public void TestChromosomeClone()
{
// Use the Assert class to test conditions
var chromosome = new LevelChunkChromosome(10);
Assert.AreEqual(chromosome, chromosome.Clone());
}
public void TestEquality()
{
var fitness = new LevelChunkFitness(0);
// Use the Assert class to test conditions
var chromosome = new LevelChunkChromosome(10);
Assert.LessOrEqual(Mathf.Abs((float)fitness.Evaluate(chromosome)
- (float)fitness.Evaluate(chromosome.Clone())), _errorMargin);
}
public void TestIncreaseHT()
{
var fitness = new LevelChunkFitness(10);
// Use the Assert class to test conditions
var chromosome1 = new LevelChunkChromosome(2);
chromosome1.ReplaceGenes(0, new Gene[] { new Gene(LevelElement.None), new Gene(LevelElement.None) });
var chromosome2 = new LevelChunkChromosome(2);
chromosome2.ReplaceGenes(0, new Gene[] { new Gene(LevelElement.None), new Gene(LevelElement.Beast) });
Assert.Less(fitness.Evaluate(chromosome1), fitness.Evaluate(chromosome2));
}
private void GenerateLevelGS()
{
Level = new LevelElement[LevelSize];
for (var i = 0; i < nChunks; i++)
{
var dcParams = DifficultyCurveParams;
var difficulty = DifficultyCurve(i, dcParams.a, dcParams.b, dcParams.k);
var eParams = EvolutionParams;
var chunkSize = LevelSize / nChunks;
var fitness = new LevelChunkFitness(difficulty);
var chromosome = new LevelChunkChromosome(chunkSize);
var crossover = new OnePointCrossover(chunkSize / 2);
var mutation = new UniformMutation(allGenesMutable: true);
var selection = new RouletteWheelSelection();
var population = new Population(50, 100, chromosome);
var m_ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new TimeEvolvingTermination(new TimeSpan(0, 0, 1))
};
m_ga.TaskExecutor = new ParallelTaskExecutor
{
MinThreads = 2,
MaxThreads = 5
};
m_ga.Start();
// Everty time a generation ends, we log the best solution.
/*m_ga.GenerationRan += delegate
* {
* var f = m_ga.BestChromosome.Fitness;
* Debug.Log($"Generation: {m_ga.GenerationsNumber} - Fitness: ${f}");
* };*/
var genes = m_ga.BestChromosome.GetGenes();
for (int j = 0; j < chunkSize; j++)
{
Level[i * chunkSize + j] = (LevelElement)genes[j].Value;
}
Debug.Log($"Fitness: {m_ga.BestChromosome.Fitness}");
}
}
