public void CreatePopulation(int i_size)
{
int[] permutation = new int[i_size];
for (int i = 0; i < i_size; i++)
{
permutation[i] = i;
}
Inhabitants[0] = new Individual(permutation);
var temp = EvalFunctions.GetFitness(Inhabitants[0]);
Inhabitants[0].FitnessD = temp.Item1;
Inhabitants[0].FitnessT = temp.Item2;
Inhabitants[0].FitnessC = temp.Item3;
Inhabitants[0].Fitness = temp.Item4;
//Creating random permutations using Knuth algorithm.
for (int i = 1; i < Size; i++)
{
permutation = ExtensionMethods.GetRandomPermutation(permutation);
Inhabitants[i] = new Individual(permutation);
var fitness = EvalFunctions.GetFitness(Inhabitants[i]);
Inhabitants[i].FitnessD = fitness.Item1;
Inhabitants[i].FitnessT = fitness.Item2;
Inhabitants[i].FitnessC = fitness.Item3;
Inhabitants[i].Fitness = fitness.Item4;
}
Sort();
}
public Individual CreateIndividual()
{
HashSet <int> inPath;
inPath = new HashSet <int>();
Random rd = new Random();
int[] new_path = Enumerable.Range(0, path_size).ToArray();
double threshold, cumulative_probability;
//Creating path.
//i -> x; j -> y
for (int i = 0; i < path_size - 1; i++)
{
threshold = rd.NextDouble();
cumulative_probability = 0;
for (int j = 0; j < path_size; j++)
{
cumulative_probability += distribution[i, j];
if (cumulative_probability < threshold)
{
continue;
}
//If a node is already in the set, start the search for another node.
if (inPath.Contains(j))
{
break;
}
else
{
//If the node isn't in the set, add it.
//place node in the right position.
inPath.Add(j);
int position = new_path.FindValue(j);
int temp;
temp = new_path[i];
new_path[i] = new_path[position];
new_path[position] = temp;
break;
}
}
}
Individual new_individual = new Individual(new_path);
var fitness = EvalFunctions.GetFitness(new_individual);
new_individual.FitnessD = fitness.Item1;
new_individual.FitnessT = fitness.Item2;
new_individual.FitnessC = fitness.Item3;
new_individual.Fitness = fitness.Item4;
return(new_individual);
}
/// <summary>
/// Creates a new solution in the neighborhood of the given solution.
/// </summary>
/// <param name="solution"></param>
/// <returns></returns>
private Individual newSolution(Individual solution)
{
rd = new Random();
int[] new_path = solution.Path;
//Creating new path in the neighborhood.
for (int i = 0; i < new_path.Length / 10; i++)
{
swap(ref new_path[rd.Next() % new_path.Length], ref new_path[rd.Next() % new_path.Length]);
}
Individual new_solution = new Individual(new_path);
//Setting fitness for new solution.
var fitness = EvalFunctions.GetFitness(new_solution);
new_solution.FitnessD = fitness.Item1;
new_solution.FitnessT = fitness.Item2;
new_solution.FitnessC = fitness.Item3;
new_solution.Fitness = fitness.Item4;
return(new_solution);
}