public MelodySequence Generate()
{
NoteGene baseGene = new NoteGene(GPGeneType.Function);
baseGene.Function = NoteGene.FunctionTypes.Concatenation;
GPCustomTree tree = new GPCustomTree(baseGene);
if (base_seq != null)
{
tree.Generate(base_seq.ToArray());
tree.Mutate();
tree.Crossover(tree);
int length = base_seq.Length;
int depth = (int)Math.Ceiling(Math.Log(length, 2));
GPCustomTree.MaxInitialLevel = depth - 2;
GPCustomTree.MaxLevel = depth + 5;
}
var selection = new EliteSelection();
pop = new Population(30, tree, fitnessFunction, selection);
pop.AutoShuffling = true;
pop.CrossoverRate = 0.9;
pop.MutationRate = 0.1;
int percentage = MaxGenerations / 100;
for (int i = 0; i < MaxGenerations; i++)
{
if(i>percentage)
{
if (OnPercentage != null)
OnPercentage(this, i, pop.FitnessAvg);
percentage += MaxGenerations / 100;
}
pop.RunEpoch();
if ((int)(i) % 100 == 0)
Console.WriteLine(i / (float)MaxGenerations * 100 + "% : " + pop.FitnessAvg);
}
GPCustomTree best = pop.BestChromosome as GPCustomTree;
var notes = best.GenerateNotes();
return new MelodySequence(notes);
}
private void OnWorkerDoWorkEvolutionary(object sender, DoWorkEventArgs e)
{
ArrayList parameters = e.Argument as ArrayList;
Debug.Assert(parameters != null);
bool useRed = (bool)parameters[0], useGreen = (bool)parameters[1], useBlue = (bool)parameters[2];
List<int> slotSizes = (List<int>)parameters[3];
ProgramFitnessFunction fitnessFunction = new ProgramFitnessFunction(this.puzzle, slotSizes);
ISelectionMethod selectionMethod = new EliteSelection();
const int PopulationSize = 500;
Population population = new Population(
PopulationSize,
new ProgramChromosome(slotSizes.Sum(), slotSizes.Count, useRed, useGreen, useBlue),
fitnessFunction,
selectionMethod);
int iteration = 0;
population.MutationRate = 0.6;
population.CrossoverRate = 0.6;
while (true)
{
population.RunEpoch();
backgroundWorker.ReportProgress(++iteration, population.FitnessMax - 1);
if (backgroundWorker.CancellationPending)
{
e.Cancel = true;
return;
}
if (population.FitnessMax - 1 == this.puzzle.StarsCount)
{
e.Result = fitnessFunction.ChromosomeToProgram((ProgramChromosome)population.BestChromosome);
return;
}
}
}