public void Generation()
{
batch.ForEach(evolvable => evolvable.Mutate(mutatability, random));
batch.ForEach(evolvable => evolvable.CalculateFitness());
double survivalFitness = survivalRate < 0 ? -survivalRate *batch.Min(x => x.Fitness) + (1 + survivalRate) * batch.Average(x => x.Fitness) :
survivalRate *batch.Max(x => x.Fitness) + (1 - survivalRate) * batch.Average(x => x.Fitness);
batch = batch
.Where(x => x.Fitness > 0 && random.NextDouble() < failureImmunity ||
(x.Fitness >= survivalFitness && random.NextDouble() >= suddenDeath))
.ToList();
if (batch.Count < batchSize)
{
WeightedListRandom <T> genePool = new WeightedListRandom <T>(batch, random);
while (batch.Count < batchSize)
{
List <IEvolvable> parents = genePool.Next(genders, x => x.Fitness, false).Select(x => (IEvolvable)x).ToList();
batch.Add((T)parents[0].Crossover(parents.Skip(1), crossovers, random));
}
}
}
public void Generation()
{
evolvers.ForEach(x =>
{
x.genders = genders;
x.crossovers = crossovers;
x.mutatability = mutatability;
x.survivalRate = survivalRate;
x.failureImmunity = failureImmunity;
x.suddenDeath = suddenDeath;
});
evolvers.ForEach(x => x.Generation());
if (generation++ % 10 == 0)
{
List <T> batch = Batch;
batch.ForEach(x => x.CalculateFitness());
double averageFitness = batch.Average(x => x.Fitness);
double survivalFitness = evolverSurvivalRate < 0 ? -evolverSurvivalRate *batch.Min(x => x.Fitness) + (1 + evolverSurvivalRate) * averageFitness :
evolverSurvivalRate *batch.Max(x => x.Fitness) + (1 - evolverSurvivalRate) * averageFitness;
evolvers = evolvers
.Where(x => (x.batch.Average(y => y.Fitness) > 0 &&
random.NextDouble() < failureImmunity
||
(x.batch.Average(y => y.Fitness) >= survivalFitness &&
random.NextDouble() >= suddenDeath)))
.ToList();
if (evolvers.Count < evolverCount)
{
WeightedListRandom <Evolver <T> > genePool = new WeightedListRandom <Evolver <T> >(evolvers, random);
while (evolvers.Count < evolverCount)
{
List <Evolver <T> > parents = genePool.Next(genders, x => x.batch.Average(y => y.Fitness), false).ToList();
List <int> crossoverSpots = new List <int>(crossovers);
int batchSize = parents[0].batchSize;
for (int i = 0; i < evolverCrossovers; i++)
{
int nextSpot;
while (crossoverSpots.Contains(nextSpot = random.Next(0, batchSize)))
{
;
}
crossoverSpots.Add(nextSpot);
}
crossoverSpots.Sort();
Evolver <T> evolver = new Evolver <T>(new List <T>(batchSize), random);
int currentParent = 0;
for (int i = 0; i < batchSize; i++)
{
if (crossoverSpots.Count > 0 && i > crossoverSpots[0])
{
currentParent = (currentParent + 1) % parents.Count;
crossoverSpots.RemoveAt(0);
}
evolver.batch.Add(parents[currentParent].batch[i]);
}
evolvers.Add(evolver);
}
}
}
}
