public Population Select(int n)
{
_controllers.Sort();
Controller worst = _controllers.First();
List <Controller> newList = new List <Controller>(_controllers.Count);
for (int i = 0; i < _controllers.Count; i++)
{
List <Controller> challengeList = new List <Controller>(n);
for (int j = 0; j < n; j++)
{
challengeList.Add(_controllers[random.Next(_controllers.Count)]);
}
challengeList.Sort();
Controller best = challengeList.Last();
NeuralNetwork newNetwork = NNFactory.CreateElmanNN(best.Simulation.SensorStates.Count, 2);
newNetwork.SetAllWeights(best.NeuralNetwork.GetAllWeights());
if (gaMode == GA_CONST_START)
{
newList.Add(new Controller(newNetwork));
}
else if (gaMode == GA_HYBRID)
{
newList.Add(new Controller(newNetwork, worst.Simulation));
}
}
return(new Population(newList, iteration + 1)
{
gaMode = gaMode
});
}
public Population(int popSize, int _gaMode)
{
_controllers = new List <Controller>(popSize);
gaMode = _gaMode;
//_simulation = Simulation.CloneDefault();
int inputCount = Simulation.CloneDefault().SensorStates.Count;
for (int i = 0; i < popSize; i++)
{
NeuralNetwork network = NNFactory.CreateElmanNN(inputCount, 2);
network.RandomizeWeights(-0.5, 0.5);
_controllers.Add(new Controller(network));
}
}
