private void evaluatePopulation()
{
evaluator.NewGeneration();
for (int i = 0; i < workers.Length; i++)
{
workers[i].setGenomeList(genomeList);
threads[i] = new Thread(new ThreadStart(workers[i].DoWork));
threads[i].Start();
}
EvaluationWorker mainWorker = new EvaluationWorker(start, genomeList.Count, genomeList, evaluator);
mainWorker.DoWork();
for (int i = 0; i < threads.Length; i++)
{
threads[i].Join();
}
updateStats();
}
private void createPopulation()
{
genomeList = new List <QEAGenome>();
int layers = LEEAParams.DOMAIN.getHiddenLayers() + 1; // layers needs to be the number of layers of connections
int[] layerNodes = new int[layers + 1];
layerNodes[0] = LEEAParams.DOMAIN.getInputs() + 1; // +1 for the bias!
for (int i = 0; i < layers - 1; i++)
{
layerNodes[i + 1] = LEEAParams.DOMAIN.getHiddenLayerNeurons(i + 1);
}
layerNodes[layers] = LEEAParams.DOMAIN.getOutputs();
Parallel.For(0, LEEAParams.POPSIZE, po, i =>
{
QEAGenome genome = new QEAGenome(layers, layerNodes, 1);
lock (genomeList)
{
genomeList.Add(genome);
}
});
// set up our worker threads
int end;
for (int i = 0; i < workers.Length; i++)
{
end = start + genomeList.Count / LEEAParams.THREADS;
workers[i] = new EvaluationWorker(start, end, genomeList, evaluator);
start = end;
}
// speciate the newly created folks
speciatePopulation();
}
