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Back T., Fogel D.B., Michalewicz Z. — Evolutionary computation (Vol. 2. Advanced algorithms and operators)
Back T., Fogel D.B., Michalewicz Z. — Evolutionary computation (Vol. 2. Advanced algorithms and operators)



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Название: Evolutionary computation (Vol. 2. Advanced algorithms and operators)

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

Аннотация:

Volume I provided the general theory of evolutionary computation. This second volume on the other hand aims at introducing the reader to more practical aspects of evolutionary computation. While i found the first volume great, this second volume lacked the details that are required to provide an intuition of the working of advanced evolutionary techniques. I feel that "How to solve it" by Michalewicz and Fogel and "Genetic algorithms + data structures = evolution programs" by Michalewicz both provide this experience useful to implement evolutionary techniques, by not trying to trade-off pages for understandability. I would not recommend this book because it tries to introduce advanced aspects that are too difficult to cover in a single chapter each. If you really want to understand the practice of evolutionary techniques, you need a good intuition of how the various operators and structures work on real problems, just reading a few pages will not do the job.


Язык: en

Рубрика: Computer science/Генетика, нейронные сети/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Год издания: 2000

Количество страниц: 270

Добавлена в каталог: 09.11.2005

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID
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Предметный указатель
Absorption time      145
Adaptation      170 182
Adaptive control      185
Adaptive landscape      102 103
Adaptive parameter control      180 189
Adaptive techniques, recombination operators      160—164
Adaptive value      102
AIC (Akaike information criterion)      15 16
Akaike information criterion (AIC)      15 16
ALECSYS      258
Artificial intelligence (AI)      38
Artificial Neural Networks      see "Neural networks"
Asymptotics      247
Baldwin effect      56
Base pair mutations of Eschericia coli      144
Base-level EAs      213 214
Behavioral memory approach      71
Bias      154 155
Biased continuous competition pattern      234
Binary decision diagram (BDD)      261
Binary representation      149
Binary search spaces      206 207
binary strings      4 5
Binary variables, coding      9
Binary vectors      see "Binary strings"
Binary-string encodings      45
Bipartite competitive fitness pattern      228
Bit strings      see "Binary strings"
Building block hypothesis (BBH)      164
Cauchy density function      197
Chromosomes      111 178
Classification application      231
Classification rules      18
Classifier systems (CFS)      161
Classifier systems (CFS), hardware      257 258
Coarse-grained PGAs      253
Coevolution, definition      224
Coevolutionary algorithms      224—238
Coevolutionary genetic algorithm (CGA)      228—234
Coevolutionary genetic algorithm (CGA), applications      231—234
Coevolutionary genetic algorithm (CGA), future research      236
Coevolutionary model      70 71
Coevolutionary, introduction in EAs      224
Coevolving sorting networks      227 228
Comma-selection      146
Communication topology      105 106
Competition pattern      225
Competitive evolution      220
Competitive fitness      12—14 225—227
Complexity-based fitness evaluation      15—24
Computation time, evolutionary algorithms (EAs)      247—252
Computation time, mutation operators      250
Computation time, recombination operators      251
Computation time, selection operators      247—250
Conceptualization      102
Connection Machine (CM)      257
Constrained optimization problems (COPs)      75 76 77
Constraint satisfaction      232
Constraint-handling methods      69—74
Constraint-handling techniques introduction      38—40 see
Constraint-preserving operators      62—68
Constraint-satisfaction problems (CSPs)      38 75—86
Constraint-satisfaction problems (CSPs), changing the search space      81
Constraint-satisfaction problems (CSPs), solving the transformed problem      82 83
Constraint-satisfaction problems (CSPs), transforming to constrained optimization problem      80
Constraint-satisfaction problems (CSPs), transforming to evolutionary-algorithm-suited problems      77
Constraint-satisfaction problems (CSPs), transforming to free optimization problem      78
Control level parallelism      254
Control problems, coding      9 10
Convergence velocity      143 145
Cooperative coevolutionary genetic algorithms (CCGAs)      235
Correlated mutations      143
Cost assignment strategy      26
Covariances      180
Cross-validation      17
Crossover      178
Crossover operators      111 219
Crossover, one-point      181
Crossover, uniform      181
Crowding techniques      89 90 219
Cultural algorithms      71 72
Cut points      153
Data level parallelism      254
Decision trees      18 19
Decoders      49—55
Decoders, examples      58—61
Decoders, formal description      50—55
Decoders, selection procedure      58 59
Decoding functions      2 4—11
Deme attributes      129—131
Demes      103 104 255
Density classification      233
Derived delta      163
Deterministic crowding      89 90
Deterministic crowding algorithm      89
Deterministic evaluations      244
Deterministic parameter control      179 180
Diffusion models      107 125—133
Diffusion models, formal description      125 126
Diffusion models, implementation techniques      126—131
Diffusion models, theoretical research      131 132
Distribution bias      154 155
Dynamic parameter control      189
Edge recombination crossover      65
Eldredge Gould theory      104
Elitist model      219
Embryology-oriented approach      261
Encoding functions      4—11
Encore      132
Encounter      229
Engineering-oriented approach      260
epochs      106
Evaluation function      178
Evolution      174 178
Evolutionary algorithms (EAs)      174 see
Evolutionary algorithms (EAs), $(\mu + \lambda)$      249
Evolutionary algorithms (EAs), $(\mu, \lambda)$      248
Evolutionary algorithms (EAs), components      179
Evolutionary algorithms (EAs), computation time      247—252
Evolutionary algorithms (EAs), dedicated hardware implementations      256—258
Evolutionary algorithms (EAs), effectiveness      182
Evolutionary algorithms (EAs), hardware realizations      253—263
Evolutionary algorithms (EAs), implementation      239—246
Evolutionary algorithms (EAs), intelligence      184
Evolutionary programming (EP)      4
Evolutionary robotics      see also "Robots"
Evolutionary strategies (ESs)      4 180
Evolutionary strategies (ESs), $(\mu + \lambda)$      248 249
Evolvable hardware (EHW)      253 258—261
Exemplars      see "Taxon-exemplar scheme"
Exploration power      154
Fast evolutionary programming      197
Feasibility condition      77
Feasibility search space      77
Feedback      172 175
Field programmable gate arrays (FPGAs)      256 257 259
Fine-grained PGAs      256
Finite-state machines      158 205
Fitness assignment strategy      30
Fitness evaluation      1—3 25 26
Fitness evaluation, competitive      12—14
Fitness evaluation, complexity-based      15—24
Fitness evaluation, minimum-description-length-based      17 18
Fitness evaluation, overview      1 2
Fitness evaluation, related problems      2
Fitness landscapes      87
Fitness proportional selection (FPS)      30 89
Fitness sharing      32 87—89 235
Fitness values      152
Fitness variance of formae      160
Floating-point coding      7 8
Formae      158 165
FORTRAN      248
Free optimization problem (FOP)      76
Free search space      76
Fuzzy rules      163
Gate level evolution      259
Gaussian mutation      174 176
Gaussian mutation operator      174
Generalized Rastrigin function      199
Genetic algorithms (GAs)      see also "Specific applications"
Genetic algorithms (GAs) with punctuated equilibria (GAPE)      104
Genetic algorithms (GAs), design      171
Genetic drift      31
GENOCOP III      59 60 73
Genotypes      81 111
Genotypic mating restriction      96 97
Genotypic sharing      88
Genotypic-level combination      153—156
Goal attainment method      27 28
Goal programming      35
Granularity      101
Gray code      198 199
Gray-coded strings      6 7
Hamming cliffs      157
Hamming distance      6 147
Hardware description language (HDL)      259
Hardware realizations, evolutionary algorithms (EAs)      253—263
Heapsort      248
Heuristics      155 156
Identically distributed (IID) random variables      241
Implicit parallelism      134
Inherited delta      163
Integer search spaces      202—204
Interior solutions      41
Intermediary recombination      195
Internet      132
Interval schemata      157
Inverse fitness interaction      224
Island models      101—124 127
Island models, influence of parameters on evolution      113—119
Island models, VLSI circuit design problem      108—113
Isolation-by-distance model      253
Iterated prisoner's dilemma (IPD)      226
Knapsack problem (KP)      49 57 58 147
Ladder neighborhood      130 131
Lamarckian evolution      56
Learning algorithms      94
Learning rates      189
Learning rule adaptation      220
Lexicographic approach      29 30
Lifetime fitness evaluation (LTFE)      230 234 236
Linear congruential method      240
Linkage problem      7
Lisp      4 158 161
Local delta      163
Mask programmable gate arrays (MPGAs)      259
Mating restriction      32 94 96 97
Median-rank approach      31 32
Mesh (or grid) neighborhood      130
Messy coding      7
Meta-algorithm      172
Meta-evolutionary approaches      212—223
Meta-evolutionary approaches, formal description      214 215
Meta-evolutionary approaches, parameter settings      216 217
Meta-evolutionary approaches, pseudocode      215 216
Meta-evolutionary approaches, related work      217—220
Meta-evolutionary approaches, theory      217
Meta-evolutionary approaches, working mechanism      212—214
Meta-GA approach      218 219
Meta-level EAs      213
Meta-optimization      212
Metrics      192
Micro-GA      136
Migrant selection strategies      116—118
MIMD (multiple instruction, multiple data) system      99 127 254 255
Minimax approach      27—29
Minimax problem      235
Minimum Description Length (MDL) Principle      15—23
Minimum description length (MDL)-based fitness evaluation      17 18
Minimum-message-length principle (MML)      15—23
Modem synthesis      102
Monte Carlo (MC) evaluation      244 245
Multimutational self-adaptation      205
Multiobjective function optimization      87
Multiobjective optimization method      25—37 69
Multiobjective optimization, current evolutionary approaches      26
Multiple instruction, multiple data (MIMD) system      99 127 254 255
Mutation      98 178
Mutation function      219
Mutation mechanism      199
Mutation operators      112 178 189—205 242 243
Mutation operators, computation time      250
Mutation parameters      142—151
Mutation parameters for direct schedules      144—149
Mutation parameters for self-adaptation      143 144
Mutation rate      142 144 145 147
Mutation step size parameter      178
Mutation value replacement method      219
Mutational step size      143
n-point recombination      154
Natural evolution, theories      102—104
Near-feasible threshold (NFT)      46
Network weight optimization problem      218
Neural networks      231 260
Neural networks, weight optimization problem      219
Niching methods      87—92
Niching methods, parameters and extensions      88 89
Niching methods, theory      90 91
Noncoevolutionary EAs      228
Nonlinear optimization problems, with linear constraints      64 65
Nonlinear programming      38 59—61
Normalized modification      148
Number of populations      226
Objective fitness      12
Objective fitness functions      12
Objective function      194
Occam's razor      15
Occupancy rate      162
Operations research (OR)      38 see
Operator delta      163
Operator tree      163
Optimal schedules      145
Optimal schema processing      135 136
Optimization problems      174 176
Pallet loading      50
Parallel algorithms      99
Parallel computer architectures      254
Parallel environments for diffusion model implementation      127 128
Parallel evaluation      245 246
Parallel generate-and-test algorithm      245
Parallel genetic algorithms (PGAs)      120 253
Parallel genetic algorithms (PGAs), overview      253—256
Parallel structure      112
Parallelism      255
Parallelization      101 102
Parameter changes      185
Parameter control      170—187
Parameter control, classification schemes      170 171
Parameter control, on-line      185
Parameter control, value of      184
Parameter settings by analogy      173
Parameter settings, optimal      172
Parameter settings, optimizing      183
Parameter tuning      170 173
Parameter values      170 174 181
Pareto optimality      25
Pareto ranking      32 33
Pareto ranking with goal and priority information      33—35
Parthenon      260
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