public IGeneticIndividual[] Reproduce(IGeneticIndividual[] IParents, int numCrossoverPoints, int numChildren)
{
int numParents = IParents.Length;//convenient variable to have
//convert array of type IGeneticIndividual to type TestGeneticIndividual
TestGeneticIndividual[] parents = new TestGeneticIndividual[numParents];
for (int i = 0; i < numParents; i++)
{
parents[i] = IParents[i] as TestGeneticIndividual;
}
TestGeneticIndividual[] children = new TestGeneticIndividual[numChildren]; //variable to hold generated children. Will be output by method
for (int childIter = 0; childIter < numChildren; childIter++) //iterate once for each child to be generated
{
int[] crossoverPoints = new int[numCrossoverPoints];
//fill crossoverPoints array with random ints which are indexes of genes array
for (int i = 0; i < crossoverPoints.Length; i++)
{
crossoverPoints[i] = (int)(RandHolder.NextDouble() * parents[0].genes.Length);
}
int activeParentIndex = 0;
//generate child
children[childIter] = new TestGeneticIndividual(parents[0].genes.Length);
for (int i = 0; i < parents[0].genes.Length; i++)
{
children[childIter].genes[i] = parents[activeParentIndex].genes[i];
for (int iter = 0; iter < numCrossoverPoints; iter++)
{
if (i == crossoverPoints[iter])
{
int temp = (int)(RandHolder.NextDouble() * (parents.Length - 1));//minus one for value exclusion, so between first and second to last index
if (temp != activeParentIndex)
{
activeParentIndex = temp;
}
else
{
activeParentIndex = parents.Length - 1;//minus one because it is max index of array
}
}
}
}
}
//convert array of type TestGeneticIndividual to type IGeneticIndividual
IGeneticIndividual[] IChildren = new IGeneticIndividual[numChildren];
for (int i = 0; i < numChildren; i++)
{
IChildren[i] = children[i] as IGeneticIndividual;
}
return(IChildren);
}
// Update is called once per frame
void Update()
{
float secondsPerGeneration = 1 / numGenerationsPerSecond;
if (Time.time > nextGenerationTime)
{
if (ga != null)
{
ga.TrainGeneration(1);
TestGeneticIndividual bestIndividual = (TestGeneticIndividual)ga.individuals[0];
TestGeneticIndividual worstIndividual = (TestGeneticIndividual)ga.individuals[populationSize - 1];
//debug
plotBest.AddKey(Time.realtimeSinceStartup, (float)bestIndividual.Fitness());
plotWorst.AddKey(Time.realtimeSinceStartup, (float)worstIndividual.Fitness());
nextGenerationTime += secondsPerGeneration;
}
}
}
// Use this for initialization
void Start()
{
TestGeneticIndividual progenitor = new TestGeneticIndividual(testIndividualGeneSize);
ga = new GeneticAlgorithm(progenitor, populationSize, numParents, environmentalPressure, eliteFraction, numCrossoverPoints, mutationChance, tournamentSize);
}
