//This takes the perants and adds them together 50% from one and 50% from the other
private void Splitandadd(Gene parent1, Gene parent2, out List <double> weights1, out List <double> weights2, List <double> kidsweights)
{
int num = _rand.Next(0, 2);
if (num == 1)
{
weights1 = parent1.GetWeights();
weights2 = parent2.GetWeights();
}
else
{
weights2 = parent1.GetWeights();
weights1 = parent2.GetWeights();
}
for (int j = 0; j < 30; j++)
{
kidsweights.Add(weights1[j]);
}
for (int j = 30; j < 60; j++)
{
kidsweights.Add(weights2[j]);
}
}
public void Start()
{
//Create populationsize of new random genes
populationpool = new List <Gene>();
for (int i = 0; i < populationsize; i++)
{
Gene gene = new Gene();
List <double> weights = new List <double>();
for (int j = 0; j < 60; j++)
{
weights.Add((_rand.NextDouble() * 2) - 1);
}
populationpool.Add(gene);
populationpool[i].ChangeWeights(weights);
}
//setting up a stopwatch so i can exit the program once it reaches a correct value
Stopwatch stopWatch = new Stopwatch();
stopWatch.Start();
while (running == true)
{
gennumber++;
List <double> avglist = new List <double>();
double avg = 0;
double bestfit = 0;
// Run through the population calculate and asign the fitness of each gene
// This section also sets up the calculation for the best and average of the generation
foreach (Gene G in populationpool)
{
double fitness;
List <double> weights = new List <double>();
weights = G.GetWeights();
fitness = Program.GetResults(weights);
G.ChangeFitness(fitness);
if (bestfit < fitness)
{
bestG = G;
bestfit = fitness;
}
avglist.Add(fitness);
}
bestfit = 0;
// changing the list to an int
foreach (double d in avglist)
{
avg += d;
}
TimeSpan ts = stopWatch.Elapsed;
double fits = bestG.GetFitness();
int x = (int)ts.TotalSeconds;
//checks if the exit condition has been satisfied
if (x > programtime)
{
textwights = bestG.GetWeights();
running = false;
}
//Prints the curent results to console
Console.Write("Best: " + fits.ToString());
Console.Write(" Average: " + avg / avglist.Count);
Console.Write(" Generation: " + gennumber);
Console.WriteLine("");
// Create a matting pool of the genes proportional to there fitness
Proportionalpopulationpool = new List <Gene>();
Addingaweightedamountoftimes();
// Use fitness selection
populationpool = new List <Gene>();
for (int i = 0; i < populationsize; i++)
{
Gene parent1 = new Gene();
Gene parent2 = new Gene();
// Tournament selection FIGHTERSSSSS READY
Tournament(ref parent1, ref parent2);
// split the 2 parents and add them together
List <double> weights1 = new List <double>();
List <double> weights2 = new List <double>();
List <double> kidsweights = new List <double>();
Splitandadd(parent1, parent2, out weights1, out weights2, kidsweights);
// mutation chance of each weight to change and become diffrent
int chance = _rand.Next(0, 100);
if (chance < mutationchance)
{
int tochange = _rand.Next(0, 60);
kidsweights[tochange] = ((_rand.NextDouble() * 2) - 1);
}
// add and repeat untill new population = population size
Gene gene = new Gene();
int crossover = _rand.Next(0, 100);
if (crossover < crossoverchance)
{
gene.ChangeWeights(kidsweights);
}
else
{
gene = parent1;
}
populationpool.Add(gene);
}
}
// creating a textfile of the best weights
using (StreamWriter sw = File.CreateText(path))
{
foreach (double num in textwights)
{
sw.WriteLine(num);
}
}
bestG.ChangeWeights(textwights);
}