Генетический алгоритм: теория и практика. Бураков М.В. - 122 стр.

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СПб ГУАП | Санкт-Петербург

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
options =
PopulationType: �doubleVector’
PopInitRange: [2x1 double]
PopulationSize: 20
EliteCount: 2
CrossoverFraction: 0.8000
MigrationDirection: �forward’
MigrationInterval: 20
MigrationFraction: 0.2000
Generations: 100
TimeLimit: Inf
FitnessLimit: -Inf
StallGenLimit: 50
StallTimeLimit: 20
TolFun: 1.0000e-006
TolCon: 1.0000e-006
InitialPopulation: []
InitialScores: []
InitialPenalty: 10
PenaltyFactor: 100
PlotInterval: 1
CreationFcn: @gacreationuniform
FitnessScalingFcn: @fitscalingrank
SelectionFcn: @selectionstochunif
CrossoverFcn: @crossoverscattered
MutationFcn: @mutationgaussian
HybridFcn: []
Display: �final’
PlotFcns: []
OutputFcns: []
Vectorized: �off’


>> options.PopulationSize
ans =
20


>> options = gaoptimset(�PopulationSize’, 100)

>> options = gaoptimset(�CrossoverFraction’, 0.9)

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