public EvaluationWorker(int start, int end, List <QEAGenome> genomeList, IDomainEvaluator evaluator)
{
this.start = start;
this.end = end;
this.genomeList = genomeList;
this.evaluator = evaluator;
}
public QuickEvolutionAlgorithm(IDomainEvaluator evaluator)
{
this.evaluator = evaluator;
r = new FastRandom();
po = new ParallelOptions();
po.MaxDegreeOfParallelism = LEEAParams.THREADS;
currentGeneration = 0;
createPopulation();
evaluatePopulation();
MUTATIONPOWER = LEEAParams.MUTATIONPOWER;
MUTATIONRATE = LEEAParams.MUTATIONRATE;
// calculate decay rate so that final mutation power is decayed properly
decayRate = Math.Pow((1 - LEEAParams.MUTATIONPOWERDECAY), 1.0f / LEEAParams.MAXGENERATIONS);
rateDecayRate = Math.Pow((1 - LEEAParams.MUTATIONRATEDECAY), 1.0f / LEEAParams.MAXGENERATIONS);
}
public BackpropAlgorithm()
{
LEEAParams.DOMAIN.generateSampleList();
evaluator = LEEAParams.DOMAIN.createEvaluator();
// initialize the network, use the QEAGenome class since it's already structured well for this task
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();
network = new QEAGenome(layers, layerNodes, LEEAParams.BPINITIALWEIGHTMAX);
box = new QuickBlackBox(network);
}
