public bool Iterate()
{
try
{
// Initial cost and sort
if (g == 0)
{
foreach (NN nn in population)
{
nn.cost = cost(nn);
}
population = population.OrderBy(p => p.cost).ToArray();
}
// Copy non-elite networks and mutate them.
for (int i = 1; i < population.Length; i++)
{
if (i >= elites)
{
population[i] = NNHelper.CopyNN(population[rnd.Next(0, elites - 1)]);
NNHelper.MutateWeights(population[i], rnd, minMutation, maxMutation);
}
}
// Cost and sort
foreach (NN nn in population)
{
nn.cost = cost(nn);
}
population = population.OrderBy(p => p.cost).ToArray();
// Increment generation
g++;
return(true);
}
catch (Exception ex)
{
Debug.WriteLine(ex.Message);
return(false);
}
}
public GA(int populationSize, Func <NN, double> costFunction, int inputCount, int outputCount, NN.ActivationFunction activationFunctionHiddenLayers, NN.ActivationFunction activationFunctionOutputLayer, int minHiddenLayers, int maxHiddenLayers, int minHiddenNodes, int maxHiddenNodes, double weightMinValue, double weightMaxValue, double biasMinValue, double biasMaxValue, int elitesToCopy, double minMutationPercentage, double maxMutationPercentage)
{
population = new NN[populationSize];
cost = costFunction;
elites = elitesToCopy;
minMutation = minMutationPercentage;
maxMutation = maxMutationPercentage;
// For each new unit to be created
for (int i = 0; i < populationSize; i++)
{
// Specify input, hidden, and output layer node counts
int[] nodeCounts = new int[2 + rnd.Next(minHiddenLayers, maxHiddenLayers + 1)];
nodeCounts[0] = inputCount;
for (int j = 1; j < nodeCounts.Length - 1; j++)
{
nodeCounts[j] = rnd.Next(minHiddenNodes, maxHiddenNodes + 1);
}
nodeCounts[nodeCounts.Length - 1] = outputCount;
// Create the neural network
NN.ActivationFunction[] functions = new NN.ActivationFunction[nodeCounts.Length - 1];
for (int j = 0; j < nodeCounts.Length - 2; j++)
{
functions[j] = activationFunctionHiddenLayers;
}
population[i] = new NN(nodeCounts, functions, activationFunctionOutputLayer);
// Initialize weights
bool success = NNHelper.RandomizeWeights(population[i], rnd, weightMinValue, weightMaxValue);
}
}