public void RunGA(int runsNumber, TxtParser textParser, int populationQuantity, double MutationRate, double CrossoverRate, int GenerationsNumber)
{
txtParser = textParser;
population = new Population[runsNumber];
oper = new Operator();
for (series = 0; series < runsNumber; series++)
{
population[series] = oper.InitialiseFromInitialData(populationQuantity, txtParser.Numbers, txtParser.NumberOfLines,
txtParser.NumberOfColumns, percentOfChangeGeneInInitializationStep);
tempPopulation = new Population(populationQuantity);
mutationRate = (int)(MutationRate * population[series].PopulationQuantity);
crossoveRate = (int)(CrossoverRate * population[series].PopulationQuantity);
generationNumber = GenerationsNumber;
population[0] = oper.FitnessEvaluationByMinAndQuantity(population[0], txtParser.Numbers, txtParser.NumberOfLines, txtParser.NumberOfColumns);
for (generation = 0; generation < generationNumber; generation++)
{
population[series].SortListByFitness();
for (int i = 0; i < crossoveRate; i++)
{
parentIndiv = oper.SelectionTournament(population[series], 4);
offspringsIndiv = oper.TwoPointCrossover(parentIndiv);
tempPopulation.AddIndividual(offspringsIndiv);
}
for (int i = 0; i < mutationRate; i++)
{
population[series] = oper.MutationFromInitialData(population[series], txtParser.Numbers, txtParser.NumberOfLines);
population[series] = oper.MutationFromInitialDataInSecondChromosome(population[series], txtParser.Numbers, txtParser.NumberOfLines);
}
population[series] = oper.FitnessEvaluationByMinAndQuantity(population[series], txtParser.Numbers, txtParser.NumberOfLines, txtParser.NumberOfColumns);
population[series] = oper.ReductionSelecionScheme(population[series], tempPopulation);
}
}
}
public void RunGA(int numberOfParallelPopulations, TxtParser textParser, int populationQuantity, double MutationRate, double CrossoverRate, int GenerationsNumber)
{
txtParser = textParser;
population = new Population[numberOfParallelPopulations];
oper = new Operator();
Random rnd = new Random();
int indx = 0;
for (series = 0; series < numberOfParallelPopulations; series++)
{
population[series] = oper.InitialiseFromInitialData(populationQuantity, txtParser.Numbers,
txtParser.NumberOfLines, txtParser.NumberOfColumns, percentOfChangeGeneInInitializationStep);
mutationRate = (int)(MutationRate * population[series].PopulationQuantity);
crossoveRate = (int)(CrossoverRate * population[series].PopulationQuantity);
generationNumber = GenerationsNumber;
population[series] = oper.FitnessEvaluationByMinAndQuantity(population[series], txtParser.Numbers, txtParser.NumberOfLines, txtParser.NumberOfColumns);
}
for (generation = 0; generation < generationNumber; generation++)
{
if (rnd.NextDouble() < transmissionRateBetweenParallelPopulations)
{
for (series = 1; series < numberOfParallelPopulations; series++)
{
indx = (int)(rnd.NextDouble() * (double)population[series].PopulationQuantity);
Individual indiv = population[series].GetIndividualByIndex(indx);
population[series].SetIndividualByIndex(indx, population[series - 1].GetIndividualByIndex(indx));
population[series - 1].SetIndividualByIndex(indx, indiv);
oper.FitnessEvaluationByMinAndQuantityByIndex(population[series - 1], txtParser.Numbers, txtParser.NumberOfLines, txtParser.NumberOfColumns, indx);
oper.FitnessEvaluationByMinAndQuantityByIndex(population[series], txtParser.Numbers, txtParser.NumberOfLines, txtParser.NumberOfColumns, indx);
}
}
for (series = 0; series < numberOfParallelPopulations; series++)
{
tempPopulation = new Population(populationQuantity);
population[series].SortListByFitness();
for (int i = 0; i < crossoveRate; i++)
{
parentIndiv = oper.SelectionTournament(population[series], tournamentGroupQuantity);
offspringsIndiv = oper.TwoPointCrossover(parentIndiv);
//offspringsIndiv = oper.IntermediateRecombinationCrossover(parentIndiv);
tempPopulation.AddIndividual(offspringsIndiv);
}
for (int i = 0; i < mutationRate; i++)
{
tempPopulation = oper.MutationFromInitialData(tempPopulation, txtParser.Numbers, txtParser.NumberOfLines);
tempPopulation = oper.MutationFromInitialDataInSecondChromosome(tempPopulation, txtParser.Numbers, txtParser.NumberOfLines);
}
population[series] = oper.FitnessEvaluationByMinAndQuantity(population[series], txtParser.Numbers, txtParser.NumberOfLines, txtParser.NumberOfColumns);
population[series] = oper.ReductionSelecionScheme(population[series], tempPopulation);
population[series].SortListByFitness();
}
}
}
public Individual[] RunSerialGATest(int SeriesNumber, TxtParser TextParser, int PopulationQuantity, double MutationRate,
double CrossoverRate, int GenerationsNumber, int NumberOfTestRuns)
{
for (currentTestNumber = 0; currentTestNumber < NumberOfTestRuns; currentTestNumber++)
{
SGA.RunGA(SeriesNumber, TextParser, PopulationQuantity, MutationRate, CrossoverRate, GenerationsNumber);
for (int j = 0; j < SeriesNumber; j++)
{
AddOneBestIndividualToList(SGA.PopulationList[j]);
}
}
return(individualList);
}
private void buttonLoadData_Click(object sender, EventArgs e)
{
if (openFileDialogLoadData.ShowDialog() == DialogResult.OK)
{
txtParser = new TxtParser(openFileDialogLoadData.FileName, int.Parse(textBoxObservationsNumber.Text));
if (openFileDialogTestData.ShowDialog() == DialogResult.OK)
{
txtParserForTest = new TxtParser(openFileDialogTestData.FileName, int.Parse(textBoxObservationsNumber.Text));
if (this.checkBoxTest.Checked)
{
this.buttonTestGA.Enabled = true;
}
else
{
this.buttonSearch.Enabled = true;
}
}
}
}
