//The method the sort the genomes.
#endregion
public void PopulationGeneration()
{
for (int i = 0; i < kInitialPopulation; i++)
{
ListGenome aGenome = new ListGenome(kMin, kMax, kParameterMin, kParameterMax, kParameterStep);
int kLength = kParameterMin.Length;
int crossoverRandomSeed = ListGenome.TheSeed.Next(kLength);
kCrossover = crossoverRandomSeed;
//kCrossover or crossover point was a random number among the length of the genome.
aGenome.SetCrossoverPoint(kCrossover);
aGenome.CalculateFitness(calibrationRoundID, dataDirectory, workDirectory, projectDirectory, genericAlgorithmsDirectory, observationArray, stdArray, kParameterSiteIndex, amplificationFactor, CV, observationDateArray, observationSeperator);
aGenome.OutputString(observationArray);
Genomes.Add(aGenome);
}
Genomes.Sort(myCompareMethod);
int kPopulationLimitOfInitialPopulation = 20 * kPopulationLimit;//The population limit of first generation is 10 times of the limits of other generations.
// kill all the genes above the population limit
if (Genomes.Count > kPopulationLimitOfInitialPopulation)
{
Genomes.RemoveRange(kPopulationLimitOfInitialPopulation, Genomes.Count - kPopulationLimitOfInitialPopulation);
}
CurrentPopulation = Genomes.Count;
}
public void LocalPopulationGeneration( )
{
int parameterNumber = optimalParameterValueArray.Count;
double bestFitness = 0;
int numberOfGenomes = 0;
double[] kLocalParameterValue = new double[parameterNumber];
double[] kLocalParameterMin = new double[parameterNumber];
double[] kLocalParameterMax = new double[parameterNumber];
double[] kLocalParameterStep = new double[parameterNumber];
double[] kLocalParameterStepLimit = new double[parameterNumber];
for (int i = 0; i < optimalParameterValueArray.Count; i++)
{
double tempLocalParameterValue = Convert.ToDouble(optimalParameterValueArray[i]);
kLocalParameterValue[i] = tempLocalParameterValue;
double tempInitialParameterMin = kParameterMin[i];
double tempInitialParameterMax = kParameterMax[i];
kLocalParameterMin[i] = Math.Max((tempLocalParameterValue * (1 - kLocalParameterRatio)), tempInitialParameterMin);
kLocalParameterMax[i] = Math.Min((tempLocalParameterValue * (1 + kLocalParameterRatio)), tempInitialParameterMax); //Restric the local seaching range of the parameters with climbing hill methods. Modified by He, 14/10/2010.
kLocalParameterStep[i] = kParameterStep[i] * (kLocalParameterRatio / kLocalParameterRatioPartition); //Define the local searching spaces.
}
for (int j = 0; j < kLocalPopulation; j++)
{
ListGenome aGenome = new ListGenome(kMin, kMax, kLocalParameterValue, kLocalParameterMin, kLocalParameterMax, kLocalParameterStep, kParameterChangeProbability);
aGenome.CalculateFitness(calibrationRoundID, dataDirectory, workDirectory, projectDirectory, genericAlgorithmsDirectory, observationArray, stdArray, kParameterSiteIndex, amplificationFactor, CV, observationDateArray, observationSeperator);
aGenome.OutputString(observationArray);
if (j == 0)
{
Genomes.Add(aGenome);
}
//Add the first genome.
numberOfGenomes = Genomes.Count;
double tempFitness = aGenome.CurrentFitness;
if (tempFitness > bestFitness)
{
Genomes.RemoveAt((numberOfGenomes - 1));//Remove the last element of Genomes.
Genomes.Add(aGenome);
bestFitness = tempFitness;
}
}
}