public GeneticAlgorithmResult Evolve(int generations, double crossoverProbability, double mutationProbability, double duplicationProbability, double pruningProbability)
{
GeneticAlgorithmResult result = new GeneticAlgorithmResult();
result.stats = new List <GenerationStats>();
//Evaluate and save the results of the first generation
Console.WriteLine("Eseguendo generazione iniziale");
Evaluate(Population.CurrentGeneration);
Population.CurrentGeneration.Stats.fitnessValues = Population.CurrentGeneration.Select(x => x.Fitness.Value).ToList();
Population.CurrentGeneration.Stats.bestChromosome = Population.CurrentGeneration.OrderByDescending(x => x.Fitness).First();
result.stats.Add(Population.CurrentGeneration.Stats);
for (int i = 1; i < generations; i++)
{
Console.WriteLine("Eseguendo generazione " + (i + 1) + " di " + generations);
Generation newGeneration = new Generation();
//Finds all the individuals with a valid fitness and takes the best ones
int elitesNumber = Math.Min(Population.CurrentGeneration.FindAll(x => x.Fitness.HasValue).Count, ElitismSize);
List <Chromosome> elites = Population.CurrentGeneration.OrderByDescending(x => x.Fitness.Value).Take(elitesNumber).Select(x => x.GetClone()).ToList();
int newGenerationSize = Population.CurrentGeneration.Count - elitesNumber;
while (newGeneration.Count < newGenerationSize)
{
Tuple <Chromosome, Chromosome> parents = Selection.Select(Population.CurrentGeneration);
Tuple <Chromosome, Chromosome> children = Crossover.Cross(parents.Item1, parents.Item2, crossoverProbability);
//If there is room for only one individual, choose randomly
if (newGeneration.Count == newGenerationSize - 1)
{
if (GrammaticalEvolution.random.NextDouble() > 0.5)
{
newGeneration.Add(children.Item1);
}
else
{
newGeneration.Add(children.Item2);
}
}
else
{
newGeneration.Add(children.Item1);
newGeneration.Add(children.Item2);
}
}
foreach (Chromosome c in newGeneration)
{
Mutation.Mutate(c, mutationProbability);
}
Evaluate(newGeneration);
Prune(newGeneration, pruningProbability);
Duplicate(newGeneration, duplicationProbability);
newGeneration.AddRange(elites);
newGeneration.Stats.fitnessValues = newGeneration.Select(x => x.Fitness.Value).ToList();
newGeneration.Stats.bestChromosome = newGeneration.OrderByDescending(x => x.Fitness).First().GetClone();
Population.Add(newGeneration);
//We remove the chromosomes of older generations (but store their fitness)
if (!SaveChromosomes && Population.Count > 1)
{
Population[Population.Count - 2].Clear();
}
result.stats.Add(newGeneration.Stats);
}
return(result);
}
public GeneticAlgorithm(int elitismSize, GrammaticalEvolution ge, FitnessCalculator fitnessCalculator, Selection selection, Crossover crossover, Mutation mutation, Population population, ExecutionParameters parameters)
{
ElitismSize = elitismSize;
GE = ge;
_FitnessCalculator = fitnessCalculator;
Selection = selection;
Crossover = crossover;
Mutation = mutation;
Population = population;
Parameters = parameters;
}
