private void GenerateSequence(ref int x0, ref int a, ref int b, ref int c) // returns also the Optmal PSNR value
{
// Defining genes Domains
BoundPair[] parameterDomain = new BoundPair[4];
//Domain of x0
parameterDomain[0] = new BoundPair(0, imageWidth * imageHeight);
//Domain of a
parameterDomain[1] = new BoundPair(1, imageWidth * imageHeight);
//Domain of b
parameterDomain[2] = new BoundPair(1, imageWidth * imageHeight);
//Domain of c
parameterDomain[3] = new BoundPair(1, imageWidth * imageHeight);
GeneticAlgorithm GA = new GeneticAlgorithm(
30, // Population Size
4, // Chromosome Length
100, // Number of Generations
0.85, // Crossover Ratio
0.25, // Mutation Ratio
new GeneticAlgorithm.EvaluationDelegate(PSNR), // Fitness Function
parameterDomain, // Domain of each Parameter
1, // Elitism Factor
GeneticAlgorithm.SelectionMode.RouletteWheel //Selection Method
);
ImageHiding.GA.Organism OptimalSequence = GA.Run(encryptWorker, encryptEvent); // Run the Genetic Algorithm
//Get the Best Result
x0 = OptimalSequence.chromosome[0];
a = OptimalSequence.chromosome[1];
b = OptimalSequence.chromosome[2];
c = OptimalSequence.chromosome[3];
double trivial = PSNR(0, 1, 1, 1);
double opt = PSNR(OptimalSequence.chromosome);
int x = 0;
}
private void crossover(ref Organism parent1, ref Organism parent2, out Organism child1, out Organism child2)
{
int pivot = GARandomGenerator.Next(0, chromosomeLength - 1);
for (int i = pivot; i < chromosomeLength; i++)
{
int temp = parent1.chromosome[i];
parent1.chromosome[i] = parent2.chromosome[i];
parent2.chromosome[i] = temp;
}
child1 = parent1;
child2 = parent2;
}
