private void Initialization()
{
if (Settings.IS_NEURO && File.Exists(Settings.DATA_FILENAME))
{
strategy = (Strategy)Network.Load(Settings.DATA_FILENAME);
NeuroEvaluateFunction = strategy.Compute;
enableNeuroSuccessful = true;
}
}
private void FindBestChromosome()
{
BestChromosome = population[0];
double fitnessMax = BestChromosome.Fitness;
for (int i = 1; i < size; i++)
{
double fitness = population[i].Fitness;
// check for max
if (fitness > fitnessMax)
{
fitnessMax = fitness;
BestChromosome = population[i];
}
}
}
public Strategy Clone()
{
DoubleArrayChromosome daChromosome = ToDoubleArrarChromosome();
Strategy s = new Strategy(activationFunction, inputsCount, neuronsCount);
s.SetWithDoubleArrayChromosome(daChromosome);
return s;
}
public GameAINeuroEvolutionaryLearning(int size)
{
population = new List<Strategy>();
this.size = size;
rand = new ThreadSafeRandom();
Strategy strategy = new Strategy(
//new SigmoidFunction(),
new LineFunction(),
Settings.MAX_CHESSES,
Settings.NETWORK_STRUCT);
population.Add(strategy);
for (int i = 0; i < size - 1; i++)
{
population.Add(strategy.CreateNew());
}
}
public void Crossover(Strategy strategy)
{
if (strategy != null)
{
DoubleArrayChromosome c1 = ToDoubleArrarChromosome();
DoubleArrayChromosome c2 = strategy.ToDoubleArrarChromosome();
c1.Crossover(c2);
SetWithDoubleArrayChromosome(c1);
}
else
{
throw new NullReferenceException();
}
}
