public MainForm()
{
InitializeComponent();
//Initialize a test network
testNN = new NN(4, 2, 1, new int[] { 6 });
//Build a test population
pop = new NNPopulation(Params.simulationPopulationSize, Params.neuralNetworkStructure);
//Output the default object test results
richTextBox_simpleOut.Text = getTestDataString();
//Fill the simThread with a default value (not running)
simThread = new Thread(new ThreadStart(runGeneticSimulation));
//No stop is request at the beginning of execution
requestStop = false;
label_totalIterations.Text = "0 Total Iterations, Population Size: " + pop.StartPopulationSize;
comboBox_BreedingType.SelectedIndex = 1;
numIterations = 0;
avgScoreList = new List<double>();
movAvgScoreList = new List<double>();
maxScoreList = new List<double>();
movMaxScoreList = new List<double>();
Series mAvgSeries = new Series("Average Moving Average");
Series avgSeries = new Series("Average");
Series mMaxSeries = new Series("Max Moving Average");
Series maxSeries = new Series("Max");
mAvgSeries.ChartType = SeriesChartType.Spline;
avgSeries.ChartType = SeriesChartType.Line;
mMaxSeries.ChartType = SeriesChartType.Spline;
maxSeries.ChartType = SeriesChartType.Line;
chart.Series.Add(mAvgSeries);
chart.Series.Add(avgSeries);
chart.Series.Add(mMaxSeries);
chart.Series.Add(maxSeries);
}
public List<NN> BreedPickEach(Tuple<int, int>[] couples, KeyValuePair<int, double>[] sortedFitnessList)
{
//Create a list of new neural networks for the next generation
List<NN> newPopulation = new List<NN>();
//Place the elites in the population
for (int i = 0; i < Params.numberOfElites; i++)
for (int j = 0; j < Params.numOfCopiesOfElites; j++)
newPopulation.Add(population[sortedFitnessList[i].Key]);
//Filling the remainder of the population
int k = 0;
while (newPopulation.Count < population.Count)
{
//If we randomly are above the crossover rate
if (Util.randNumGen.NextDouble() > Params.crossoverRate)
{
//Add the first parent
newPopulation.Add(population[couples[k].Item1]);
//If we have room for another add the second parent
if (!(newPopulation.Count <= population.Count))
newPopulation.Add(population[couples[k].Item2]);
}
//If we are below the crossover rate
else
{
//Create a new neural network with the same structure as the rest of the population
NN child1 = new NN(networkStructure[0],
networkStructure[networkStructure.Length - 1],
hiddenLayerStructure.Length,
hiddenLayerStructure);
//Create a new neural network with the same structure as the rest of the population
NN child2 = new NN(networkStructure[0],
networkStructure[networkStructure.Length - 1],
hiddenLayerStructure.Length,
hiddenLayerStructure);
//Add all genes from each parent up to the crossover point to the two different children
int i;
for (i = 0; i < population[k].Weights.Length; i++)
{
//Randomly stitch one of the parents genes onto each child
if (Util.randNumGen.NextDouble() > 0.5)
{
child1.Weights[i] = population[couples[k].Item1].Weights[i];
child2.Weights[i] = population[couples[k].Item2].Weights[i];
}
else
{
child1.Weights[i] = population[couples[k].Item2].Weights[i];
child2.Weights[i] = population[couples[k].Item1].Weights[i];
}
}
//Add the first child to the new population
newPopulation.Add(child1);
//If we have room for more, add the second child to the population
if (!(newPopulation.Count <= population.Count))
newPopulation.Add(child2);
}
//Confirm that the iteration variable does not exceed the array size from which it is pulling values
k = (k + 1) % couples.Length;
}
//Return the new population
return newPopulation;
}