private static Chromosome OnePointBinary(Chromosome mother, Chromosome father)
{
// implemented for binary encoding
Chromosome child = new Chromosome(mother); // for array allocation
for (int i = 0; i < child.NoGenes; i++)
{
int len = child.BinaryGenes[i].Length;
int pivot = (int)(ExtendedRandom.NextUniform() * len);
if (ExtendedRandom.NextUniform() < _info.Probability)
{
child.BinaryGenes[i] = mother.BinaryGenes[i].Substring(0, pivot) + father.BinaryGenes[i].Substring(pivot);
}
else // no crossover, copy one parent
{
if (ExtendedRandom.NextUniform() < 0.5)
{
child.BinaryGenes[i] = mother.BinaryGenes[i];
}
else
{
child.BinaryGenes[i] = father.BinaryGenes[i];
}
}
}
return(child);
}
private static void ExchangePermutation(Chromosome child)
{
// implemented for permutation encoding
for (int i = 0; i < child.NoGenes; i++)
{
int len = child.PermutationGenes[i].Length;
if (ExtendedRandom.NextUniform() < _info.Probability)
{
// generate exchange points
int p1 = (int)(ExtendedRandom.NextUniform() * len);
int p2 = p1;
while (p2 == p1)
{
p2 = (int)(ExtendedRandom.NextUniform() * len);
}
int auxi = child.PermutationGenes[i][p1];
child.PermutationGenes[i][p1] = child.PermutationGenes[i][p2];
child.PermutationGenes[i][p2] = auxi;
}
}
}
private static void ResetBinary(Chromosome child)
{
// implemented for binary encoding
for (int i = 0; i < child.NoGenes; i++)
{
string newGene = "";
for (int j = 0; j < child.BinaryGenes[i].Length; j++)
{
if (ExtendedRandom.NextUniform() < _info.Probability)
{
if (child.BinaryGenes[i][j] == '0')
{
newGene += "1";
}
else
{
newGene += "0";
}
}
else
{
newGene += child.BinaryGenes[i][j];
}
}
child.BinaryGenes[i] = newGene;
}
}
private static Chromosome OnePointPermutation(Chromosome mother, Chromosome father)
{
// implemented for permutation encoding
Chromosome child = new Chromosome(mother); // for array allocation
for (int i = 0; i < child.NoGenes; i++)
{
int len = child.PermutationGenes[i].Length;
int pivot = (int)(ExtendedRandom.NextUniform() * len);
if (ExtendedRandom.NextUniform() < _info.Probability)
{
List <int> values = new List <int>();
int j = 0;
for (; j < pivot; j++)
{
// child.PermutationGenes[i][j] = mother.PermutationGenes[i][j];
values.Add(mother.PermutationGenes[i][j]);
}
// add all the values in the father, in order, except those already added from the mother
int k = 0;
while (j < len)
{
if (values.Contains(father.PermutationGenes[i][k]))
{
k++;
}
else
{
child.PermutationGenes[i][j] = father.PermutationGenes[i][k];
j++; k++;
}
}
}
else // no crossover, copy one parent
{
if (ExtendedRandom.NextUniform() < 0.5)
{
for (int j = 0; j < len; j++)
{
child.PermutationGenes[i][j] = mother.PermutationGenes[i][j];
}
}
else
{
for (int j = 0; j < len; j++)
{
child.PermutationGenes[i][j] = father.PermutationGenes[i][j];
}
}
}
}
return(child);
}
private static void ResetReal(Chromosome child, Parameters parameters)
{
// implemented for real valued encoding
for (int i = 0; i < child.NoGenes; i++)
{
if (ExtendedRandom.NextUniform() < _info.Probability)
{
child.RealGenes[i] = parameters.EncodingInfo.MinValues[i] + ExtendedRandom.NextUniform() *
(parameters.EncodingInfo.MaxValues[i] - parameters.EncodingInfo.MinValues[i]);
}
}
}
private static Chromosome Arithmetic(Chromosome mother, Chromosome father)
{
Chromosome child = new Chromosome(mother);
if (ExtendedRandom.NextUniform() < _info.Probability)
{
for (int i = 0; i < child.NoGenes; i++)
{
double point = ExtendedRandom.NextUniform();
child.RealGenes[i] = point * mother.RealGenes[i] + (1 - point) * father.RealGenes[i];
}
}
return(child);
}
private static Chromosome ArithmeticInteger(Chromosome mother, Chromosome father)
{
Chromosome child = new Chromosome(mother);
if (ExtendedRandom.NextUniform() < _info.Probability)
{
for (int i = 0; i < child.NoGenes; i++)
{
if (ExtendedRandom.NextUniform() < 0.5)
{
child.RealGenes[i] = father.RealGenes[i];
}
}
}
return(child);
}
private static void GaussianReal(Chromosome child, Parameters parameters)
{
// implemented for real valued encoding
for (int i = 0; i < child.NoGenes; i++)
{
if (ExtendedRandom.NextUniform() < _info.Probability)
{
child.RealGenes[i] = ExtendedRandom.NextNormal(child.RealGenes[i], _info.StandardDeviation);
if (child.RealGenes[i] < parameters.EncodingInfo.MinValues[i])
{
child.RealGenes[i] = parameters.EncodingInfo.MinValues[i];
}
if (child.RealGenes[i] > parameters.EncodingInfo.MaxValues[i])
{
child.RealGenes[i] = parameters.EncodingInfo.MaxValues[i];
}
}
}
}
private static Chromosome MultiplePoint(Chromosome mother, Chromosome father)
{
// implemented for binary encoding
Chromosome child = new Chromosome(mother); // for array allocation
for (int i = 0; i < child.NoGenes; i++)
{
int len = child.BinaryGenes[i].Length;
if (ExtendedRandom.NextUniform() < _info.Probability)
{
// generate crossover points
bool[] selected = new bool[len];
for (int j = 0; j < len; j++)
{
selected[j] = false;
}
for (int j = 0; j < _info.NoPoints; j++)
{
bool ok = false;
while (!ok)
{
int crtSel = (int)(ExtendedRandom.NextUniform() * len);
if (!selected[crtSel])
{
selected[crtSel] = true;
ok = true;
}
}
}
bool selMother = true; // switch between mother genes and father genes
string newGene = "";
for (int j = 0; j < len; j++)
{
if (selected[j])
{
selMother = !selMother;
}
if (selMother)
{
newGene += mother.BinaryGenes[i][j];
}
else
{
newGene += father.BinaryGenes[i][j];
}
}
child.BinaryGenes[i] = newGene;
}
else // no crossover, copy one parent
{
if (ExtendedRandom.NextUniform() < 0.5)
{
child.BinaryGenes[i] = mother.BinaryGenes[i];
}
else
{
child.BinaryGenes[i] = father.BinaryGenes[i];
}
}
}
return(child);
}
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;
}
}
}
