public void NewGeneration(int newDNACount = 0, bool crossoverNewDNA = false)
{
// amount of DNA to hold in the new population
int finalCount = _population.Count + newDNACount;
// if there are no DNA to be generated then return
if (finalCount <= 0)
{
return;
}
// if there is an existing population
if (_population.Count > 0)
{
// recalculate the fitness rating
CalculateFitness();
// sort the by best fitness rating
_population.Sort(CompareDNA);
}
_newPopulation.Clear();
// generate a new population
for (int i = 0; i < finalCount; i++) // finalCount or _population.Count
{
// elitism
if (i < _elitism && i < _population.Count)
{
_newPopulation.Add(_population [i]);
}
else if (i < _population.Count || crossoverNewDNA == true)
{
// splice the DNA of the best parents
AIDNA <T> parentA = ChooseParent();
AIDNA <T> parentB = ChooseParent();
// splice
AIDNA <T> child = parentA.Splice(parentB);
// change to mutate
child.Mutate(_mutationRate);
_newPopulation.Add(child);
}
else
{
// add a new DNA to the pool
_newPopulation.Add(new AIDNA <T> (_DNACount, _rand, _getRandomGene, _fitnessFunction, true));
}
}
// keep track of old and new population
List <AIDNA <T> > tempList = _population;
_population = _newPopulation;
_newPopulation = tempList;
_currGeneration++;
}
private void SpawnWave()
{
if (_genticAlgorithm.bestFitness == 1.0f)
{
Debug.Log("!!!!!BEST DNA REACHED!!!!!!!");
}
else
{
// generate a new generation of DNA
_genticAlgorithm.NewGeneration();
currentGeneration++;
// update debug representation
debugPopulation.Clear();
for (int i = 0; i < _genticAlgorithm.population.Count; i++)
{
debugPopulation.Add(_genticAlgorithm.population[i].genes[0]);
}
}
// clear the population ready to spawn in new enemies
_AIPopulation.Clear();
// keep track of spawn in locations so that there is no spawning
// on a node that already contains an enemy
List <Node> spawnedInNodes = new List <Node> ();
for (int i = 0; i < _genticAlgorithm.population.Count; i++)
{
// get the traits from the population
AIDNA <EvolutionTraits> traits = _genticAlgorithm.population [i];
// spawn in the enemy using the prefab
GameObject newAI = Instantiate(_enemyPrefab);
// apply to speed and damage from the traits/genes
newAI.GetComponent <SimpleEnemyState> ().damage = Mathf.RoundToInt(traits.genes [0].muscleArm);
newAI.GetComponent <SimpleEnemyState> ().speed = traits.genes [0].muscleLeg;
// spawn enemy at an adjacent tile from the Boss (this.transform)
List <Node> neighbours = _Grid.GetNeighbours(_Grid.NodeFromWorldPoint(this.transform.position));
foreach (Node node in neighbours)
{
// node must be walkable and not have been spawned on by another enemy
if (node.walkable == true && spawnedInNodes.Contains(node) == false)
{
// update position
newAI.transform.position = new Vector3(node.worldPosition.x, 1.0f, node.worldPosition.z);
_AIPopulation.Add(newAI);
spawnedInNodes.Add(node);
break;
}
}
}
}
// Calculates the fitness rating for a gene in the population
private float FitnessFunction(int index)
{
float score = 0.0f;
// score between 0 and 100%
AIDNA <EvolutionTraits> dna = _genticAlgorithm.population[index];
//one strong enemy is just as effective as many weak but fast enemies.
// for this reason both the strengths are taken into one equation equally.
score += dna.genes [0].muscleArm / _maxArm;
score += dna.genes [0].muscleLeg / _maxLeg;
score /= 2.0f; // as it is out of 1
return(score);
}
// Better fitness rating means better DNA
public int CompareDNA(AIDNA <T> a, AIDNA <T> b)
{
if (a.fitnessRating > b.fitnessRating)
{
return(-1);
}
else if (a.fitnessRating < b.fitnessRating)
{
return(1);
}
else
{
return(0);
}
}
public AIDNA <T> Splice(AIDNA <T> otherParent)
{
AIDNA <T> child = new AIDNA <T> (_genes.Length, _rand, _getRandomGene, _fitnessFunction, false);
// splice choosing the next gene from a either of the parents
// (50% chance of parent A, 50% chance of parent B)
for (int i = 0; i < _genes.Length; i++)
{
if (_rand.NextDouble() < 0.5)
{
child._genes [i] = _genes[i];
}
else
{
child._genes [i] = otherParent._genes [i];
}
}
return(child);
}
public void CalculateFitness()
{
_fitnessTotal = 0;
// find the best DNA in the population
AIDNA <T> best = _population [0];
for (int i = 0; i < _population.Count; i++)
{
_fitnessTotal += _population [i].CalculateFitness(i);
if (_population [i].fitnessRating > best.fitnessRating)
{
best = _population [i];
}
}
// store best fitness rating and its genes
_bestFitness = best.fitnessRating;
best.genes.CopyTo(_bestGenes, 0);
}
