public SimpleNNMaker(AITrainableGame game)
{
bitsPerWeight = game.BitsPerWeight();
inputs = game.NumInputs();
hidden = game.NumHidden();
outputs = game.NumOutputs();
bitsPerBias = game.BitsPerBias();
chromosomeLength = (inputs * hidden + hidden * outputs) * bitsPerWeight + (hidden + outputs) * bitsPerBias;
}
public Simulation(AITrainableGame game, int populationSize = 100, double crossOverBredAmount = 0.5, double mutateAfterCrossoverAmount = 0.1,
double mutationRate = 0.05, int allowSinglePointCrossover = 1, int allowTwoPointCrossover = 1, int allowUniformCrossover = 1,
int offspringSelectionPolicy = 0, double initialMutation = 0.0, double initialSimilarity = 0.0, int mainDiversityMeasure = 0)
{
PopulationSize = populationSize;
CrossoverBredAmount = crossOverBredAmount;
MutateAfterCrossoverAmount = mutateAfterCrossoverAmount;
MutationRate = mutationRate;
AllowSinglePointCrossover = allowSinglePointCrossover == 1 ? true : false;
AllowTwoPointCrossover = allowTwoPointCrossover == 1 ? true : false;
AllowUniformCrossover = allowUniformCrossover == 1 ? true : false;
ReplacementRule = offspringSelectionPolicy;
InitialMutation = initialMutation;
InitialSimilarity = initialSimilarity;
DiversityMeasure = mainDiversityMeasure;
allowedCrossoverMethods = new List <CrossoverMethod>();
if (AllowSinglePointCrossover)
{
allowedCrossoverMethods.Add(new SinglePointCrossover());
}
if (AllowTwoPointCrossover)
{
allowedCrossoverMethods.Add(new TwoPointCrossover());
}
if (AllowUniformCrossover)
{
allowedCrossoverMethods.Add(new UniformCrossover());
}
switch (ReplacementRule)
{
case 0: offspringMerger = new NaiveReplacementRule(); break;
case 1: offspringMerger = new AncestorElitismNoExtinctionReplacementRule(); break;
case 2: offspringMerger = new AncestorElitismReplacementRule(); break;
case 3: offspringMerger = new SingleParentElitismReplacementRule(); break;
case 4: offspringMerger = new ExploreExploitT30ReplacementRule(); break;
case 5: offspringMerger = new ExploreExploitT20ReplacementRule(); break;
case 6: offspringMerger = new ExploreExploitT40ReplacementRule(); break;
case 7: offspringMerger = new ExploreExploitB30ReplacementRule(); break;
default: throw new Exception("Wrong offspring merge type: " + ReplacementRule);
}
diversityMeasure = diversityMeasures[0];
Game = game;
NeuralNetworkMaker = new SimpleNNMaker(game);
}
/// <summary>
/// Calculates the fitness value of each individual threaded
/// </summary>
/// <param name="offspring"></param>
private void CalcFitnessValuesThreaded(List <AIPlayer> list)
{
Task[] tasks = new Task[list.Count];
for (int i = 0; i < list.Count; i++)
{
int index = i;
AITrainableGame game = Simulation.Game;
tasks[index] = Task.Factory.StartNew(() => { list[index].CalcFitness(game); });
}
Task.WaitAll(tasks);
}
/// <summary>
/// Calculates the fitness of the AIPlayer based on the given game.
/// Call GetFitness() retrieve the fitness value afterwards.
/// </summary>
/// <param name="game"></param>
public void CalcFitness(AITrainableGame game)
{
fitness = game.CalcFitness(this);
}
private void purchaseRadioButton_CheckedChanged(object sender, EventArgs e)
{
game = new Purchase();
GameChanged();
}
private void XORRadioButton_CheckedChanged(object sender, EventArgs e)
{
game = new XOR();
GameChanged();
}
private void rosenbrockRadioButton_CheckedChanged(object sender, EventArgs e)
{
game = new Rosenbrock();
GameChanged();
}
public Form1()
{
InitializeComponent();
game = new SnakeGame();
}
private void bankruptcyRadioButton_CheckedChanged(object sender, EventArgs e)
{
game = new Bankruptcy();
GameChanged();
}
private void leafRadioButton_CheckedChanged(object sender, EventArgs e)
{
game = new Leaf();
GameChanged();
}
private void IrisRadioButton_Click(object sender, EventArgs e)
{
game = new Iris();
GameChanged();
}
private void SnakeRadioButton_CheckedChanged(object sender, EventArgs e)
{
game = new SnakeGame();
GameChanged();
}
private void FallingStarsRadioButton_CheckedChanged(object sender, EventArgs e)
{
game = new FallingStarsGame();
GameChanged();
}