// the whole encode / decode process (just for learning purposes)
void encode(WeightsInfo weightsInfo, List <double> gene)
{
for (int k = 0; k < weightsInfo.lstWeight.Count; k++)
{
for (int i = 0; i < weightsInfo.lstWeight[k].GetLength(0); i++)
{
for (int j = 0; j < weightsInfo.lstWeight[k].GetLength(1); j++)
{
gene.Add(weightsInfo.lstWeight[k][i, j]);
}
}
}
}
void decode(WeightsInfo weightsInfo, List <double> gene)
{
for (int k = 0; k < weightsInfo.lstWeight.Count; k++)
{
for (int i = 0; i < weightsInfo.lstWeight[k].GetLength(0); i++)
{
for (int j = 0; j < weightsInfo.lstWeight[k].GetLength(1); j++)
{
weightsInfo.lstWeight[k][i, j] = gene[0];
gene.RemoveAt(0);
}
}
}
}
public override ActionResult SubWeights(WeightsInfo input)
{
var termNumber = input.TermNumber;
var weights = input.Weights;
var totalWeights = State.TotalWeightsMap[termNumber];
Assert(totalWeights > 0, $"Invalid weights of term {termNumber}");
var newWeights = totalWeights - weights;
State.TotalWeightsMap[termNumber] = newWeights;
return(new ActionResult {
Success = true
});
}
public override SInt64Value AddWeights(WeightsInfo input)
{
var termNumber = input.TermNumber;
var weights = input.Weights;
var currentWeights = State.TotalWeightsMap[termNumber];
var finalWeights = currentWeights + weights;
State.TotalWeightsMap[termNumber] = finalWeights;
Context.LogDebug(() => $"Weights of term {termNumber}: {finalWeights}.[Add]");
return(new SInt64Value()
{
Value = finalWeights
});
}
// creates a new candidate solution using crossover
void crossover(List <double> gene1, List <double> gene2)
{
if (r.NextDouble() > CROSSOVER_RATE)
{
return;
}
List <double> descendant1 = new List <double>();
List <double> descendant2 = new List <double>();
int count;
// mixing the genes using the arithmetic mean
if (gene1.Count < gene2.Count)
{
count = gene1.Count;
}
else
{
count = gene2.Count;
}
for (int i = 0; i < count; i++)
{
descendant1.Add((gene1[i] + gene2[i]) / 2.0);
descendant2.Add((gene1[i] + gene2[i]) / 2.0);
}
// decoding the result back to the "weights-format"
WeightsInfo weightsInfo1 = new WeightsInfo();
decode(weightsInfo1, descendant1);
nextWeightsList.Add(weightsInfo1);
WeightsInfo weightsInfo2 = new WeightsInfo();
decode(weightsInfo2, descendant2);
nextWeightsList.Add(weightsInfo2);
}
public void breedNetworks(int gameIndex)
{
// updates the score
weightsList[gameIndex].fitness = lstgame[gameIndex].GameTime + lstgame[gameIndex].Score;
averageFitness += weightsList[gameIndex].fitness;
maxFitness = maxFitness > weightsList[gameIndex].fitness ? maxFitness : weightsList[gameIndex].fitness;
if (DataGame.IsAllGameFailed())
{
generation++;
Debug.WriteLine("GEN: " + generation + " | AVG: " + averageFitness / (float)POPULATION_SIZE + " | MAX: " + maxFitness);
averageFitness = 0;
maxFitness = 0;
weightsList = weightsList.OrderByDescending(wi => wi.fitness).ToList();
// starting with a large mutation rate so there's will be more solutions to choose from
if (weightsList[0].fitness < 10)
{
MUTATION_RATE = 0.9;
}
else if (weightsList[0].fitness < 50 && weightsList[0].fitness >= 10)
{
MUTATION_RATE = 0.2;
}
else
{
MUTATION_RATE = 0.05;
}
// adding better solutions from the other instances (if any)
if (nextWeightsList.Count + 3 <= POPULATION_SIZE)
{
// the whole synchronization thingy
try
{
if (mmfMutex == null)
{
mmfMutex = Mutex.OpenExisting("mmfMutex");
}
if (mmfMutex.WaitOne())
{
WeightsInfo wi = new WeightsInfo();
wi = dataSharer.getFromMemoryMap();
if (wi == null || wi.fitness < weightsList[0].fitness)
{
if (wi == null)
{
Debug.WriteLine("Updated - NULL -> " + weightsList[0].fitness);
}
if (wi != null)
{
Debug.WriteLine("Updated - " + wi.fitness + " -> " + weightsList[0].fitness);
}
dataSharer.writeToMemoryMap(weightsList[0]);
}
if (wi != null && wi.fitness > weightsList[0].fitness)
{
nextWeightsList.Add(wi);
}
mmfMutex.ReleaseMutex();
}
}
catch (WaitHandleCannotBeOpenedException ex)
{
mmfMutex = new Mutex(true, "mmfMutex");
nextWeightsList.AddRange(weightsList.Take(4));
mmfMutex.ReleaseMutex();
}
// adding elites to the next generation
nextWeightsList.AddRange(weightsList.Take(3));
}
// creating a new generation
while (nextWeightsList.Count < POPULATION_SIZE)
{
WeightsInfo w1 = select();
WeightsInfo w2 = select();
while (w1 == w2)
{
w1 = select();
w2 = select();
}
List <double> gene1 = new List <double>();
List <double> gene2 = new List <double>();
encode(w1, gene1);
encode(w2, gene2);
crossover(gene1, gene2);
if (mutate(gene1))
{
w1 = new WeightsInfo();
}
if (mutate(gene2))
{
w2 = new WeightsInfo();
}
decode(w1, gene1);
decode(w2, gene2);
if (!nextWeightsList.Contains(w1))
{
nextWeightsList.Add(w1);
}
if (!nextWeightsList.Contains(w2))
{
nextWeightsList.Add(w2);
}
}
weightsList.Clear();
nextWeightsList = nextWeightsList.OrderByDescending(wi => wi.fitness).ToList();
weightsList.AddRange(nextWeightsList);
nextWeightsList.Clear();
}
}