private void initDNA(bool[] booleanDNA)
{
float initialGene, a, b, var;
bool[] bits;
int nVars = Fitness.nInputVars;
if (booleanDNA == null)
{
for (int i = 0; i < nVars; i++)
{
var = (float)GARandomNumberGenerator.getNextfloat();
a = (float)-Math.Pow(10, 1);
b = (float)Math.Pow(10, 1);
initialGene = (b - a) * var + a;
DNA[i] = initialGene;
//bits = float2bits(initialGene);
//for (int j = 0; j < 32; j++)
// DNA[i * 32 + j] = bits[j];
}
}
else
{
DNA = bits2floats(booleanDNA);
}
}
public void evolve()
{
int i, j;
//Individual[] children = new Individual[population.Length];
// all population should reproduce
Individual mom, dad, child1, child2;
bool[] momDNA, dadDNA, child1DNA, child2DNA;
for (i = 0; i <= population.Length - 1; i += 2)
{
mom = population[i]; // clone();
dad = population[i + 1]; // clone();
momDNA = mom.binaryDNA();
dadDNA = dad.binaryDNA();
////////////////////////// crossover and mutation
int crossoverPosition1, crossoverPosition2, temp;
while (true)
{
crossoverPosition1 = (int)(GARandomNumberGenerator.getNextfloat() * momDNA.Length);
crossoverPosition2 = (int)(GARandomNumberGenerator.getNextfloat() * momDNA.Length);
if (crossoverPosition1 != crossoverPosition2)
{
break;
}
}
if (crossoverPosition1 > crossoverPosition2)
{
temp = crossoverPosition2;
crossoverPosition2 = crossoverPosition1;
crossoverPosition1 = temp;
}
child1DNA = new bool[momDNA.Length];
child2DNA = new bool[momDNA.Length];
for (j = 0; j < momDNA.Length; j++)
{
// crossover
if (j >= crossoverPosition1 && j <= crossoverPosition2)
{
child1DNA[j] = dadDNA[j];
child2DNA[j] = momDNA[j];
}
else
{
child1DNA[j] = momDNA[j];
child2DNA[j] = dadDNA[j];
}
// mutation
if (GARandomNumberGenerator.getNextfloat() < options.MutationProbability)
{
child1DNA[j] = !child1DNA[j];
}
if (GARandomNumberGenerator.getNextfloat() < options.MutationProbability)
{
child2DNA[j] = !child2DNA[j];
}
}
child1 = new Individual(child1DNA);
child2 = new Individual(child2DNA);
//children[i] = child1;
//children[i+1] = child2;
population[i] = child1;
population[i + 1] = child2;
/////////////////////////////////////
}
// insert the children into the population except the best individual
//population = children;
population[bestIndex] = bestIndividual; // clone();
// scramble population;
int[] scrambledIndexes = new int[population.Length];
for (i = 0; i < population.Length; i++)
{
scrambledIndexes[i] = i;
}
scrambledIndexes = ShuffleArray(scrambledIndexes);
Individual[] p = new Individual[population.Length];
for (i = 0; i < population.Length; i++)
{
p[i] = population[scrambledIndexes[i]];
}
population = p;
return;
}
