public Individual RunSimulation(int maxNumberOfGenerations)
{
ResetSimulations();
for (int i = 0; i < maxNumberOfGenerations; i++)
{
var parents = _selectionOperator.GenerateParentPopulation(_population);
for (int j = 0; j < _numberOfIndividuals - 1; j += 2)
{
if (_random.NextDouble() < CrossoverProbability)
{
_crossOperator.Crossover(parents[j], parents[j + 1]);
_mutationOperator.Mutation(parents[j], MutationProbability);
_mutationOperator.Mutation(parents[j + 1], MutationProbability);
}
}
_population = parents;
UpdateFitness();
if (PrintStatistics)
{
Console.WriteLine($"Generation: {i}");
Console.WriteLine($"The best is: x = {TakeTheBest().Chromosome.DecodedValue}\tf = {TakeTheBest().Fitness}");
}
}
return(TakeTheBest());
}
public Individual RunSimulation(int maxNumberOfGenerations)
{
ResetSimulations();
for (int i = 0; i < maxNumberOfGenerations; i++)
{
//System.Diagnostics.Stopwatch sw = new System.Diagnostics.Stopwatch();
//sw.Start();
var parents = _selectionOperator.GenerateParentPopulation(_population);
//sw.Stop();
//Console.WriteLine(sw.ElapsedMilliseconds);
Parallel.For(0, _numberOfIndividuals / 2, j =>
{
if (_random.NextDouble() < CrossoverProbability)
{
int index = j * 2;
_crossOperator.Crossover(parents[index], parents[index + 1]);
_mutationOperator.Mutation(parents[index], MutationProbability);
_mutationOperator.Mutation(parents[index + 1], MutationProbability);
}
});
//for (int j = 0; j < _numberOfIndividuals - 1; j += 2)
//{
// if (_random.NextDouble() < CrossoverProbability)
// {
// _crossOperator.Crossover(parents[j], parents[j + 1]);
// _mutationOperator.Mutation(parents[j], MutationProbability);
// _mutationOperator.Mutation(parents[j + 1], MutationProbability);
// }
//}
_population = parents;
UpdateFitness();
if (PrintStatistics)
{
Console.WriteLine($"Generation: {i}");
Console.WriteLine($"The best is: x = {TakeTheBest().Chromosome.DecodedValue}\tf = {TakeTheBest().Fitness}");
}
}
return(TakeTheBest());
}
public Individual RunSimulation(int maxNumberOfGenerations)
{
ResetSimulations();
for (int i = 0; i < maxNumberOfGenerations; i++)
{
var parents = _selectionOperator.GenerateParentPopulation(_population);
for (int j = 0; j < _numberOfIndividuals - 1; j += 2)
{
if (_random.NextDouble() < CrossoverProbability)
{
_crossOperator.Crossover(parents[j], parents[j + 1]);
if (_random.NextDouble() < MutationProbability)
{
_mutationOperator.Mutation(parents[j], MutationProbability);
}
if (_random.NextDouble() < MutationProbability)
{
_mutationOperator.Mutation(parents[j + 1], MutationProbability);
}
}
}
_population = parents;
UpdateFitness();
}
return(_population
.OrderByDescending(x => x.Fitness)
.FirstOrDefault());
}
public Individual RunSimulation(int maxNumberOfGenerations)
{
ResetSimulations();
// Sekwencyjnie
//for (int i = 0; i < maxNumberOfGenerations; i++)
//{
// var parents = _selectionOperator.GenerateParentPopulation(_population);
// for (int j = 0; j < _numberOfIndividuals - 1; j += 2)
// {
// if (_random.NextDouble() < CrossoverProbability)
// {
// _crossOperator.Crossover(parents[j], parents[j + 1]);
// _mutationOperator.Mutation(parents[j], MutationProbability);
// _mutationOperator.Mutation(parents[j + 1], MutationProbability);
// }
// }
// _population = parents;
// UpdateFitness();
// if (PrintStatistics)
// {
// Console.WriteLine($"Generation: {i}");
// Console.WriteLine($"The best is: x = {TakeTheBest().Chromosome.DecodedValue}\tf = {TakeTheBest().Fitness}");
// }
//}
// Parallel
for (int i = 0; i < maxNumberOfGenerations; i++)
{
var parents = _selectionOperator.GenerateParentPopulation(_population);
Parallel.For(0, _numberOfIndividuals - 1, j =>
{
if (_random.NextDouble() < CrossoverProbability)
{
_crossOperator.Crossover(parents[j], parents[j + 1]);
_mutationOperator.Mutation(parents[j], MutationProbability);
if (j == _numberOfIndividuals - 2)
{
_mutationOperator.Mutation(parents[j + 1], MutationProbability);
}
}
});
_population = parents;
UpdateFitness();
if (PrintStatistics)
{
Console.WriteLine($"Generation: {i}");
Console.WriteLine($"The best is: x = {TakeTheBest().Chromosome.DecodedValue}\tf = {TakeTheBest().Fitness}");
}
}
// Taski
//for (int i = 0; i < maxNumberOfGenerations; i++)
//{
// var parents = _selectionOperator.GenerateParentPopulation(_population);
// double seed = 10000;
// double tasks = (_population.Length / seed);
// int amountOfTask = (int)Math.Ceiling(tasks);
// Task[] t = new Task[amountOfTask];
// int LeftC = 0;
// int RightC = (int)seed;
// for (int k = 0; k < amountOfTask; k++)
// {
// if (k == amountOfTask - 1)
// {
// RightC = _population.Length - 1;
// t[k] = GeneticOperations(LeftC, RightC, parents);
// }
// else
// {
// t[k] = GeneticOperations(LeftC, RightC, parents);
// LeftC = RightC;
// RightC += (int)seed;
// }
// }
// for (int k = 0; k < amountOfTask; k++)
// {
// t[k].Wait();
// }
// _population = parents;
// UpdateFitness();
// if (PrintStatistics)
// {
// Console.WriteLine($"Generation: {i}");
// Console.WriteLine($"The best is: x = {TakeTheBest().Chromosome.DecodedValue}\tf = {TakeTheBest().Fitness}");
// }
//}
return(TakeTheBest());
}