public static void SwapMutation(Solution <T> a, CreateNewSolution <T> createSolution, FitnessFunction <T> fitnessFunction, double mutationProbability)
{
Random r = new Random();
foreach (var c in a.Chromosomes)
{
for (int i = 0; i < c.GenesCount; i++)
{
// Probability od mutatnion / 2 - 2 genes affected for one mutation
if (mutationProbability / 2 < r.NextDouble())
{
continue;
}
int pos1 = r.Next(c.GenesCount);
int pos2 = r.Next(c.GenesCount);
T tmp = c.Genes[pos1];
c.Genes[pos1] = c.Genes[pos2];
c.Genes[pos2] = tmp;
}
}
a.Fitness = fitnessFunction(a);
}
public static void ReplaceMutation(Solution <T> a, CreateNewSolution <T> createSolution, FitnessFunction <T> fitnessFunction, double mutationProbability)
{
Random r = new Random();
Solution <T> n = createSolution();
var ae = a.Chromosomes.GetEnumerator();
var ne = n.Chromosomes.GetEnumerator();
while (ae.MoveNext() && ne.MoveNext())
{
var ac = ae.Current.Genes;
var nc = ne.Current.Genes;
for (int i = 0; i < ac.Count; i++)
{
// Probability od mutatnion
if (mutationProbability < r.NextDouble())
{
break;
}
ac[i] = nc[i];
}
}
a.Fitness = fitnessFunction(a);
}
public GeneticAlgorithm(Selection <T> selectionMethod, Crossover <T> crossoverMethod, Mutation <T> mutationMethod, FitnessFunction <T> fitnessFunction, CreateNewSolution <T> createNewSolution, AcceptResult <T> acceptResult, int population, double mutationProbability, int selectionCount, int eliteCount = 0)
{
if (population < 2)
{
throw new Exception("Population must be greater or equal 2.");
}
if (eliteCount >= population)
{
throw new Exception("EliteCount must be less than population.");
}
selection = selectionMethod;
crossover = crossoverMethod;
mutation = mutationMethod;
fitness = fitnessFunction;
createSolution = createNewSolution;
accept = acceptResult;
maxPopulation = population;
this.mutationProbability = mutationProbability;
this.selectionCount = selectionCount;
this.eliteCount = eliteCount;
}
