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| Авторизация |
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| Поиск по указателям |
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| Coley D.A. — An Introduction to Genetic Algorithms for Scientists and Engineers |
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| Предметный указатель |
 (the objective function) 6
Accuracy, basic problem with 21
Artificial landscapes 36
Binary encoding 10
Building block 56
Chromosome 17
Combinatorial optimisation 59
Complex search space, example of 9
Complex-valued unknowns 22
Constraints 72
Convergence velocity 41
Convergence, problems of 43
Cost function 5
Crossover 10
Crossover, alternative methods 83
Crossover, reduced surrogate operator 84
Crossover, single point 25
Crossover, two-point 84
Crossover, typical settings 25
Crossover, uniform 84
Deception 57
Direct search 8
Domination 14
Elitism 25
Encoding, Gray 87
Encoding, logarithmic 87
Encoding, principle of meaningful building blocks 86
Encoding, principle of minimal alphabets 86
Enumerative search 7
Evolution strategies 32
Evolutionary programming 32
Exploitation 25
Exploration 25
Fitness landscape 16
Fitness scaling 43
Generation 10
Generation gap 83
Genetic diversity 14
Genetic drift 45
Genotype 17
Global maximum 4
Global optimum 4
Gray encoding 87
Hybrid algorithms 76
Implicit parallelism 56
Least-squares 5
LGA 17
LGADOS 28
Little Genetic Algorithm see “LGA”
Local maxima 4
Local minimum 8
Local optima 4 9
Messy GA 73 85
Meta GAs 89
Multicriteria optimisation 73
Multiparameter problems 22
Mutation 10
Mutation, alternative definition 23
Mutation, alternative methods 89
Mutation, possible settings 22
| Mutation, the role of 14
Non-dominated sorting 74
Non-integer unknowns 19
Objective function, 5
Off-line performance 37
On-line performance 37
Organism 17
Parallel algorithms 90
Parallel algorithms, diffusion 90
Parallel algorithms, global 90
Parallel algorithms, island 90
Parallel algorithms, migration 90
Pareto optimality 73
Pareto ranking 74
Partially Matched Crossover see “PMX”
Path-orientated see “Search”
Penalty function 72
Phenotype 17
PMX 63
Population 10
Principle of meaningful building blocks see “Encoding”
Principle of minimal alphabets see “Encoding”
Random search 9
Reduced surrogate operator see “Crossover”
Robustness 18
Roulette wheel selection 23
Schema 46
Schema, defining length 51
Schema, growth equation 54
Schema, order 51
Schema, the effect of crossover 55
Schema, the effect of mutation 56
Search, path-orientated 78
Search, volume-orientated 78
Selection 10
Selection, alternative methods 78
Selection, fitness-proportional 23
Selection, ranking methods 81
Selection, roulette wheel 23
Selection, sampling errors 79
Selection, sigma scaling 83
Selection, steady-state algorithms 83
Selection, stochastic sampling 80
Selection, stochastic universal sampling 81
Selection, take-over time 78
Selection, tournament 82
SGA 17
Sharing 67
Simple Genetic Algorithm see “SGA”
Simulated annealing 9
Species 69
Speed, general considerations 84
Steady-state algorithms see “Selection”
String 17
Take-over time see “Selection”
Temporary population 14
Test functions 38
Travelling salesman problem see “TSP”
TSP 59
TSP, use of heuristics 77
Volume-orientated see “Search”
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