public virtual void EvaluatePopulation(Population pop, EvolutionAlgorithm ea)
{
// Evaluate in single-file each genome within the population.
// Only evaluate new genomes (those with EvaluationCount==0).
int count = pop.GenomeList.Count;
for (int i = 0; i < count; i++)
{
IGenome g = pop.GenomeList[i];
if (g.EvaluationCount != 0)
{
continue;
}
INetwork network = g.Decode(activationFn);
if (network == null)
{ // Future genomes may not decode - handle the possibility.
g.Fitness = EvolutionAlgorithm.MIN_GENOME_FITNESS;
}
else
{
g.Fitness = Math.Max(networkEvaluator.EvaluateNetwork(network), EvolutionAlgorithm.MIN_GENOME_FITNESS);
}
// Reset these genome level statistics.
g.TotalFitness = g.Fitness;
g.EvaluationCount = 1;
// Update master evaluation counter.
evaluationCount++;
}
}
/// <summary>
/// Mate two genomes. Will loop over all chromosomes.
/// </summary>
/// <param name="father">The father.</param>
/// <param name="child1">The first child.</param>
/// <param name="child2">The second child.</param>
public void Mate(IGenome father, IGenome child1, IGenome child2, GATracker.MatedElement matedElement)
{
int motherChromosomes = Chromosomes.Count;
int fatherChromosomes = father.Chromosomes.Count;
if (motherChromosomes != fatherChromosomes)
{
throw new GeneticError(
"Mother and father must have same chromosome count, Mother:"
+ motherChromosomes + ",Father:"
+ fatherChromosomes);
}
for (int i = 0; i < fatherChromosomes; i++)
{
Chromosome motherChromosome = chromosomes[i];
Chromosome fatherChromosome = father.Chromosomes[i];
Chromosome offspring1Chromosome = child1.Chromosomes[i];
Chromosome offspring2Chromosome = child2.Chromosomes[i];
//This splices the chromosomes together, using a random number to decide splice points
GA.Crossover.Mate(motherChromosome, fatherChromosome, offspring1Chromosome, offspring2Chromosome);
//Set the gene number
if (matedElement != null)
{
matedElement.SetNumChildGenes(offspring1Chromosome.Genes.Count, 1);
matedElement.SetNumChildGenes(offspring2Chromosome.Genes.Count, 2);
}
//Now we potentially mutate the children
if (ThreadSafeRandom.NextDouble() < GA.MutationPercent)
{
if (matedElement != null)
{
matedElement.Child1Mutated = true;
}
GA.Mutate.PerformMutation(offspring1Chromosome, matedElement, 1);
}
//Now we potentially mutate the children
if (ThreadSafeRandom.NextDouble() < GA.MutationPercent)
{
if (matedElement != null)
{
matedElement.Child2Mutated = true;
}
GA.Mutate.PerformMutation(offspring2Chromosome, matedElement, 2);
}
}
child1.Decode();
child2.Decode();
GA.PerformScoreCalculation(child1);
GA.PerformScoreCalculation(child2);
}
public override void EvaluatePopulation(Population pop, EvolutionAlgorithm ea)
{
// Evaluate in single-file each genome within the population.
// Only evaluate new genomes (those with EvaluationCount==0).
int count = pop.GenomeList.Count;
for (int i = 0; i < count; i++)
{
// Grab the next individual out of the population
IGenome g = pop.GenomeList[i];
if (g.EvaluationCount != 0)
{
continue;
}
// The current genome is a CPPN genome, not a network genome
// So, decode the CPPN genome into a CPPN, use the CPPN to generate an ANN,
// then run the networkEvaluator on the ANN
INetwork cppn = g.Decode(activationFn);
if (cppn == null)
{ // Future genomes may not decode - handle the possibility.
g.Fitness = EvolutionAlgorithm.MIN_GENOME_FITNESS;
}
else
{
//BehaviorType behavior;
//INetwork network = Chromaria.Simulator.controllerSubstrate.generateGenome(cppn).Decode(activationFn);
g.Fitness = Math.Max(0.0f, EvolutionAlgorithm.MIN_GENOME_FITNESS);
//if (Chromaria.Simulator.plantingWasValid)
//Chromaria.Simulator.successfulPlanterGenome = g;
g.RealFitness = g.Fitness;
}
// Reset these genome level statistics.
g.TotalFitness = g.Fitness;
g.EvaluationCount = 1;
// Update master evaluation counter.
evaluationCount++;
// Close the XML tag for this individual
//Chromaria.Simulator.xmlWriter.WriteEndElement();
}
if (ea.NeatParameters.noveltySearch)
{
if (ea.NeatParameters.noveltySearch && ea.noveltyInitialized)
{
ea.CalculateNovelty();
}
}
}
public override void EvaluatePopulation(Population pop, EvolutionAlgorithm ea)
{
// Evaluate in single-file each genome within the population.
// Only evaluate new genomes (those with EvaluationCount==0).
FoodGatherParams.fillLookups();
FoodGatherParams.fillFood();
int count = pop.GenomeList.Count;
for (int i = 0; i < count; i++)
{
IGenome g = pop.GenomeList[i];
if (g.EvaluationCount != 0)
{
continue;
}
INetwork network = g.Decode(activationFn);
if (network == null)
{ // Future genomes may not decode - handle the possibility.
g.Fitness = EvolutionAlgorithm.MIN_GENOME_FITNESS;
}
else
{
g.Fitness = Math.Max(networkEvaluator.EvaluateNetwork(network), EvolutionAlgorithm.MIN_GENOME_FITNESS);
g.ObjectiveFitness = g.Fitness;
}
// Reset these genome level statistics.
g.TotalFitness = g.Fitness;
g.EvaluationCount = 1;
// Update master evaluation counter.
evaluationCount++;
}
if (requestResolutionUp == true)
{
requestResolutionUp = false;
requestResolutionDown = false;
FoodGatherParams.resolution *= 2;
}
else if (requestResolutionDown == true)
{
requestResolutionUp = false;
requestResolutionDown = false;
if (FoodGatherParams.resolution > 4)
{
FoodGatherParams.resolution /= 2;
}
}
}
/// <summary>
/// Mate two genomes. Will loop over all chromosomes.
/// </summary>
///
/// <param name="father">The father.</param>
/// <param name="child1">The first child.</param>
/// <param name="child2">The second child.</param>
public void Mate(IGenome father, IGenome child1,
IGenome child2)
{
int motherChromosomes = Chromosomes.Count;
int fatherChromosomes = father.Chromosomes.Count;
if (motherChromosomes != fatherChromosomes)
{
throw new GeneticError(
"Mother and father must have same chromosome count, Mother:"
+ motherChromosomes + ",Father:"
+ fatherChromosomes);
}
for (int i = 0; i < fatherChromosomes; i++)
{
Chromosome motherChromosome = _chromosomes[i];
Chromosome fatherChromosome = father.Chromosomes[i];
Chromosome offspring1Chromosome = child1.Chromosomes[i];
Chromosome offspring2Chromosome = child2.Chromosomes[i];
_ga.Crossover.Mate(motherChromosome,
fatherChromosome, offspring1Chromosome,
offspring2Chromosome);
if (ThreadSafeRandom.NextDouble() < _ga.MutationPercent)
{
_ga.Mutate.PerformMutation(
offspring1Chromosome);
}
if (ThreadSafeRandom.NextDouble() < _ga.MutationPercent)
{
_ga.Mutate.PerformMutation(
offspring2Chromosome);
}
}
child1.Decode();
child2.Decode();
_ga.PerformCalculateScore(child1);
_ga.PerformCalculateScore(child2);
}
public virtual void EvaluatePopulation(Population pop, EvolutionAlgorithm ea)
{
// Evaluate in single-file each genome within the population.
// Only evaluate new genomes (those with EvaluationCount==0).
int count = pop.GenomeList.Count;
for (int i = 0; i < count; i++)
{
IGenome g = pop.GenomeList[i];
if (g.EvaluationCount != 0)
{
continue;
}
INetwork network = g.Decode(activationFn);
if (network == null)
{ // Future genomes may not decode - handle the possibility.
g.Fitness = EvolutionAlgorithm.MIN_GENOME_FITNESS;
}
else
{
BehaviorType behavior;
g.Fitness = Math.Max(networkEvaluator.EvaluateNetwork(network, out behavior), EvolutionAlgorithm.MIN_GENOME_FITNESS);
g.RealFitness = g.Fitness;
g.Behavior = behavior;
}
// Reset these genome level statistics.
g.TotalFitness = g.Fitness;
g.EvaluationCount = 1;
// Update master evaluation counter.
evaluationCount++;
}
if (ea.NeatParameters.noveltySearch)
{
if (ea.NeatParameters.noveltySearch && ea.noveltyInitialized)
{
ea.CalculateNovelty();
}
}
}
/// <summary>
/// Mate two genomes. Will loop over all chromosomes.
/// </summary>
///
/// <param name="father">The father.</param>
/// <param name="child1">The first child.</param>
/// <param name="child2">The second child.</param>
public void Mate(IGenome father, IGenome child1,
IGenome child2)
{
int motherChromosomes = Chromosomes.Count;
int fatherChromosomes = father.Chromosomes.Count;
if (motherChromosomes != fatherChromosomes)
{
throw new GeneticError(
"Mother and father must have same chromosome count, Mother:"
+ motherChromosomes + ",Father:"
+ fatherChromosomes);
}
for (int i = 0; i < fatherChromosomes; i++)
{
Chromosome motherChromosome = _chromosomes[i];
Chromosome fatherChromosome = father.Chromosomes[i];
Chromosome offspring1Chromosome = child1.Chromosomes[i];
Chromosome offspring2Chromosome = child2.Chromosomes[i];
_ga.Crossover.Mate(motherChromosome,
fatherChromosome, offspring1Chromosome,
offspring2Chromosome);
if (ThreadSafeRandom.NextDouble() < _ga.MutationPercent)
{
_ga.Mutate.PerformMutation(
offspring1Chromosome);
}
if (ThreadSafeRandom.NextDouble() < _ga.MutationPercent)
{
_ga.Mutate.PerformMutation(
offspring2Chromosome);
}
}
child1.Decode();
child2.Decode();
_ga.PerformCalculateScore(child1);
_ga.PerformCalculateScore(child2);
}
