public bool Equals(AngryBirdsGen otherGen)
{
// if (ReferenceEquals(null, otherGen)) return false;
// if (ReferenceEquals(this, otherGen)) return true;
// the amount of birds must be the same
if(level.birdsAmount != otherGen.level.birdsAmount)
return false;
// the amount of stacks must be the same
if(level.GetTotalObjectsAmount() != otherGen.level.GetTotalObjectsAmount())
return false;
for(int i = 0; i < level.GetStacksAmount() && i < otherGen.level.GetStacksAmount(); i++)
{
// the height of each stack must be the same
if(level.GetStack(i).Count != otherGen.level.GetStack(i).Count)
return false;
if(level.GetStack(i).Count > 0)
{
LinkedListNode<ShiftABGameObject> obj1 = level.GetStack(i).First;
LinkedListNode<ShiftABGameObject> obj2 = otherGen.level.GetStack(i).First;
for(; obj1 != level.GetStack(i).Last.Next && obj2 != otherGen.level.GetStack(i).Last.Next; obj1 = obj1.Next, obj2 = obj2.Next)
{
if(!obj1.Value.Equals(obj2.Value))
return false;
}
}
}
return true;
}
private void SaveLog()
{
float fitness = Mathf.Infinity;
AngryBirdsGen genome = new AngryBirdsGen();
_geneticAlgorithm.GetBest(out genome, out fitness);
float experimentTime = Time.realtimeSinceStartup - _lastExperimentTime;
_logContent += "====== RESULTS ======\n";
_logContent += "Execution time: " + experimentTime + "\n";
_logContent += "Convergence: " + _generationIdx + "\n";
_logContent += "Cache size:" + _fitnessCache.Count + "\n";
_logContent += "Fitness calculations: " + _fitnessEvaluation + "\n";
_logContent += "Fitness recovered: " + _fitnessRecovered + "\n";
_logContent += "Best Fitness: " + fitness + "\n";
_logContent += "Linearity: " + genome.level.GetLevelLinearity() + "\n";
_logContent += "Density: " + genome.level.GetLevelDensity() + "\n";
_logContent += "Frequency pig: " + genome.level.GetABGameObjectFrequency(GameWorld.Instance.Templates.Length) + "\n";;
_logContent += "Frequency bird: " + genome.level.GetBirdsFrequency() + "\n";
_logContent += "Fitness Evolution: " + _genLog + "\n";
_logContent += "Cache Size Evolution: " + _cacheLog + "\n";
_logContent += "Cache Usage Evolution: " + _recoverLog + "\n";
_genLog = "";
_cacheLog = "";
_recoverLog = "";
_lastExperimentTime = Time.realtimeSinceStartup;
}
private void SaveGenerationLog()
{
float fitness = Mathf.Infinity;
AngryBirdsGen genome = new AngryBirdsGen();
_geneticAlgorithm.GetBest(out genome, out fitness);
_genLog += fitness + " ";
_cacheLog += _fitnessCache.Count + " ";
_recoverLog += _fitnessRecovered + " ";
//Debug.Log("Step : " + step++ + " Best Fitness: " + fitness);
//Debug.Log("Cache Size: " + _fitnessCache.Count);
//Debug.Log("Cache Usage: " + _fitnessRecovered);
}
public void InitAngryBirdsGenome(out AngryBirdsGen genome) {
genome = new AngryBirdsGen();
genome.level.birdsAmount = UnityEngine.Random.Range(0, ABLevel.BIRDS_MAX_AMOUNT);
genome.level = GenerateRandomLevel();
}
public void Crossover(ref Genome<AngryBirdsGen> genome1, ref Genome<AngryBirdsGen> genome2,
out Genome<AngryBirdsGen> child1, out Genome<AngryBirdsGen> child2) {
child1 = new Genome<AngryBirdsGen>();
child2 = new Genome<AngryBirdsGen>();
AngryBirdsGen genes1 = new AngryBirdsGen();
AngryBirdsGen genes2 = new AngryBirdsGen();
if(UnityEngine.Random.value <= _geneticAlgorithm.CrossoverRate)
{
int maxGenomeSize = Mathf.Max (genome1.Genes.level.GetStacksAmount(),
genome2.Genes.level.GetStacksAmount());
for(int i = 0; i < maxGenomeSize; i++)
{
if(genome1.Genes.level.GetStacksAmount() == genome2.Genes.level.GetStacksAmount())
{
if(UnityEngine.Random.value < 0.5f)
genes1.level.AddStack(CopyStack(genome1.Genes.level.GetStack(i)));
else
genes1.level.AddStack(CopyStack(genome2.Genes.level.GetStack(i)));
if(UnityEngine.Random.value < 0.5f)
genes2.level.AddStack(CopyStack(genome1.Genes.level.GetStack(i)));
else
genes2.level.AddStack(CopyStack(genome2.Genes.level.GetStack(i)));
}
else if(genome1.Genes.level.GetStacksAmount() < genome2.Genes.level.GetStacksAmount())
{
if(i < genome1.Genes.level.GetStacksAmount())
{
if(UnityEngine.Random.value < 0.5f)
genes1.level.AddStack(CopyStack(genome1.Genes.level.GetStack(i)));
else
genes1.level.AddStack(CopyStack(genome2.Genes.level.GetStack(i)));
if(UnityEngine.Random.value < 0.5f)
genes2.level.AddStack(CopyStack(genome1.Genes.level.GetStack(i)));
else
genes2.level.AddStack(CopyStack(genome2.Genes.level.GetStack(i)));
}
else
{
if(UnityEngine.Random.value < 0.5f)
genes1.level.AddStack(CopyStack(genome2.Genes.level.GetStack(i)));
if(UnityEngine.Random.value < 0.5f)
genes2.level.AddStack(CopyStack(genome2.Genes.level.GetStack(i)));
}
}
else
{
if(i < genome2.Genes.level.GetStacksAmount())
{
if(UnityEngine.Random.value < 0.5f)
genes1.level.AddStack(CopyStack(genome1.Genes.level.GetStack(i)));
else
genes1.level.AddStack(CopyStack(genome2.Genes.level.GetStack(i)));
if(UnityEngine.Random.value < 0.5f)
genes2.level.AddStack(CopyStack(genome1.Genes.level.GetStack(i)));
else
genes2.level.AddStack(CopyStack(genome2.Genes.level.GetStack(i)));
}
else
{
if(UnityEngine.Random.value < 0.5f)
genes1.level.AddStack(CopyStack(genome1.Genes.level.GetStack(i)));
if(UnityEngine.Random.value < 0.5f)
genes2.level.AddStack(CopyStack(genome1.Genes.level.GetStack(i)));
}
}
}
// Integer crossover for birds
genes1.level.birdsAmount = (int)(0.5f * genome1.Genes.level.birdsAmount + 0.5f * genome2.Genes.level.birdsAmount);
genes2.level.birdsAmount = (int)(1.5f * genome1.Genes.level.birdsAmount - 0.5f * genome2.Genes.level.birdsAmount);
}
else
{
for(int i = 0; i < genome1.Genes.level.GetStacksAmount(); i++)
{
genes1.level.AddStack(CopyStack(genome1.Genes.level.GetStack(i)));
}
genes1.level.birdsAmount = genome1.Genes.level.birdsAmount;
for(int i = 0; i < genome2.Genes.level.GetStacksAmount(); i++)
{
genes2.level.AddStack(CopyStack(genome2.Genes.level.GetStack(i)));
}
genes2.level.birdsAmount = genome2.Genes.level.birdsAmount;
}
genes1.level.FixLevelSize();
genes2.level.FixLevelSize();
child1.Genes = genes1;
child2.Genes = genes2;
}
private float CheckStopCriteria()
{
float fitness = Mathf.Infinity;
AngryBirdsGen genome = new AngryBirdsGen();
_geneticAlgorithm.GetBest(out genome, out fitness);
if(_lastGenerationBestFitness == fitness)
_sameBestFitnessCount++;
else
_sameBestFitnessCount = 0;
_lastGenerationBestFitness = (float)fitness;
return fitness;
}
private void EndEvolution()
{
AudioSource.PlayClipAtPoint(finishSound, new Vector3(0f, -2.25f, 0f));
CaptureScreenshot();
_experimentsIdx++;
// Save the results
SaveLog();
if(_experimentsIdx < _experimentsAmount)
{
// Run next experiment
Application.LoadLevel(Application.loadedLevel);
}
else
{
float fitness = Mathf.Infinity;
AngryBirdsGen genome = new AngryBirdsGen();
_geneticAlgorithm.GetBest(out genome, out fitness);
// Save results
WriteLogToFile(_logFile, _logContent);
// Default time scale
Time.timeScale = 1f;
// Play level starting audio
GameWorld.Instance.GetComponent<AudioSource>().PlayOneShot(GameWorld.Instance._clips[0]);
GameWorld.Instance.GetComponent<AudioSource>().PlayOneShot(GameWorld.Instance._clips[1]);
// Clear the level and decode the best genome of the last generation
GameWorld.Instance.DecodeLevel(genome.level.gameObjects, genome.level.birdsAmount);
// Save file in xml for future use
LevelLoader.SaveXmlLevel(genome.level);
// Disable simulation
GameWorld.Instance._isSimulation = false;
// Disable AI and allow player to test the level
if(GameWorld.Instance._birdAgent != null)
Destroy(GameWorld.Instance._birdAgent.gameObject);
// Destroy the generator
Destroy(this.gameObject);
}
}
private void CaptureScreenshot()
{
float fitness = Mathf.Infinity;
AngryBirdsGen genome = new AngryBirdsGen();
_geneticAlgorithm.GetBest(out genome, out fitness);
GameWorld.Instance.DecodeLevel(genome.level.gameObjects, genome.level.birdsAmount);
string info;
float aestheticFitness = AestheticFitness(out info);
Application.CaptureScreenshot("Level " + screenshotId++ + "Def Fit " + fitness.ToString("F05") + "-Fit " + aestheticFitness.ToString("F05") + "-Pop " + _populationSize.ToString() + "-Gen " + _generationSize.ToString() + info + ".png");
}
public float EvaluateUsingAI(AngryBirdsGen genome, int genomeIdx)
{
//Debug.Log ("fitness = " + _fitnessCache[genome]);
return _fitnessCache[genome];
}
