public void Run(ref IGrid grid, SelectionFunction SelectionFunction)
{
var startAt = grid.GetRandomCell;
active = new Stack<Cell>();
active.Push(startAt);
while(active.Any())
{
var cell = SelectionFunction(active);
var availableNeighbors = cell.Neighbors.Where(x => x.Links.Count == 0);
if (availableNeighbors.Any())
{
var neighbor = availableNeighbors.Sample();
cell.Link(neighbor);
active.Push(neighbor);
}
else
{
var list = active.ToList();
list.Remove(cell);
active = new Stack<Cell>(list);
}
}
}
public void Run(ref IGrid grid, SelectionFunction SelectionFunction)
{
var startAt = grid.GetRandomCell;
active = new Stack <Cell>();
active.Push(startAt);
while (active.Any())
{
var cell = SelectionFunction(active);
var availableNeighbors = cell.Neighbors.Where(x => x.Links.Count == 0);
if (availableNeighbors.Any())
{
var neighbor = availableNeighbors.Sample();
cell.Link(neighbor);
active.Push(neighbor);
}
else
{
var list = active.ToList();
list.Remove(cell);
active = new Stack <Cell>(list);
}
}
}
public static (MatingPool.Parent better, MatingPool.Parent worse) Select(this MatingPool pool, SelectionFunction selectionFunction,
float fitnessPower) {
switch (selectionFunction) {
case SelectionFunction.Best:
var b = pool.Best();
return (b, b);
case SelectionFunction.TopTwo:
return (pool.Best(), pool.Best(1));
case SelectionFunction.TopTwoRandom:
var r1 = Random.value;
var r2 = Random.value;
var (max, min) = r1 > r2 ? (r1, r2) : (r2, r1);
return (pool.elements.First(p => Mathf.Pow(p.Fitness, fitnessPower) >= max),
pool.elements.First(p => Mathf.Pow(p.Fitness, fitnessPower) >= min));
default:
return default;
}
}
public static void Crossover(this CrossoverFunction crossoverFunction, NeuralNetwork child,
GenerationEvaluation evaluation, SelectionFunction selectionFunction, float fitnessPower)
{
var(b, w) = evaluation.pool.Select(selectionFunction, fitnessPower);
var(better, worse) = (b.Genes, w.Genes);
var genes = child.genes;
var count = genes.Length;
var halfCount = genes.Length / 2;
switch (crossoverFunction)
{
case CrossoverFunction.HalfWorstBest:
for (var i = 0; i < halfCount; i++)
{
child.genes[i] = better[i];
}
for (var i = halfCount; i < count; i++)
{
child.genes[i] = worse[i];
}
break;
case CrossoverFunction.HalfBestWorse:
for (var i = 0; i < halfCount; i++)
{
child.genes[i] = worse[i];
}
for (var i = halfCount; i < count; i++)
{
child.genes[i] = better[i];
}
break;
case CrossoverFunction.HalfRandomShift:
var shift = Random.Range(0, count - 1);
var overlaps = shift + halfCount > count - 1;
for (var i = 0; i < count; i++)
{
child.genes[i] = (!overlaps && i > shift && i < shift + halfCount || overlaps && !(i > shift && i < shift + halfCount) ? better : worse)[i];
}
break;
case CrossoverFunction.Step:
for (var i = 0; i < count; i++)
{
genes[i] = (i % 2 == 0 ? better : worse)[i];
}
break;
case CrossoverFunction.StepRandom:
for (var i = 0; i < count; i++)
{
genes[i] = (Random.value > .5f ? better : worse)[i];
}
break;
case CrossoverFunction.FractionRandom: {
for (var i = 0; i < count; i++)
{
var fraction = Random.value;
genes[i] = better[i] * fraction + worse[i] * (1f - fraction);
}
break;
}
case CrossoverFunction.FractionByFitness: {
var fraction = b.Fitness / (b.Fitness + w.Fitness);
for (var i = 0; i < count; i++)
{
genes[i] = better[i] * fraction + worse[i] * (1f - fraction);
}
break;
}
case CrossoverFunction.Average:
default:
for (var i = 0; i < count; i++)
{
genes[i] = (better[i] + worse[i]) * .5f;
}
break;
}
}