public void Iterate()
{
// Get the phenotypes for the individuals
foreach (Individual individual in ChildPopulation)
{
individual.Phenotype = PhenotypeDeveloper.Develop(individual.Genotype);
}
foreach (Individual individual in ChildPopulation)
{
FitnessEvaluator.Evaluate(individual);
}
// Perform adult selection
AdultSelector.Select(ChildPopulation, AdultPopulation);
// Perform parent selection
ParentSelector.Select(ParentList, AdultPopulation);
// Make children
Individual parent1, parent2, child1, child2;
// Special case if child population size is 1
if (ChildCount == 1)
{
child1 = new Individual();
child1.Genotype = ParentList[0].Genotype.Clone();
GeneticOperator.Mutate(child1.Genotype);
ChildPopulation.Add(child1);
}
else
{
for (int i = 0; i < ParentList.Count - 1; i += 2)
{
parent1 = ParentList[i];
parent2 = ParentList[i + 1];
child1 = new Individual();
child2 = new Individual();
child1.Genotype = parent1.Genotype.Clone();
child2.Genotype = parent2.Genotype.Clone();
// Crossover
GeneticOperator.Crossover(parent1.Genotype, parent2.Genotype, child1.Genotype, child2.Genotype);
// Mutate
GeneticOperator.Mutate(child1.Genotype);
GeneticOperator.Mutate(child2.Genotype);
ChildPopulation.Add(child1);
ChildPopulation.Add(child2);
}
// check if the count is divisible by 2
if (ParentList.Count % 2 == 1)
{
child1 = new Individual();
child1.Genotype = ParentList[0].Genotype.Clone();
GeneticOperator.Mutate(child1.Genotype);
ChildPopulation.Add(child1);
}
}
// Update generation
generation++;
}
public void Iterate()
{
// Get the phenotypes for the individuals
foreach (Individual individual in ChildPopulation)
{
individual.Phenotype = PhenotypeDeveloper.Develop(individual.Genotype);
}
// Evaluate fitness of the individuals
average = 0;
// If no elitism, check the best of the generation
//if (!Elitism)
//{
max = float.MinValue;
//}
// Put in the best
if (Elitism && prevBest != null)
{
ChildPopulation.Add(prevBest);
}
FitnessEvaluator.NextGeneration();
foreach (Individual individual in ChildPopulation)
{
individual.Fitness = FitnessEvaluator.Evaluate(individual);
average += individual.Fitness;
if (individual.Fitness > max)
{
max = individual.Fitness;
best = individual;
}
}
average = average / ChildPopulation.Count;
// Perform adult selection
AdultSelector.Select(ChildPopulation, AdultPopulation);
// Perform parent selection
ParentSelector.Select(ParentList, AdultPopulation);
// Make children
Individual parent1, parent2, child1, child2;
// Special case if child population size is 1
if (ChildCount == 1)
{
child1 = new Individual();
child1.Genotype = ParentList[0].Genotype.Clone();
GeneticOperator.Mutate(child1.Genotype);
ChildPopulation.Add(child1);
}
else
{
for (int i = 0; i < ParentList.Count - 1; i += 2)
{
parent1 = ParentList[i];
parent2 = ParentList[i + 1];
child1 = new Individual();
child2 = new Individual();
child1.Genotype = parent1.Genotype.Clone();
child2.Genotype = parent2.Genotype.Clone();
// Crossover
GeneticOperator.Crossover(parent1.Genotype, parent2.Genotype, child1.Genotype, child2.Genotype);
// Mutate
GeneticOperator.Mutate(child1.Genotype);
GeneticOperator.Mutate(child2.Genotype);
ChildPopulation.Add(child1);
ChildPopulation.Add(child2);
}
// check if the count is divisible by 2
if (ParentList.Count % 2 == 1)
{
child1 = new Individual();
child1.Genotype = ParentList[0].Genotype.Clone();
GeneticOperator.Mutate(child1.Genotype);
ChildPopulation.Add(child1);
}
}
// Set the best individual for the next generation
prevBest = best;
// Update generation
generation++;
}
