisive contributions to the field are there. Reading from cover to cover might be difficult if the purpose is to introduce one to the field, but this is certainly the reference i would suggest to students and researchers new in this field. Each chapter is self-contained and references to the most important works for each chapter is provided at the end of the chapter.


Язык: en

Рубрика: Computer science/Генетика, нейронные сети/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Год издания: 2000

Количество страниц: 339

Добавлена в каталог: 09.11.2005

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID
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Предметный указатель
GENITOR system      67 189 209
Genomic DNA      35
Genotypes      23 64 231 232
Geometrical crossover      273
Global convergence      199 200
Gradient descent      1
Gray code      76 133
Gray-coded strings      128
Grow mutation operator      248
H-infinity optimal controllers      8
Hamiltonian circuit      1 39
Hamming cliffs      128
Hamming distance      75 133 148
Haploid      27 35
Hardy — Weinberg theorem      35
Heating, ventilation and air conditioning (HVAC) controllers      98
Hetero/ygote      30
Heuristic crossover      273
Hinton and Nowlan's model      310 311 312
Hitchhiking effects      71
Homozygotes      30
Hybrid algorithms      308—311
Hybridizations      60
Hydrodynamics      81
Hyerplanes      72 177 178 179
Hyerplanes analysis      69
Identification applications      7 8
IEEE World Congress on Computational Intelligence (WCCI)      41
Image processing      9
Image processing applications      44
Implicit parallelism      134 190 191
Incremental models      217 220—222
Inductive bias      268
Infinite impulse response (IIR) filters      6
Information retrieval (IR) systems      9
information storage      9
Inheritance systems      28 29 37
Initialization      74 89
Insert operator      245 246
Intelligent behavior      42
Interactive evolution (IE)      228—234
Interactive evolution (IE) application areas      231 232
Interactive evolution (IE) approach      229—231
Interactive evolution (IE) difficulties encountered      231
Interactive evolution (IE) formulation of algorithm      230
Interactive evolution (IE) further developments and perspectives      232 233
Interactive evolution (IE) history and prospects      228 229
Interactive evolution (IE) minimum requirement      228
Interactive evolution (IE) overview      231
Interactive evolution (IE) problem definition      229
Interactive evolution (IE) samples of evolved objects      233
Interactive evolution (IE) selection      229
Interactive evolution (IE) standard system      229
Interceptor      94
Interdisciplinary Workshop in Adaptive Systems      46
Intermediate recombination      85
International Conference on Genetic Algorithms (ICGA)      47
International Society for Genetic Algorithms (ISGA)      47
Introns      163 164
Inverse permutation      141 142
inversion      145
Inversion operator      77
Island models      36 77
Job shop scheduling (JSS)      5
Joint plate      48
Juxtapositional phase      74
k-ary alphabet      134
Knapsack problems (KP)      6 132
Knowledge-augmented operators      317—319
Kohonen feature map design      6
Lamarckian inheritance      308
Languages      60
Laplace-distributed mutation      240
Learning algorithms      308
Learning and evolution      308 309
Learning as phenotypic variance      313 314
Learning classifier systems (LCSs)      114—123
Learning classifier systems (LCSs) introduction      117 118
Learning classifier systems (LCSs) Michigan approach      118
Learning classifier systems (LCSs) operational cycle      118
Learning classifier systems (LCSs) Pitt approach      118
Learning classifier systems (LCSs) stimulus response      118
Learning classifier systems (LCSs) structure      117
Learning models      316
Learning problems      114—118
Life cycle model      28
LINAC      see “Linear accelerator”
Linear accelerator, design      7
Linear ranking      188 215
Linear-quadratic-Gaussian controllers (LQG)      8
Linguistics      9
Linkage, equilibrium      264
Lisp      75 108 109 128 129
Local search (LS)      149
Machine intelligence      2
Machine learning (ML) problems      321
Mapping function      142
Markov decision problem      115
Mask      257
Mathematical analyses      44
Matrix representations      143—145
Maximum independent set problem      132
Medical applications      98
Medicine      44
Meiosis      27 32
Memory cache replacement policies      5
Mendel, Gregor      28
Mendelian experiment      28 29 30
Mendelian inheritance      261
Mendelian ratios      31
Messenger RNA (mRNA)      33 34
Messy GA      251
Messy genetic algorithms (mGAs)      72 73 164
Metropolis algorithm      196
Military applications      9
Minimux optimization      87
Mixed-integer optimization      87
Mixed-integer representation      163
Mixing events      268
Monohybrid ratio      30
Monte Carlo (MC) generators      7
Multicriteiion problems      50
Multimodal objective functions      84
Multiple criterion decision making (MCDM)      22
Multiple-input, multiple-output (MIMO) model      98 99
Multipoint crossover      266
Multiprocessor system      5
Multivariate zero-mean Gaussian random variable      137
Mutation      23 35 42 59 60 61 68—75 89 108 152 228 237—555
Mutation function      314
Mutation operators      84 92 93 125 132 158 237—255
Mutation-selection equilibrium variance      314
Mutations      36
n-point crossover      259 266
Near misses      308
Neighborhood, model      77
Neo-Darwinism      23 24 37
Nesting technique      86
Network design problems      6
Neural networks      6 43 44 99 163
Neural networks design      97
Neural networks training      43 44
Neutral molecular evolution theory      37
Niching methods      50
No-free-lunch (NFL) theorem      20 21
nodes      104
Nondeterministic-polynomial-time (NP) complete problems      97
Nondisruptive crossovers      267
Nonlinear optimization problems      1
Nonlinear ranking      188 189 215
Nonlinear ranking selection      202 203
Nonoveilapping systems      208
Nonoverlapping populations      205
Nonregressive evolution      43
Nonuniform mutation      243
Object parameters      136
object variables      136—138
Objective functions      172 173 178
Objective values      192
Offspring machines      42 152
One-point crossover      258 265 266 271
Online planning/navigating      5
Operator descriptions      149
Operon system      34
Optical character recognition (OCR)      9
Optimization methods      1 2 89 160
Optimum convergence      241
Order-based mutation      246
Ordering schemata (o-schemata)      146 147
Oren — Luenberger class      85
Overlapping populations      205 208 209 210
Ovum      27
Packing problems      6
Pairwise implementation      70
Pairwise mating      70 71 75
Parallel genetic algorithms (PGAs)      77
Parallel Problem Solving from Nature (PPSN) (workshop)      41
Parallel recombinative simulated annealing (PRSA)      196 197
Parallel recombinative simulated annealing (PRSA) parameters and their settings      197
Parallel recombinative simulated annealing (PRSA) pseudocode for common variation      197
Parallel recombinative simulated annealing (PRSA) working mechanism      196 197
Parameter optimization      133
Parameter settings      22
Parasites      120 121
Parse tree representation      1 55—59
Parse tree representation complex numerical function      157
Parse tree representation primitive language      156 157
Parse trees      134 248—250
Partially matched crossover (PMX) operators      147
Pattern recognition      45
Payoff matrix      99
Penalty functions      95 130
permutations      75 128 129 134 139—150 243—246
Permutations inverse      141 142
Permutations, mapping integers to      140
Pharmaceutical fermentation process data      8
Phenotype table      31
Phenutypes      23 31 232 313 314
Pitt approach to rule learning      47
Planning applications      4—6
Pleiotropy      23
Ploidy      35
Point mutation      35
Polygeny      23
Poolwise CCV algorithm      75
Poolwise mating      75
Poolwise methods      71
Poolwise schemes      70
Population models      214
Population parameters      228
Population size      198
Populations      35—37
Position-based mutation      246
Positional bias      269
Primordial phase      74
Prisoner's Dilemma      98 99 153 154
Probability density function (pdf)      239 240
Proportional selection      64 90 167 172—182
Proportional selection theory      176—180
Protein secondary-structure determination      9
Protein segments      9
PRSA      see “Parallel recombinative simulated annealing”
Pseudo-Boolean optimization problems      132 134
Pseudocode      166—170
Punctuated crossover      260
Q-learning      116
Q-values      116
Quantitative genetics models      313—316
Query construction      10
Random decisions      48
Random keys      146
Random mutation hill climbing (RMHC)      243
Random program trees      106
Random search      1
Randomness      1
Rank-based selection      66 169 187—194 209
Rank-based selection overview      187 188
Rank-based selection theory      190—194
ranking      187 188 215 216 218 219
Real-valued parameters      75
Real-valued vectors      60 128 134 136—138 2393 270—274
Recombination      59 60 85 106 107 152 228 256—307
Recombination bias      269
Recombination distributions      261 264 265 270
Recombination dynamics      262 263
Recombination events      257 265 266 269
Recombination formal analysis      261
Recombination-mutation-selection loop      61
Recurrence relations      263
Reduced surrogate recombination      261
Reinforcement learning problem      115 117
Replacement biased v. unbiased strategy      67
Replacement selection      67
Representation      75—77 127 128 228 235;
Representation alternative      145 146
Representation guidelines for suitable encoding      160—162
Representation nonstandard      163 164
Representation, importance of      128—130
Representations      127—131
Reproduction      66
Reproductive plans      45
Reproductive rate      192
Resource scheduling      139
REVOP      40
Ribonucleic acid (RNA)      33
Robbins equilibrium      264
Robots      see also “Evolutionary robotics”
Robots applications      9
Robots control systems      8
Robots optimization applications of EP      43
Robots path planning      5
Robustness      1 2 20
Roulette wheel, sampling algorithm      175 176
Route planning      43
Routing problems      4 5 97
Rule-based learning, Pitt approach to      47
S-expressions      75
Sampling algorithms      175—177
Scaling      66
Scheduling      5
Scheduling problems      5
Schema analysis      45
Schema bias      270
Schema processing      44 130 134
Schema theorem      134 177 178
Schemata      69 134 265 266 269
Scramble mutation operator      245 246
Search operators      228
Search operators introduction      235 236
Second-order schemata, transmission probabilities for      268
Segmented crossover      73
Selection      24 25 37 45 59 89
Selection algorithm monotonic      191
Selection algorithm strictly monotonic      191
Selection biasing      66 67
Selection differential      179 192 193
Selection intensity      184 192 213 224
Selection introduction      166—171
Selection mechanisms, comparison      212—227
Selection methods      201—204
1 2 3
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