private void buttonShowSimulation_Click(object sender, EventArgs e)
{
FlatlandFitnessEvaluator evaluator = (FlatlandFitnessEvaluator)eaLoop.FitnessEvaluator;
Board board = evaluator.boards[0];
board.ResetBoard();
SimulationForm simulationForm = new SimulationForm(board);
simulationForm.bestIndividual = eaLoop.best;
simulationForm.ShowDialog();
}
private void SetupProblem()
{
if (comboBoxProblem.SelectedIndex == ANN_INDEX)
{
// Setup ANN
// These settings are currently hardcoded
int numSensorNodes = 6;
int numMotorNodes = 3;
// Number of hidden layers
int numHiddenLayers = listBoxANN.Items.Count;
// The array containing the number of nodes for each hidden layer
int[] numNodesPerLayer = listBoxANN.Items.OfType <int>().ToArray();
// Setup the actication function (currently hardcoded)
//ActivationFunction activationFunction = new SigmoidActivation();
ActivationFunction activationFunction = new HyperbolicTangentActivation();
bool useBiasNode = checkBoxBiasNode.Checked;
ANN ann = new ANN(numSensorNodes, numMotorNodes, numHiddenLayers, numNodesPerLayer, activationFunction, useBiasNode);
int numWeights = ann.GetNumberOfWeights();
// Setup child population
int childCount = (int)numericChildCount.Value;
eaLoop.ChildCount = childCount;
// Setup genotype (TODO Remove hardcoding)
int numBitsPerWeight = (int)numericBitsPerWeight.Value;
int numBits = numBitsPerWeight * numWeights;
eaLoop.Genotype = new BinaryGenotype(numBits);
// Setup phenotype developer
BinaryToANNWeightsDeveloper developer = new BinaryToANNWeightsDeveloper();
developer.NumBitsPerWeight = numBitsPerWeight;
developer.NumWeights = numWeights;
eaLoop.PhenotypeDeveloper = developer;
// Setup fitness evaluator
FlatlandFitnessEvaluator evaluator = new FlatlandFitnessEvaluator();
evaluator.ann = ann;
evaluator.changeBoardBetweenGenerations = checkBoxDynamic.Checked;
evaluator.numBoards = (int)numericNumBoards.Value;
evaluator.dimensions = new int[] { 10, 10 };
evaluator.FPD = new float[] { 0.33f, 0.33f };
evaluator.timeSteps = 60;
eaLoop.FitnessEvaluator = evaluator;
// Set Genetic operator
eaLoop.GeneticOperator = new BinaryGeneticOperator();
eaLoop.GeneticOperator.MutationRate = (float)mutationNumeric.Value;
eaLoop.GeneticOperator.CrossoverRate = (float)crossoverNumeric.Value;
eaLoop.goal = int.MaxValue;
}
}
