/// <summary>
/// Solves the given problem.
/// </summary>
/// <returns></returns>
public override TSolution Solve(TProblem problem, TObjective objective, out TFitness fitness)
{
var population = new Individual <TSolution, TFitness> [_settings.PopulationSize];
// generate initial population.
var solutionCount = 0;
while (solutionCount < _settings.PopulationSize)
{
TFitness localFitness;
var solution = _generator.Solve(problem, objective, out localFitness);
population[solutionCount] = new Individual <TSolution, TFitness>()
{
Fitness = localFitness,
Solution = solution
};
solutionCount++;
}
// sort population.
Array.Sort(population, (x, y) =>
{
return(objective.CompareTo(problem, x.Fitness, y.Fitness));
});
var bestIndividual = population[0];
this.ReportIntermidiateResult(bestIndividual.Solution);
// mutate/crossover population.
var stagnation = 0;
var generation = 0;
var elitism = (int)(_settings.PopulationSize * (_settings.ElitismPercentage / 100.0));
var crossOver = (int)(_settings.PopulationSize * (_settings.CrossOverPercentage / 100.0));
var crossOverIndividuals = new Individual <TSolution, TFitness> [crossOver];
var exclude = new HashSet <int>();
while (stagnation < _settings.StagnationCount &&
generation < _settings.MaxGenerations &&
!this.IsStopped)
{
// select individuals for crossover.
exclude.Clear();
for (int i = 0; i < crossOver; i++)
{
// select individual.
var selected = -1;
while (selected < 0)
{
selected = _selection.Select(problem, objective, population, exclude);
}
crossOverIndividuals[i] = population[selected];
exclude.Add(selected);
}
// replace part of the population by offspring.
for (int i = elitism; i < population.Length; i++)
{
// take two random parents.
var individual1 = _random.Next(crossOver);
var individual2 = _random.Next(crossOver - 1);
if (individual1 <= individual2)
{ // make sure they are different.
individual2++;
}
// create offspring.
TFitness offspringFitness;
var offspring = _crossOver.Apply(problem, objective, population[individual1].Solution,
population[individual2].Solution, out offspringFitness);
population[i] = new Individual <TSolution, TFitness>()
{
Solution = offspring,
Fitness = offspringFitness
};
}
// mutate part of the population.
for (int i = elitism; i < population.Length; i++)
{
if (_random.Next(100) <= _settings.MutationPercentage)
{ // ok, mutate this individual.
TFitness mutatedDelta;
if (_mutation.Apply(problem, objective, population[i].Solution, out mutatedDelta))
{ // mutation succeeded.
population[i].Fitness = objective.Subtract(problem, population[i].Fitness, mutatedDelta);
}
}
}
// sort new population.
Array.Sort(population, (x, y) =>
{
return(objective.CompareTo(problem, x.Fitness, y.Fitness));
});
if (objective.IsBetterThan(problem, bestIndividual.Fitness, population[0].Fitness))
{ // a better individual was found.
bestIndividual = population[0];
stagnation = 0; // reset stagnation flag.
this.ReportIntermidiateResult(bestIndividual.Solution);
}
else
{ // no better solution found.
stagnation++;
}
}
fitness = bestIndividual.Fitness;
return(bestIndividual.Solution);
}
