private float GetFitness(IChromosome c)
{
AiSettingsChromosome chromosome = (AiSettingsChromosome)c;
int aiSettings = chromosome.ToInt();
AiEvolutionaryAlgorithmData toDelete = null;
float fitness = 0;
foreach (AiEvolutionaryAlgorithmData data in settingsScoreData)
{
if (data.aiSettings == aiSettings)
{
fitness = data.score;
toDelete = data;
}
}
float maxScore = ScoreManager.Instance.GetTheHighestScoreValue();
float playerScore = ScoreManager.Instance.GetScore(0);
fitness = fitness / maxScore;
playerScore = playerScore / maxScore;
fitness = 1 - Math.Abs(playerScore - fitness);
if (toDelete != null)
{
settingsScoreData.Remove(toDelete);
}
return(fitness);
}
public void OptimizeAI()
{
if (frequency != turnsSinceLastOptimization)
{
turnsSinceLastOptimization++;
return;
}
GenerateScoreData();
int maxSize = GetMaxSize();
AiSettingsChromosome intelligenceChromosome = new AiSettingsChromosome(3);
Population population = new Population(maxSize, maxSize, intelligenceChromosome);
FuncFitness fitness;
if (HistoryManagr.Instance.savingHistory == true)
{
fitness = new FuncFitness((c) => { return(GetHistoryFitness(c)); });
}
else
{
fitness = new FuncFitness((c) => { return(GetFitness(c)); });
}
var selection = new EliteSelection();
var crossover = new UniformCrossover(0.5f);
var mutation = new DisplacementMutation();
var termination = new GenerationNumberTermination(2);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
List <IChromosome> savedChromosomes = new List <IChromosome>();
ga.Termination = termination;
ga.GenerationRan += (sender, e) =>
{
if (ga.GenerationsNumber == 2)
{
savedChromosomes = (List <IChromosome>)ga.Population.CurrentGeneration.Chromosomes;
}
};
ga.Start();
turnsSinceLastOptimization = 0;
settingsScoreData.Clear();
settingsData.Clear();
SetGeneratedData(savedChromosomes);
foreach (PlayerData player in PlayerManager.Instance.players)
{
EventSaverManager.Instance.SaveDDAEvolutionAIEvent(player.id);
}
}
private float GetHistoryFitness(IChromosome c)
{
AiSettingsChromosome chromosome = (AiSettingsChromosome)c;
int aiSettings = chromosome.ToInt();
float fitness = 0;
AiEvolutionaryAlgorithmData toDelete = null;
foreach (AiEvolutionaryAlgorithmData data in settingsScoreData)
{
if (data.aiSettings == aiSettings)
{
fitness = data.score;
toDelete = data;
}
}
if (toDelete != null)
{
settingsScoreData.Remove(toDelete);
}
return(fitness);
}