public override void Solve(Problem problem)
{
if (_initialValue == null)
{
throw new Exception("You must provide a starting point for the search");
}
_problem = problem;
_population = new Chromosome[_noNeighbors];
bool searchOver = false;
int generations = 0;
Chromosome best = new Chromosome(_initialValue);
while (!searchOver)
{
_population[0] = best;
_population[0].ComputeFitness(_problem);
// generating population by mutation
for (int i = 1; i < _noNeighbors; i++)
{
_population[i] = new Chromosome(best);
double r = _rand.NextDouble();
if (r < Settings.pmg)
{
Mutation.PerformMutation(_population[i], _problem.Parameters); // mutatie gaussiana
}
else if (r < Settings.pmg + Settings.pmr) // mutatie prin resetare
{
for (int j = 0; j < _population[i].NoGenes; j++)
{
if (_rand.NextDouble() < Settings.pm)
{
_population[i].RealGenes[j] = _problem.Parameters.EncodingInfo.MinValues[j] + ExtendedRandom.NextUniform() * (_problem.Parameters.EncodingInfo.MaxValues[j] - _problem.Parameters.EncodingInfo.MinValues[j]);
}
}
}
else
{
int i1 = _rand.Next(_population[i].RealGenes.Length);
int i2 = _rand.Next(_population[i].RealGenes.Length);
if (_population[i].RealGenes[i1] >= 2 && _population[i].RealGenes[i2] >= 2)
{
_population[i].RealGenes[i1] -= 1;
_population[i].RealGenes[i2] += 1;
}
}
_population[i].ComputeFitness(_problem);
}
best = Selection.GetBest(_population);
generations++;
if (generations > _noIterations)
{
_solution = best;
searchOver = true;
}
}
}
public override void Solve(Problem problem)
{
_problem = problem;
// generating the initial random population
_population = new Chromosome[_problem.Parameters.PopulationSize];
for (int i = 0; i < _problem.Parameters.PopulationSize; i++)
{
_population[i] = new Chromosome(_problem.Parameters, true)
{
Age = Settings.lf + _rand.Next(Settings.lf)
};
}
bool searchOver = false;
int generations = 0;
while (!searchOver)
{
for (int i = 0; i < _population.Length; i++)
{
int age = _population[i].Age;
Chromosome c = PerformHC(_population[i]);
_population[i] = c;
_population[i].Age = age - 1;
_population[i].ComputeFitness(_problem);
}
Chromosome[] newPopulation = new Chromosome[_problem.Parameters.PopulationSize];
if (_problem.Parameters.SelectionInfo.Elitism > 0)
{
Chromosome[] elite = Selection.GetElite(_population, _problem.Parameters.SelectionInfo.Elitism);
for (int i = 0; i < _problem.Parameters.SelectionInfo.Elitism; i++)
{
newPopulation[i] = elite[i];
newPopulation[i].Age = Settings.lf;
}
}
//if (_problem.Parameters.SelectionInfo.Type == SelectionType.Rank)
// Selection.SortPopulation(_population);
for (int i = problem.Parameters.SelectionInfo.Elitism; i < _problem.Parameters.PopulationSize; i++)
{
// select
Chromosome mother = Selection.PerformSelection(_population, _problem.Parameters.SelectionInfo);
Chromosome father = Selection.PerformSelection(_population, _problem.Parameters.SelectionInfo);
// combine
Chromosome child = Crossover.PerformCrossover(mother, father, _problem.Parameters);
// mutate
Mutation.PerformMutation(child, _problem.Parameters);
child.ComputeFitness(_problem);
child.Age = Settings.lf + _rand.Next(Settings.lf);
newPopulation[i] = child;
}
List <Chromosome> mergedPop = new List <Chromosome>();
for (int i = 0; i < _population.Length; i++)
{
if (_population[i].Age > 0)
{
mergedPop.Add(_population[i]);
}
}
for (int i = 0; i < newPopulation.Length; i++)
{
if (newPopulation[i].Age > 0)
{
mergedPop.Add(newPopulation[i]);
}
}
for (int i = 0; i < _problem.Parameters.PopulationSize * Settings.f; i++) // indivizi noi
{
Chromosome c = new Chromosome(_problem.Parameters, true)
{
Age = Settings.lf1 + _rand.Next(Settings.lf1)
};
mergedPop.Add(c);
}
_population = new Chromosome[mergedPop.Count];
for (int i = 0; i < _population.Length; i++)
{
_population[i] = mergedPop[i];
}
generations++;
Console.Write("Generation {0} / {1}: ", generations, _problem.Parameters.StoppingInfo.MaxGenerations);
_solution = Selection.GetBest(_population);
Console.WriteLine("{0:F4} {1}", _solution.Fitness, _population.Length);
if (generations > _problem.Parameters.StoppingInfo.MaxGenerations)
{
//_solution = Selection.GetBest(_population);
searchOver = true;
}
}
}
