public static float[] getEvolved(IWeapon parent, int efficiencyPercentage)
{
ArrayList population = getRandomPopulation(parent, 1f);
float[] behaviorValues = parent.BehaviorTracker.getNormalizedBehavior();
int maxIndex = 0;
for (int i = 1; i < behaviorValues.Length; i++)
if (Math.Abs(behaviorValues[i] - 50) > Math.Abs(behaviorValues[maxIndex] - 50))
maxIndex = i;
int[] factorArray = getFactorArray(maxIndex);
float[] bestIndividual = (float[])population[0];
float bestFitness = -1;
foreach (float[] individual in population)
{
float fitness = fitnessFunction(factorArray, individual);
if (fitness > bestFitness)
{
bestIndividual = individual;
bestFitness = fitness;
}
}
return bestIndividual;
}
private static ArrayList getRandomPopulation(IWeapon parent, float efficiencyPercentage)
{
ArrayList population = new ArrayList();
float[] parentCharacteristics = parent.getCharacteristics();
float[] parentWeaponBias = parent.getWeaponBias();
for (int i = 0; i < populationSize; i++)
{
float[] characteristics = new float[(int)IWeapon.Stats.END];
for (int j = 0; j < (int)IWeapon.Stats.END; j++)
{
characteristics[j] = parentCharacteristics[j]
+ (float)random.NextDouble() * parentWeaponBias[j] * efficiencyPercentage;
}
population.Add(characteristics);
}
return population;
}
public static float[] getRandomIndividual(IWeapon parent, int efficiencyPercentage)
{
ArrayList population = getRandomPopulation(parent, (efficiencyPercentage / 100f));
return (float[])population[random.Next(population.Count)];
}
public float getCharacteristic(IWeapon.Stats stat)
{
return characteristics[(int)stat];
}