public List <MapIndividual> EvolveTestChunkOnRight(List <MapIndividual> pop = null)
{
List <MapIndividual> population;
if (pop == null)
{
population = createRightPopulation();
}
else
{
population = pop;
}
// Evolve over generations
int numberOfChildren = (int)((1 - elitism) * (float)population.Count);
MapIndividual[] children = new MapIndividual[numberOfChildren];
for (int i = 0; i < 1; i++)
{
// Sort population
population.Sort((x, y) => x.Fitness.CompareTo(y.Fitness));
// Crossover
for (int ci = 0; ci < numberOfChildren; ci++)
{
// Pick parents
var parent1 = pickParentFromPopulation(population);
var parent2 = pickParentFromPopulation(population);
// Crossover parents
MapIndividual newChild = crossover(parent1, parent2);
children[ci] = newChild;
}
for (int ci = 0; ci < numberOfChildren; ci++)
{
population[ci] = children[ci];
}
// Mutation
mutatePopulation(population);
}
// Update world matrix with best individual
MapIndividual bestIndividual = population[0];
foreach (MapIndividual individual in population)
{
if (individual.GetFitness() > bestIndividual.GetFitness())
{
bestIndividual = individual;
}
}
population.Sort((x, y) => x.Fitness.CompareTo(y.Fitness));
return(population);
}
internal MapIndividual CrossoverWith(MapIndividual parent2)
{
Random random = new Random();
int mutableXRange = this.GenerationTail.GlobalX - this.GenerationHead.GlobalX + 1;
int leftCrossLimit = (mutableXRange / 3);
int rightCrossLimit = mutableXRange - (mutableXRange / 3);
MapIndividual leftParent = random.Next(1) == 0 ? this : parent2;
MapIndividual rightParent = leftParent == this ? parent2 : this;
int maxTries = rightCrossLimit - leftCrossLimit + 1;
int tries = 0;
int originalCrossoverXPoint = random.Next(leftCrossLimit, rightCrossLimit);
int crossoverXPoint = originalCrossoverXPoint;
GeneColumn leftParentCrossPoint = leftParent.GenerationHead.SeekNthColumn(crossoverXPoint);
GeneColumn rightParentCrossPoint = rightParent.GenerationHead.SeekNthColumn(crossoverXPoint);
int lowestDiff = Mathf.Abs(leftParentCrossPoint.GroundHeight - rightParentCrossPoint.GroundHeight);
// Make sure gene combination creates a valid individual
int crossoverJump = 1;
while (tries < maxTries && !IsCrossOverXValid(leftParentCrossPoint, rightParentCrossPoint))
{
leftParentCrossPoint = leftParentCrossPoint.SeekNthColumn(crossoverJump);
rightParentCrossPoint = rightParentCrossPoint.SeekNthColumn(crossoverJump);
if (leftParentCrossPoint.GlobalX - leftParent.GenerationHead.GlobalX > rightCrossLimit)
{
leftParentCrossPoint = leftParent.GenerationHead.SeekNthColumn(leftCrossLimit);
rightParentCrossPoint = rightParent.GenerationHead.SeekNthColumn(leftCrossLimit);
}
tries++;
}
// Here we start cloning the child genes from the left parent
GeneColumn parentIterator = leftParent.Head; // Used to iterate over left parent
GeneColumn childHead = new GeneColumn(parentIterator); // This head will be returned in the end
GeneColumn childIterator = new GeneColumn(parentIterator.Next, childHead, null); // To actually iterate, this is used
childHead.Next = childIterator;
parentIterator = parentIterator.Next; // Pointing now to the second column
// Now, starting from the 3rd column now, iterate until the crossover point is reached
for (parentIterator = parentIterator.Next; parentIterator != leftParentCrossPoint; parentIterator = parentIterator.Next)
{
// We need to clone the parent's genes, but link to the child neighbor columns.
GeneColumn nextChildColumn = new GeneColumn(parentIterator, childIterator, null);
childIterator.Next = nextChildColumn;
childIterator = nextChildColumn;
}
// Copy right parent genetic code to child, starting from crossover point.
for (parentIterator = rightParentCrossPoint; parentIterator != null; parentIterator = parentIterator.Next)
{
GeneColumn nextChildColumn = new GeneColumn(parentIterator, childIterator, null);
childIterator.Next = nextChildColumn;
childIterator = nextChildColumn;
}
// Finally, create an individual from the child genetic code!
MapIndividual childIndividual = new MapIndividual(childHead);
return(childIndividual);
}
private MapIndividual crossover(MapIndividual parent1, MapIndividual parent2)
{
return(parent1.CrossoverWith(parent2));
}
public void GenerateChunksOnRight(int numChunks)
{
for (int chunkIndex = 0; chunkIndex < numChunks; chunkIndex++)
{
List <MapIndividual> population = createRightPopulation();
// Evolve over generations
int numberOfChildren = (int)((1 - elitism) * (float)population.Count);
MapIndividual[] children = new MapIndividual[numberOfChildren];
for (int i = 0; i < maxIterations; i++)
{
// Sort population
population.Sort((x, y) => x.Fitness.CompareTo(y.Fitness));
// Crossover
for (int ci = 0; ci < numberOfChildren; ci++)
{
// Pick parents
var parent1 = pickParentFromPopulation(population);
var parent2 = pickParentFromPopulation(population);
// Crossover parents
MapIndividual newChild = crossover(parent1, parent2);
children[ci] = newChild;
}
for (int ci = 0; ci < numberOfChildren; ci++)
{
population[ci] = children[ci];
}
// Mutation
mutatePopulation(population);
}
// Update world matrix with best individual
MapIndividual bestIndividual = population[0];
foreach (MapIndividual individual in population)
{
if (individual.GetFitness() > bestIndividual.GetFitness())
{
bestIndividual = individual;
}
}
// Force playability of individual
//bestIndividual.forcePlayability();
// Make best individual genes unmutable
for (GeneColumn iterator = bestIndividual.GenerationHead;
iterator != null && iterator.IsMutable;
iterator = iterator.Next)
{
iterator.IsMutable = false;
}
GlobalTail.Next = bestIndividual.GenerationHead;
bestIndividual.GenerationHead.Previous = GlobalTail;
GlobalTail = bestIndividual.GenerationTail;
}
}
public void StartTest(RTLLog log = null)
{
File testData = new File();
string fileName = "test.csv";
if (testData.FileExists(fileName))
{
testData.Open(fileName, File.ModeFlags.ReadWrite);
testData.GetCsvLine();
while (!testData.EofReached())
{
Int32.TryParse(testData.GetCsvLine()[0], out int tId);
if (this.TestId <= tId)
{
this.TestId++;
}
}
}
else
{
testData.Open(fileName, File.ModeFlags.Write);
testData.StoreCsvLine(GenerationResult.GetHeaderStringArray());
}
if (log != null)
{
log.LogLine($"Initiating test id {this.TestId} with config: popSize {this.PopulationSize}, mutRate {MutationRate}, elitism {Elitism}, maxIterations {MaxIterations}.");
}
// First generation of this test batch
// Start timer to track ellapsed time per generation
var watch = System.Diagnostics.Stopwatch.StartNew();
double duration = 0;
// the code that you want to measure comes here
int genNumber = 0;
List <MapIndividual> population = Generator.EvolveTestChunkOnRight();
MapIndividual bestIndividual = population[population.Count - 1];
GenerationResult currentGenerationResult = new GenerationResult {
TestId = this.TestId,
BestFitness = bestIndividual.Fitness,
GenerationNumber = genNumber,
FivePercentBestFitnessAvg = getFivePercentAvg(population),
IsBestPlayable = bestIndividual.IsPlayable,
FitnessAverage = getFitnessAverage(population),
FitnessMedian = getFitnessMedian(population),
PopulationSize = this.PopulationSize,
GenChunkSize = this.GenChunkSize,
MutationRate = this.MutationRate,
Elitism = this.Elitism,
};
watch.Stop();
currentGenerationResult.ExecutionTimeMs = watch.ElapsedMilliseconds;
testData.StoreCsvLine(currentGenerationResult.GetCsvStringArray());
for (genNumber = genNumber + 1; genNumber <= MaxIterations; genNumber++)
{
watch = System.Diagnostics.Stopwatch.StartNew();
population = Generator.EvolveTestChunkOnRight(population);
bestIndividual = population[population.Count - 1];
currentGenerationResult = new GenerationResult {
TestId = this.TestId,
BestFitness = bestIndividual.Fitness,
GenerationNumber = genNumber,
FivePercentBestFitnessAvg = getFivePercentAvg(population),
IsBestPlayable = bestIndividual.IsPlayable,
FitnessAverage = getFitnessAverage(population),
FitnessMedian = getFitnessMedian(population),
PopulationSize = this.PopulationSize,
GenChunkSize = this.GenChunkSize,
MutationRate = this.MutationRate,
Elitism = this.Elitism,
};
watch.Stop();
currentGenerationResult.ExecutionTimeMs = watch.ElapsedMilliseconds;
duration += watch.ElapsedMilliseconds;
testData.StoreCsvLine(currentGenerationResult.GetCsvStringArray());
}
// Close file at the end of this test
testData.Close();
TimeSpan t = TimeSpan.FromMilliseconds(duration);
string durationString = string.Format("{0:D2}h:{1:D2}m:{2:D2}s:{3:D3}ms",
t.Hours, t.Minutes, t.Seconds, t.Milliseconds);
if (log != null)
{
log.LogLine($"Done with test {this.TestId} in {durationString}!");
}
}
