protected AEvolutionEnvironment(PopulationParameters populationParameters, IEnvironmentParameters environmentParameters, AbstractPopulation <T> gradedPopulation, AbstractPopulation <T> solution, AbstractPopulation <T> parents, AbstractPopulation <T> children)
{
_populationParameters = populationParameters;
_environmentParameters = environmentParameters;
_gradedPopulation = gradedPopulation;
_solution = solution;
_parents = parents;
_children = children;
_rand = new Random(42);
}
public Tuple <AbstractPopulation <T>, float, AbstractPopulation <T> > EvolvePopulation(AbstractPopulation <T> population)
{
Stopwatch sw = new Stopwatch();
sw.Start();
var rawGradedPopulation = GradePopulation(population);
_gradedPopulation.CreaturePopulation.Clear();
float averageGrade = 0;
averageGrade = ComputeAverageGrade(rawGradedPopulation);
if (_solution.CreaturePopulation.Count > 0)
{
return(new Tuple <AbstractPopulation <T>, float, AbstractPopulation <T> >(population, averageGrade, _solution));
}
_parents.CreaturePopulation.Clear();
//TOP Creature
_parents.CreaturePopulation = FindTopCreatures(rawGradedPopulation);
//Genetic diversity
for (int i = _populationParameters.GradedIndividualRetainCount; i < _gradedPopulation.CreaturePopulation.Count; ++i)
{
if (_rand.Next(1, 100) < _populationParameters.ChanceRetainNonGraded)
{
var creature = _gradedPopulation.CreaturePopulation.ToArray()[i];
_parents.CreaturePopulation.Add(creature);
}
}
//Mutation
_parents = MutatePopulation();
//Crossover parents
_children = CrossOverPopulation();
_parents.CreaturePopulation.AddRange(_children.CreaturePopulation);
return(new Tuple <AbstractPopulation <T>, float, AbstractPopulation <T> >(_parents, averageGrade, _solution));
}
public virtual List <Tuple <T, IFitness> > GradePopulation(AbstractPopulation <T> population)
{
var tupleList = population.CreaturePopulation.Select(x => new Tuple <T, IFitness>(x, x.GetIndividualFitness(_environmentParameters))).ToList();
return(tupleList.OrderByDescending(x => x.Item2.GetGrade()).ToList());
}
public virtual float AveragePopulationGrade(AbstractPopulation <T> population)
{
float total = population.CreaturePopulation.Sum(creature => creature.GetIndividualFitness(_environmentParameters).GetGrade());
return(total / _populationParameters.PopulationCount);
}
