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Coley D.A. — An Introduction to Genetic Algorithms for Scientists and Engineers
Coley D.A. — An Introduction to Genetic Algorithms for Scientists and Engineers



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Название: An Introduction to Genetic Algorithms for Scientists and Engineers

Автор: Coley D.A.

Аннотация:

Designed for those who are using GAs as a way to help solve a range of difficult modelling problems. Designed for most practicing scientists and engineers, whatever their field and however rusty their mathematics and programming might be.


Язык: en

Рубрика: Биология/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Год издания: 1997

Количество страниц: 227

Добавлена в каталог: 16.04.2007

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID
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Предметный указатель
$\Omega$ (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, $\Omega$      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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