private void ChangeVector(VariablesVector variablesVector)
{
variablesVector.IsActualF = false;
int[] variables = variablesVector.Variables;
for (int i = 0; i < m_dimentionsNumber; i++)
{
ChangeVariable(ref variables[i]);
}
}
VariablesVector[] createFirtsPopulation()
{
Random random = new Random();
VariablesVector[] result = new VariablesVector[m_populationLength];
for (int i = 0; i < m_populationLength; i++)
{
VariablesVector person = new VariablesVector(m_variablesVectorLength);
person.RandomizeVariables(random, m_maxValue);
result[i] = person;
}
return(result);
}
public static VariablesVector[] Twist(List <RouletteElement> rouletteElements)
{
Random random = new Random();
int neededVariablesVectorLength = 2; //amount of persons
VariablesVector[] result = new VariablesVector[neededVariablesVectorLength];
for (int i = 0; i < neededVariablesVectorLength; i++)
{
double r = random.NextDouble();
result[i] = rouletteElements.Find(el => r >= el.BottomProbability && r < el.TopProbability).VariablesVector;
}
return(result);
}
public VariablesVector[] TwoPointCrossing(VariablesVector[] variablesVectors)
{
VariablesVector a = variablesVectors[0];
VariablesVector b = variablesVectors[1];
VariablesVector[] result = new[] { new VariablesVector(m_dimentionsNumber), new VariablesVector(m_dimentionsNumber) };
for (int dimNumber = 0; dimNumber < m_dimentionsNumber; dimNumber++)
{
//получение результата скрещивания по одному измерению
int[] crossingInDimentionResult = CrossTwoVariables(a.Variables[dimNumber], b.Variables[dimNumber]);
//переносим это в массив результатов
AssingCrossingResultToDimention(result, crossingInDimentionResult, dimNumber);
}
return(result);
}
public static RouletteElement[] formRouletteElements(List <VariablesVector> population)
{
RouletteElement[] result = new RouletteElement[population.Count];
double sumOfFitnessFunctionOfAllPersnons = population.Sum(el => el.F);
double accumulativeSumOfProbability = 0;
for (int i = 0; i < population.Count; i++)
{
VariablesVector currentPerson = population[i];
double probability = currentPerson.F / sumOfFitnessFunctionOfAllPersnons;
double topProbability = accumulativeSumOfProbability + probability;
result[i] = new RouletteElement(currentPerson,
accumulativeSumOfProbability,
topProbability);
accumulativeSumOfProbability = topProbability;
}
return(result);
}
public RouletteElement(VariablesVector variablesVector, double bottomProbability, double topProbability)
{
this.VariablesVector = variablesVector;
this.BottomProbability = bottomProbability;
this.TopProbability = topProbability;
}
public double Maximize()
{
int positionOfMaxBit = Service.getPositionOfMaxBit(m_maxValue);
Cross crossObj = new Cross(positionOfMaxBit, m_variablesVectorLength);
Mutation mutationObj = new Mutation(positionOfMaxBit, m_variablesVectorLength, m_mutationPercent);
HardMutation hardMutationObj = new HardMutation(positionOfMaxBit, m_variablesVectorLength, m_hardMutationPercent);
List <VariablesVector> population = createFirtsPopulation().ToList();
CalcFValueForPopulation(population);
population = population.OrderByDescending(el => el.F).ToList();
processing?.Invoke(this, new GeneticAlgorithmEventArgs(-1, population[0].F, population.Average(el => el.F)));
int bestPersonsLength = Math.Max(1, (int)(m_populationLength * m_bestPersonsPart));
for (int iterations = 0; iterations < m_iterationsLimit; iterations++)
{
//form the roulette
RouletteElement[] rouletteElements = Roulette.formRouletteElements(population);
//crossing
for (int i = 0; i < m_populationLength / 8; i++)
{
VariablesVector[] selectedByRoulette = Roulette.Twist(rouletteElements.ToList());
VariablesVector[] descendants = crossObj.TwoPointCrossing(selectedByRoulette);
population.AddRange(descendants);
}
population = population.OrderByDescending(el => el.F).ToList();
List <VariablesVector> populationWithoutBestPersons = population.Skip(bestPersonsLength).ToList();
//and mutation
mutationObj.DoMutation(populationWithoutBestPersons);
//hard Mutation
if (iterations > 0 && iterations % m_hardMutaionFriquency == 0)
{
hardMutationObj.DoMutation(populationWithoutBestPersons);
}
CalcFValueForPopulation(population.Where(el => !el.IsActualF).ToList());
//leaving the best variants
population = population.OrderByDescending(el => el.F).Take(m_populationLength).ToList();
//debug
double F = population.Max(el => el.F);
processing?.Invoke(this, new GeneticAlgorithmEventArgs(iterations, F, population.Average(el => el.F)));
}
double max = population.Max(el => el.F);
VariablesVector withMax = population.Find(el => el.F == max);
return(max);
}
