private static IEnumerable <Chromosome <TGene, TFitness> > GetImprovment(MutateChromosomeDelegate newChild,
GenerateParentDelegate generateParent)
{
var bestParent = generateParent();
yield return(bestParent);
while (true)
{
var child = newChild(bestParent);
if (bestParent.Fitness.CompareTo(child.Fitness) > 0)
{
continue;
}
if (child.Fitness.CompareTo(bestParent.Fitness) <= 0)
{
bestParent = child;
continue;
}
yield return(child);
bestParent = child;
}
// ReSharper disable once IteratorNeverReturns
}
private static IEnumerable <Chromosome <TGene, TFitness> > GetImprovement(MutateChromosomeDelegate newChild,
GenerateParentDelegate generateParent, int?maxAge = null)
{
var bestParent = generateParent();
var parent = bestParent;
yield return(bestParent);
var historicalFitnesses = new List <TFitness> {
bestParent.Fitness
};
while (true)
{
var child = newChild(parent);
if (parent.Fitness.CompareTo(child.Fitness) > 0)
{
if (maxAge == null)
{
continue;
}
parent.Age++;
if (parent.Age < maxAge)
{
continue;
}
var index = historicalFitnesses.BinarySearch(child.Fitness);
if (index < 0)
{
index = ~index;
}
var difference = historicalFitnesses.Count - index;
var proportionSimilar = (double)difference / historicalFitnesses.Count;
var exp = Math.Exp(-proportionSimilar);
if (Rand.Random.NextDouble() < exp)
{
parent = child;
continue;
}
bestParent.Age = 0;
parent = bestParent;
continue;
}
if (parent.Fitness.CompareTo(child.Fitness) == 0)
{
child.Age = parent.Age + 1;
parent = child;
continue;
}
child.Age = 0;
parent = child;
if (bestParent.Fitness.CompareTo(child.Fitness) >= 0)
{
continue;
}
bestParent = child;
historicalFitnesses.Add(bestParent.Fitness);
yield return(bestParent);
}
// ReSharper disable once IteratorNeverReturns
}
private static Chromosome <TGene, TFitness> Crossover(IReadOnlyList <TGene> parentGenes, int index,
IList <Chromosome <TGene, TFitness> > parents, GetFitnessDelegate getFitness, CrossoverDelegate crossover,
MutateChromosomeDelegate mutate, GenerateParentDelegate generateParent)
{
var donorIndex = Rand.Random.Next(0, parents.Count);
if (donorIndex == index)
{
donorIndex = (donorIndex + 1) % parents.Count;
}
var childGenes = crossover(parentGenes, parents[donorIndex].Genes);
if (childGenes == null)
{
// parent and donor are indistinguishable
parents[donorIndex] = generateParent();
return(mutate(parents[index]));
}
var fitness = getFitness(childGenes);
return(new Chromosome <TGene, TFitness>(childGenes, fitness, Strategy.Crossover));
}
private static IEnumerable <Chromosome <TGene, TFitness> > GetImprovement(
StrategyDelegate newChild, GenerateParentDelegate generateParent, int?maxAge, int poolSize, int?maxSeconds)
{
var watch = Stopwatch.StartNew();
var bestParent = generateParent();
if (maxSeconds != null && watch.ElapsedMilliseconds > maxSeconds * 1000)
{
throw new SearchTimeoutException(bestParent);
}
yield return(bestParent);
var parents = new List <Chromosome <TGene, TFitness> > {
bestParent
};
var historicalFitnesses = new List <TFitness> {
bestParent.Fitness
};
while (parents.Count < poolSize)
{
var parent = generateParent();
if (maxSeconds != null && watch.ElapsedMilliseconds > maxSeconds * 1000)
{
throw new SearchTimeoutException(parent);
}
if (parent.Fitness.CompareTo(bestParent.Fitness) > 0)
{
yield return(parent);
bestParent = parent;
historicalFitnesses.Add(parent.Fitness);
}
parents.Add(parent);
}
var lastParentIndex = poolSize - 1;
var pIndex = 1;
while (true)
{
if (maxSeconds != null && watch.ElapsedMilliseconds > maxSeconds * 1000)
{
throw new SearchTimeoutException(bestParent);
}
pIndex = pIndex > 0 ? pIndex - 1 : lastParentIndex;
var parent = parents[pIndex];
var child = newChild(parent, pIndex, parents);
if (parent.Fitness.CompareTo(child.Fitness) > 0)
{
if (maxAge == null)
{
continue;
}
parent.Age++;
if (parent.Age < maxAge)
{
continue;
}
var index = historicalFitnesses.BinarySearch(child.Fitness);
if (index < 0)
{
index = ~index;
}
var difference = historicalFitnesses.Count - index;
var proportionSimilar = (double)difference / historicalFitnesses.Count;
var exp = Math.Exp(-proportionSimilar);
if (Rand.Random.NextDouble() < exp)
{
parents[pIndex] = child;
continue;
}
bestParent.Age = 0;
parents[pIndex] = bestParent;
continue;
}
if (parent.Fitness.CompareTo(child.Fitness) == 0)
{
child.Age = parent.Age + 1;
parents[pIndex] = child;
continue;
}
child.Age = 0;
parents[pIndex] = child;
if (bestParent.Fitness.CompareTo(child.Fitness) >= 0)
{
continue;
}
bestParent = child;
historicalFitnesses.Add(bestParent.Fitness);
yield return(bestParent);
}
// ReSharper disable once IteratorNeverReturns
}
