private void CreateNextGeneration()
{
List <ClimberGenetics> newPopulation = new List <ClimberGenetics>(PopulationSize);
List <ClimberGenetics> sourcePopulation = new List <ClimberGenetics>(population);
RouletteWheel rouletteWheel;
float averageFitness = 0f;
float highestGenFitness = 0f;
// Calculate highest and average fitness
foreach (ClimberGenetics genetics in population)
{
averageFitness += genetics.FitnessScore;
highestGenFitness = Mathf.Max(highestGenFitness, genetics.FitnessScore);
highestFitness = Mathf.Max(highestFitness, genetics.FitnessScore);
}
averageFitness /= population.Count;
// Sort based on fitness (most to least fit)
sourcePopulation.Sort((a, b) => { return(a.FitnessScore > b.FitnessScore ? -1 : 1); });
// Before mating... KILL THE WEAK!@$
for (int i = 0; i < NumToKill; i++)
{
ClimberGenetics deadbeat = sourcePopulation[sourcePopulation.Count - 1];
Logger.Add("Killing individual with a score of: " + deadbeat.FitnessScore.ToString("0.00"));
sourcePopulation.Remove(deadbeat);
}
rouletteWheel = new RouletteWheel(sourcePopulation);
// Add survivors to new population
newPopulation.AddRange(sourcePopulation);
// Let's fuuuuu
for (int i = 0; i < NumToKill; i++)
{
ClimberGenetics parentA = sourcePopulation[i];
ClimberGenetics parentB = null;
ClimberGenetics child = null;
// Choose a partner (making sure it's not self)
while (parentB == null || parentB == parentA)
{
parentB = rouletteWheel.GetResult();
}
// Crossover genes from parents to child
if (Random.Range(0f, 1f) < CrossoverRate)
{
int maxCrossoverIndex = Mathf.Min(parentA.Chromosome.Count, parentB.Chromosome.Count) - 1;
int crossoverIndex = Random.Range(0, maxCrossoverIndex + 1);
int maxCount = Mathf.Max(parentA.Chromosome.Count, parentB.Chromosome.Count);
child = new ClimberGenetics();
for (int j = 0; j < maxCount; j++)
{
if (j < crossoverIndex)
{
child.Chromosome.Add(parentA.Chromosome[j]);
}
else
{
if (j < parentB.Chromosome.Count)
{
child.Chromosome.Add(parentB.Chromosome[j]);
}
}
}
}
else
{
child = parentA.Copy();
}
// Mutate genes
foreach (Gene gene in child.Chromosome)
{
if (Random.Range(0f, 1f) < MutationRate)
{
MutateGene(child, gene);
}
}
newPopulation.Add(child);
}
Logger.Add("Average fitness for generation " + currentGeneration + ": " + averageFitness.ToString("0.00"));
Logger.Add("Highest fitness for generation " + currentGeneration + ": " + highestGenFitness.ToString("0.00"));
Logger.Add("Highest all-time fitness: " + highestFitness.ToString("0.00"));
currentGeneration++;
population = newPopulation;
Logger.Add("Created new genetic information for generation " + currentGeneration);
}
