private void InitializePopulation()
{
var c = new List<Chromosome>();
for (int i = 0; i < PopulationSize; i++)
{
var gList = new List<Gene>();
foreach (Gene g in ChromosomeFootprint)
{
Gene gene = g.Clone();
gene.InitializeGene();
gList.Add(gene);
}
var cx = new Chromosome(gList);
c.Add(cx);
}
_currentPopulation = new Population(c);
_currentPopulation.CalculateAllFitness(_fitnessFunction);
}
public static Population SimpleRouletteSelection(this Population pCopy, int number, Random r)
{
var result = new List<Chromosome>();
var p = new Population(new List<Chromosome>(pCopy.Chromosomes));
for (int n = 0; n < number; n++)
{
var fitnessProportion = new double[p.PopulationSize];
double totalFitness = 0;
double totalProbability = 0;
for (int i = 0; i < p.PopulationSize; i++)
{
totalFitness += p.Chromosomes[i].Fitness;
}
for (int i = 0; i < p.PopulationSize; i++)
{
fitnessProportion[i] = totalProbability + (p.Chromosomes[i].Fitness/totalFitness);
totalProbability = fitnessProportion[i];
}
double random = r.NextDouble();
for (int i = 0; i < p.PopulationSize; i++)
{
if (random <= fitnessProportion[i])
{
result.Add(p.Chromosomes[i]);
p.Chromosomes.Remove(p.Chromosomes[i]);
break;
}
}
}
return new Population(result);
}
public void JoinPopulation(Population p)
{
Chromosomes.AddRange(p.Chromosomes);
}
public void Run()
{
InitializePopulation();
double maxFitness = 0;
while (_currentTrial++ < Trials)
{
Console.WriteLine("Starting trial " + _currentTrial + "...");
string filename = QSConstants.DEFAULT_DATA_FILEPATH + "result" + _currentTrial + ".txt";
using (var file = new StreamWriter(@filename))
{
for (int generation = 0; generation < Generations; generation++)
{
Console.WriteLine(generation);
var newPop = new Population();
//elitism
Population elite = _currentPopulation.ElitismSelection(2);
newPop.JoinPopulation(elite);
//populate rest of population with children
while (newPop.PopulationSize < PopulationSize)
{
//selection
Population parents = _currentPopulation.SimpleRouletteSelection(2, _randomSeed);
//crossover
Population children = Crossover.BinaryCrossover(parents[0], parents[1], _randomSeed);
newPop.JoinPopulation(children);
}
newPop.MutatePopulation(MutationRate, _randomSeed);
double average = 0;
//calculate fitness for new population
foreach (Chromosome chromosome in newPop.Chromosomes)
{
if (chromosome.Fitness.Equals(double.NaN))
chromosome.CalculateFitness(_fitnessFunction);
if (chromosome.Fitness > maxFitness)
{
maxFitness = chromosome.Fitness;
maxC = chromosome;
file.WriteLine("Generation: {0}", generation);
file.WriteLine("Fitness: {0}", maxFitness);
for (int i = 0; i < maxC.Count(); i++)
{
file.WriteLine("Gene " + i + ":" + maxC[i]);
}
file.WriteLine();
file.Flush();
}
average += chromosome.Fitness;
//Console.WriteLine(chromosome.Fitness + ": x:" + (double)chromosome.GeneList[0].geneValue + "; y:" + (double)chromosome.GeneList[1].geneValue);
}
//Console.WriteLine(average/newPop.Chromosomes.Count);
//Console.WriteLine("-----------------------------------");
_currentPopulation = newPop;
}
}
maxC.CalculateFitness(_fitnessFunction);
}
Console.WriteLine("Execution complete.");
}
