private void improveGeneration()
{
// Selecting better solutions
Dictionary <NeuroNetwork, double> genomes_dictionary = new Dictionary <NeuroNetwork, double>();
for (int i = 0; i < genomes_count; i++)
{
genomes_dictionary.Add(genomes[i], fitness[i]);
}
genomes_dictionary = genomes_dictionary.OrderByDescending(key => key.Value).Take(solution_selection_for_crossover).ToDictionary(pair => pair.Key, pair => pair.Value);
// Create new genomes array
NeuroNetwork[] newGenomes = new NeuroNetwork[genomes_count];
// Keep original 2 better solutions
newGenomes[0] = genomes_dictionary.Keys.First();
newGenomes[1] = genomes_dictionary.Keys.Last();
// Cross-over solutions
NeuroNetwork[] hybrids = crossover(newGenomes[0], newGenomes[1]);
for (int i = 0; i < hybrids.Length; i++)
{
newGenomes[i + 2] = hybrids[i]; // Add crossed over solutions to new generation
}
// Mutated solutions
for (int i = hybrids.Length + 2; i < newGenomes.Length; i++)
{
int selection = random.Next(0, 4);
Perceptron[] old_ps = newGenomes[selection].getPerceptrons();
Perceptron[] new_ps = new Perceptron[old_ps.Length];
for (int o = 0; o < new_ps.Length; o++)
{
new_ps[o] = new Perceptron(old_ps[o].getWeights(), old_ps[o].getBias());
}
newGenomes[i] = new NeuroNetwork(neuro_input_nodes, neurons_per_layer, neuro_output_nodes, random, new_ps);
newGenomes[i].applyMutations(random);
}
// Replace old generation with new one
genomes = newGenomes;
}
private void createNetwork(sbyte[] genome)
{
perceptrons = new Perceptron[neurons_per_layer.Sum()];
n_values = new double[input_nodes + neurons_per_layer.Sum()];
// Indexes
int currLayer = 0, curr_perceptron_all_layers = 0, curr_perceptron_this_layer = 0;
for (int i = 0; i < genome.Length;)
{
if (curr_perceptron_this_layer > neurons_per_layer[currLayer] - 1)
{
curr_perceptron_this_layer = 0;
currLayer++;
}
int input_length;
if (currLayer == 0)
{
input_length = input_nodes;
}
else
{
input_length = neurons_per_layer[currLayer - 1];
}
// Set perceptron weights and bias
sbyte[] weights = new sbyte[input_length];
for (int w = 0; w < weights.Length; w++)
{
weights[w] = genome[i++];
}
sbyte bias = genome[i++];
/*if (currLayer == hidden_layers_count - 1) bias = 0;*/
perceptrons[curr_perceptron_all_layers] = new Perceptron(weights, bias);
curr_perceptron_this_layer++;
curr_perceptron_all_layers++;
}
}
