// Select a genome using tournament.
protected virtual Genome TournamentSelect(List <Genome> population)
{
List <Genome> contestents = new List <Genome>();
// First, randomize number of contestents.
for (int i = 0; i < tourneyContestents; i++)
{
int randomIndex = UnityEngine.Random.Range(0, population.Count);
contestents.Add(population[randomIndex]);
}
Genome highestFit = contestents[0];
// Find highest fitness among contestents
foreach (Genome g in contestents)
{
if (g.Fitness > highestFit.Fitness)
{
highestFit = g;
}
}
// Create a copy of the best genome and return it.
Genome copy = highestFit.GenomeCopy();
copy.RootNode = StaticMethods.DeepCopy <N_Root>(highestFit.RootNode);
return(copy);
}
// Assign fitness values according to their performance in the simulation step
protected void Evaluate()
{
bestGenome = m_population[0];
// Assign fitness value for each genome based on its statistics.
foreach (Genome g in m_population)
{
g.Fitness = 100;
// If the genome won the match and it wasn't a draw (assuming 0 damage given is a draw)
// set fitness to 150.
if (g.WonLastMatch && g.DamageGiven > 0)
{
g.Fitness += 150;
}
g.Fitness += g.HealthRemaining;
g.Fitness += g.DamageGiven;
// Make the floor of all fitness values 0 to make the roulette selection valid.
if (g.Fitness < 0)
{
g.Fitness = 0;
}
// Find the genome with highest fitness, making sure that the simulations are always
// compared to the currently best genome.
if (g.Fitness > bestGenome.Fitness)
{
bestGenome.RootNode = StaticMethods.DeepCopy <N_Root>(g.RootNode);
bestGenome.Fitness = g.Fitness;
}
}
}
private Genome BinaryCrowdedTournamentSelect(List <Genome> genomes)
{
List <Genome> contestents = new List <Genome>();
// First, randomize number of contestents.
for (int i = 0; i < 2; i++)
{
int randomIndex = UnityEngine.Random.Range(0, genomes.Count);
contestents.Add(genomes[randomIndex]);
}
Genome highestFit = contestents[0];
// Find highest fitness among contestents
foreach (Genome g in contestents)
{
if (g.Fitness < highestFit.Fitness) // Lower rank/fitness is better in this case.
{
highestFit = g;
}
else if (g.Fitness == highestFit.Fitness) // If they belong to the same front
{
// In that case, choose based on crowding distance instead.
if (g.CrowdingDistance > highestFit.CrowdingDistance)
{
highestFit = g;
}
}
}
// Create a copy of the best genome and return it.
Genome copy = highestFit.GenomeCopy();
copy.RootNode = StaticMethods.DeepCopy <N_Root>(highestFit.RootNode);
return(copy);
}
// Combine parents and create two new children based on them
protected void Combine(out Genome child0, out Genome child1, Genome parent0, Genome parent1)
{
Node subtree0, subtree1 = null;
// First, check if there'll be any combination/crossover.
float random = UnityEngine.Random.Range(0.0f, 1.0f);
if (random < combinationRate)
{
List <Node> subtrees0 = TreeOperations.RetrieveSubtreeNodes(parent0.RootNode);
List <Node> subtrees1 = TreeOperations.RetrieveSubtreeNodes(parent1.RootNode);
// Select to random subtrees...
int randomIndex = UnityEngine.Random.Range(0, subtrees0.Count);
subtree0 = subtrees0[randomIndex];
randomIndex = UnityEngine.Random.Range(0, subtrees1.Count);
subtree1 = subtrees1[randomIndex];
// Swap subtrees between 0 and 1.
if (subtree0.Parent.GetType().IsSubclassOf(typeof(N_CompositionNode)))
{
N_CompositionNode comp0 = subtree0.Parent as N_CompositionNode;
comp0.ReplaceChild(subtree0, StaticMethods.DeepCopy <Node>(subtree1));
}
// Swap subtrees between 1 and 0.
if (subtree1.Parent.GetType().IsSubclassOf(typeof(N_CompositionNode)))
{
N_CompositionNode comp1 = subtree1.Parent as N_CompositionNode;
comp1.ReplaceChild(subtree1, StaticMethods.DeepCopy <Node>(subtree0));
}
#region Old Combination
//// Get initial comp of parent 0
//N_CompositionNode comp0 = parent0.RootNode.Child as N_CompositionNode;
//// Fetch a random subtree from the parent's subtrees
//int randomIndex = UnityEngine.Random.Range(0, comp0.GetChildren().Count);
//subtree0 = comp0.GetChildren()[randomIndex];
//// Get initial comp of parent 1
//N_CompositionNode comp1 = parent1.RootNode.Child as N_CompositionNode;
//// Fetch a random subtree from the parent's subtrees
//randomIndex = UnityEngine.Random.Range(0, comp1.GetChildren().Count);
//subtree1 = comp1.GetChildren()[randomIndex];
//// Swap subtrees between 0 and 1.
//if (subtree0.Parent.GetType().IsSubclassOf(typeof(N_CompositionNode)))
//{
// comp0.ReplaceChild(subtree0, StaticMethods.DeepCopy<Node>(subtree1));
//}
//// Swap subtrees between 1 and 0.
//if (subtree1.Parent.GetType().IsSubclassOf(typeof(N_CompositionNode)))
//{
// comp1.ReplaceChild(subtree1, StaticMethods.DeepCopy<Node>(subtree0));
//}
#endregion
}
// Regardless of combination, assign the children to be the parents. (combined or not)
child0 = parent0;
child1 = parent1;
}
// Select two candidates for next generation based on their fitness values
protected void RouletteSelect(out Genome parent0, out Genome parent1)
{
Dictionary <Genome, float> genomeToWeight = new Dictionary <Genome, float>();
parent0 = parent1 = null;
// Sum up the total weight of all fitness values.
float totalFitness = 0.0f;
foreach (Genome g in m_population)
{
totalFitness += (float)g.Fitness;
}
// Calculate the relative fotness for each genome
float fitnessSum = 0.0f;
foreach (Genome g in m_population)
{
float weight = fitnessSum + ((float)g.Fitness / totalFitness);
genomeToWeight.Add(g, weight);
fitnessSum += (g.Fitness / totalFitness);
}
// Select two parents using semi-randomized roulette selection.
for (int i = 0; i < 2; i++)
{
float random = UnityEngine.Random.Range(0.0f, 1.0f);
Genome selectedTree = null;
for (int j = 0; j < genomeToWeight.Count; j++)
{
var current = genomeToWeight.ElementAt(j);
if (j == 0)
{
if (random < current.Value)
{
selectedTree = current.Key;
}
}
else
{
var prev = genomeToWeight.ElementAt(j - 1);
if (random < current.Value && random > prev.Value)
{
selectedTree = current.Key;
}
}
}
if (selectedTree == null)
{
Debug.LogError("Selected tree is null...");
}
Genome parentG = selectedTree.GenomeCopy();
// Assign parents during different iterations.
switch (i) // Make sure to also copy conditions and subroots
{
case 0:
parent0 = parentG;
parent0.RootNode = StaticMethods.DeepCopy <N_Root>(selectedTree.RootNode);
break;
case 1:
parent1 = parentG;
parent1.RootNode = StaticMethods.DeepCopy <N_Root>(selectedTree.RootNode);
break;
default:
parent0 = new Genome();
parent1 = new Genome();
Debug.LogError("No parent was set in roulette");
break;
}
}
if (parent0 == null || parent1 == null)
{
Debug.LogError("Parents weren't assigned during roulette selection!");
}
}
// Update is called once per frame
public override IEnumerator Evolve()
{
// Start by randomly generating the initial population.
for (int i = 0; i < populationSize; i++)
{
m_population.Add(RandomGenome());
}
// Randomly generate the first best genome.
bestGenome = RandomGenome();
// First, process the initial population differently.
feedbackText.SetText("Generation " + 0 + " out of " + generations + "...");
Debug.Log("Starting simulation step of initial population...");
// Start simulation and wait until done.
simulationDone = false;
StartCoroutine(Simulate(m_population));
yield return(new WaitUntil(() => simulationDone));
Debug.Log("Simulation step of initial population complete!");
// Assign fitness to each genome according to nondomination principle.
SortFitnessByNondomination(m_population);
CalculateCrowdingDistance(m_population);
// Initial "next population" processing.
while (m_childPop.Count < m_population.Count)
{
Genome parent0, parent1;
parent0 = BinaryCrowdedTournamentSelect(m_population);
parent1 = BinaryCrowdedTournamentSelect(m_population);
// Combine parents to retrieve two children
Genome child0, child1;
Combine(out child0, out child1, parent0, parent1);
Mutate(child0.RootNode);
Mutate(child1.RootNode);
m_childPop.Add(child0);
m_childPop.Add(child1);
}
//Debug.Log("Evolving rest of generations...");
// Run general algorithm for remaining -th generations. (index 1 and forward
for (int i = 1; i < generations; i++)
{
feedbackText.SetText("Generation " + i + " out of " + generations + "...");
//Debug.Log("Starting simulation step of generation " + i);
// Start simulation and wait until done.
simulationDone = false;
StartCoroutine(Simulate(m_childPop));
yield return(new WaitUntil(() => simulationDone));
//Debug.Log("Simulation step complete!");
// First, create new generation as a combination of the last and the one before that.
List <Genome> combinedGenomes = new List <Genome>();
combinedGenomes.AddRange(m_population);
combinedGenomes.AddRange(m_childPop);
List <Genome> nextPopCandidates = new List <Genome>();
// Sort according to nondomination and return list of fronts
List <List <Genome> > fronts = GetFrontsByNondomination(combinedGenomes);
int frontIndex = 0;
// Create set of potential genome candidats
while (nextPopCandidates.Count + fronts[frontIndex].Count <= populationSize)
{
// Calculate crowding distance for the front and add it to the population.
CalculateCrowdingDistance(fronts[frontIndex]);
nextPopCandidates.AddRange(fronts[frontIndex]);
frontIndex++;
}
//Debug.Log(fronts[0].Count);
// Retrieve the best genome from front 0, used for simulation
Genome bestBack = BestFromFront(fronts[0]);
bestGenome = bestBack.GenomeCopy();
bestGenome.RootNode = StaticMethods.DeepCopy <N_Root>(bestBack.RootNode);
// Sort the front that didn't fit
SortCrowdedComparison(fronts[frontIndex]);
int fillingIndex = 0;
// Complete the population by adding from the sorted front
while (nextPopCandidates.Count < populationSize)
{
nextPopCandidates.Add(fronts[frontIndex][fillingIndex]);
fillingIndex++;
}
// Push child back to pop and create new child using nextpop made from child and pop
m_population = m_childPop;
m_childPop = new List <Genome>();
// Finally, create the childpop for next generation.
while (m_childPop.Count < populationSize)
{
Genome parent0, parent1;
parent0 = BinaryCrowdedTournamentSelect(nextPopCandidates);
parent1 = BinaryCrowdedTournamentSelect(nextPopCandidates);
// Combine parents to retrieve two children
Genome child0, child1;
Combine(out child0, out child1, parent0, parent1);
Mutate(child0.RootNode);
Mutate(child1.RootNode);
m_childPop.Add(child0);
m_childPop.Add(child1);
}
}
// Save the final best tree.
FileSaver.GetInstance().SaveTree(bestGenome.RootNode, "multiEvolved");
feedbackText.SetText("Multi evolution complete!");
buttonCanvas.SetActive(true);
yield return(null);
}
