/**
* Starts the genetic algorithm.
* @param The background worker, so we can report progress.
*/
public void Start(BackgroundWorker worker, DoWorkEventArgs args)
{
Population population = new Population(fitnessCalculator, PopulationSize, BitSize, GenotypeLength, GroupSize, true);
int steps = 0;
CandidateSolution solution;
int max;
Dictionary <int, List <int> > groups;
while (steps < MaxStep)
{
population = EvolvePopulation(population);
if (steps % 2 == 0)
{
solution = population.getFittest();
max = solution.Solution.Max();
groups = new Dictionary <int, List <int> >();
for (int i = 1; i <= max; i++)
{
groups.Add(i, new List <int>());
}
for (int member = 0; member < solution.Solution.Length; member++)
{
groups[solution.Solution[member]].Add(member + 1);
}
worker.ReportProgress(steps * 100 / MaxStep, new ProblemResult()
{
Groups = groups, Fitness = solution.Fitness
});
}
steps++;
}
// Finished.
solution = population.getFittest();
max = solution.Solution.Max();
groups = new Dictionary <int, List <int> >();
for (int i = 1; i <= max; i++)
{
groups.Add(i, new List <int>());
}
for (int member = 0; member < solution.Solution.Length; member++)
{
groups[solution.Solution[member]].Add(member + 1);
}
args.Result = new ProblemResult()
{
Groups = groups, Fitness = solution.Fitness
};
}
void optimizePortfolio(int populationSize, int optimalValue)
{
Random rn = new Random(10);
double genesSum = 0;
double secGenesSum = 0;
double thirdGenesSum = 0;
double fourthGenesSum = 0;
double childGenesSum = 0;
double child2GenesSum = 0;
stopWatch.Reset();
Elapsed_Time = "Time: 00:00:00.00";
stopWatch.Start();
population.initializePopulation(populationSize);
population.calculateFitness();
Console.WriteLine("Generation: " + generationCount + " Fittest: " + population.fittest + " Second Fittest: " + population.secondFittest + " least fit: " + population.leastFittest);
/*
* for (int i = 0; i < 10; i++)
* {
* Console.WriteLine("Genes: " + population.getFittest().genes[i]);
* }
*/
Read(population.fittest, generationCount);
//Continue genetics until optimal fitness value is found
while (population.fittest < optimalValue)
{
Thread.Sleep(50);
++generationCount;
selection();
crossover();
if (rn.Next() % 10 < mutationRate)
{
mutation();
}
replacement(populationSize);
for (int i = 0; i < fittest.genes.Length; i++)
{
genesSum += fittest.genes[i];
secGenesSum += secondFittest.genes[i];
thirdGenesSum += thirdFittest.genes[i];
fourthGenesSum += fourthFittest.genes[i];
childGenesSum += child.genes[i];
child2GenesSum += child2.genes[i];
}
for (int i = 0; i < fittest.genes.Length; i++)
{
// make genes add to 1
//fittest.genes[i] = (fittest.genes[i] / genesSum) * 1;
//secondFittest.genes[i] = (secondFittest.genes[i] / secGenesSum) * 1;
//thirdFittest.genes[i] = (thirdFittest.genes[i] / thirdGenesSum) * 1;
//fourthFittest.genes[i] = (fourthFittest.genes[i] / fourthGenesSum) * 1;
child.genes[i] = (child.genes[i] / childGenesSum) * 1;
child2.genes[i] = (child2.genes[i] / child2GenesSum) * 1;
}
genesSum = 0;
secGenesSum = 0;
thirdGenesSum = 0;
fourthGenesSum = 0;
childGenesSum = 0;
child2GenesSum = 0;
for (int i = 0; i < fittest.genes.Length; i++)
{
genesSum += fittest.genes[i];
secGenesSum += secondFittest.genes[i];
thirdGenesSum += thirdFittest.genes[i];
fourthGenesSum += fourthFittest.genes[i];
childGenesSum += child.genes[i];
child2GenesSum += child2.genes[i];
}
Console.WriteLine("Genes Sum: " + genesSum);
Console.WriteLine("SecGenes Sum: " + secGenesSum);
Console.WriteLine("ThirdGenes Sum: " + thirdGenesSum);
Console.WriteLine("FourthGenes Sum: " + fourthGenesSum);
Console.WriteLine("Child Genes Sum: " + childGenesSum);
Console.WriteLine("Child2 Genes Sum: " + child2GenesSum);
genesSum = 0;
secGenesSum = 0;
thirdGenesSum = 0;
fourthGenesSum = 0;
childGenesSum = 0;
child2GenesSum = 0;
updateFitnessValues();
population.calculateFitness();
Console.WriteLine("Generation: " + generationCount + " Fittest: " + population.fittest + " Second Fittest: " + population.secondFittest);
Appl_Weight = population.getFittest().genes[0] * 100;
Msft_Weight = population.getFittest().genes[1] * 100;
Amzn_Weight = population.getFittest().genes[2] * 100;
Nflx_Weight = population.getFittest().genes[3] * 100;
Fb_Weight = population.getFittest().genes[4] * 100;
Bxc_Weight = population.getFittest().genes[5] * 100;
Xom_Weight = population.getFittest().genes[6] * 100;
Jnj_Weight = population.getFittest().genes[7] * 100;
Mcd_Weight = population.getFittest().genes[8] * 100;
Jpm_Weight = population.getFittest().genes[9] * 100;
/*
* for (int i = 0; i < 10; i++)
* {
* Console.WriteLine("Genes: " + population.getFittest().genes[i]);
* }
*/
//Update chart
Read(population.fittest, generationCount);
Console.WriteLine("\n\n");
}
Console.WriteLine("\nSolution found in generation " + generationCount);
Console.WriteLine("Fitness: " + population.getFittest().fitness);
Console.WriteLine("Genes: ");
for (int i = 0; i < 10; i++)
{
Console.WriteLine(population.getFittest().genes[i]);
}
Console.WriteLine("");
stopWatch.Stop();
TimeSpan ts = stopWatch.Elapsed;
Elapsed_Time = "Time: " + String.Format("{0:00}:{1:00}:{2:00}.{3:00}",
ts.Hours, ts.Minutes, ts.Seconds,
ts.Milliseconds / 10);
}