DNA GenerateNewDNA()
{
currentDNA.CalculateFitness(transform.position, playerLastKnownPosition);
List <DNA> dna = AI.dnaLegacy[currentDegRegion, currentRangeRegion];
// Remove less fit dna
if (dna.Count == size)
{
int lessFitIndex = 0;
float minFitness = 1000000f;
for (int i = 0; i < dna.Count; i++)
{
if (minFitness > dna[i].fitness)
{
lessFitIndex = i;
minFitness = dna[i].fitness;
}
}
if (currentDNA.fitness > lessFitIndex)
{
dna[lessFitIndex] = currentDNA;
}
}
else
{
dna.Add(currentDNA);
}
AI.dnaLegacy[currentDegRegion, currentRangeRegion] = dna;
FindRegion();
dna = AI.dnaLegacy[currentDegRegion, currentRangeRegion];
// Mating
List <DNA> matingPool = new List <DNA>();
for (int i = 0; i < dna.Count; i++)
{
for (int j = 0; j < dna[i].fitness; j++)
{
matingPool.Add(dna[i]);
}
}
float mutationRate = 0.1f;
float prob = Random.Range(0.0f, 1.0f);
Debug.Log("Prob: " + prob);
if (matingPool.Count >= 2 && prob > mutationRate)
{
int indexA = Random.Range(0, matingPool.Count - 1);
DNA parentA = matingPool[indexA];
int indexB = Random.Range(0, matingPool.Count - 1);
DNA parentB = matingPool[indexB];
Debug.Log("Before crossover");
return(Crossover(parentA, parentB));
}
else
{
return(new DNA(transform.position, playerLastKnownPosition));
}
}
// Generates new population after the individuals in the first population is setup
public void NewGeneration()
{
if (population.Count <= 0)
{
return;
}
// Calculates the total sum of the fitness in the population to be used when choosing a parent
CalculatePopulationFitness();
// Sorts the population from highest fitness to lowest
population.Sort(compareDNA);
newPopulation.Clear();
//Debug.Log(population[0].fitness);
// Iterates through the population and either adds individuals through elitism or the result of crossover
for (int i = 0; i < population.Count; i++)
{
// Takes x amount of the fittest individuals and directly adds them to the new population
if (i < elitism)
{
newPopulation.Add(population[i]);
}
else
{
// Chooses two parents from the population by having their fitness divided by the fitness sum to create their probability
DNA parent1 = ChooseParent();
DNA parent2 = ChooseParent();
// A child of the two parents are made through crossover so there is a 50/50 split between the genes of the parents
DNA child = parent1.Crossover(parent2);
// Mutation is applied with a 1% to all genes to change by a small amount and is necessary to introduce variance
child.Mutation();
// The fitness of the child is calculated based on the target DNA
child.fitness = child.CalculateFitness(gameManager.targetDNA);
// Data analyzer all new fitness
// bestFitness.Add(child.fitness);
// The child is added to the new generation
newPopulation.Add(child);
}
}
// New generation replaces old generation
List <DNA> tempList = population;
population = newPopulation;
newPopulation = tempList;
// Generation counter is incremented
generation++;
}
public void GenerationsPassed()
{
if (initialize)
{
gameManager.SetTargetDNA();
for (int i = 0; i < populationSize; i++)
{
DNA dna = new DNA();
dna.RandomDNA();
dna.CalculateFitness(gameManager.targetDNA);
population.Add(dna);
}
population.Sort(compareDNA);
population[0].fitness = Mathf.Round(population[0].fitness);
// Add best gene of first generation
bestGenes.Add(population[0]);
generation++;
for (int i = 0; i < generationsPassed - 1; i++)
{
NewGeneration();
population.Sort(compareDNA);
// Add best gene of first generation
bestGenes.Add(population[0]);
}
saveManager.Save(population[0], generation);
saveManager.LoadSave();
initialize = false;
}
else
{
gameManager.SetTargetDNA();
for (int i = 0; i < generationsPassed; i++)
{
NewGeneration();
population.Sort(compareDNA);
// Add best gene of first generation
bestGenes.Add(population[0]);
}
// GenerationCheck();
saveManager.Save(population[0], generation);
saveManager.LoadSave();
}
//for (int i = 0; i < bestGenes.Count; i++)
//{
// Debug.Log(bestGenes[i].fitness);
//}
}
