public override string ToString()
{
string result = "";
result += FeaturesToString();
result += " Emotions: " + EmotionsToString() + " EDA: " + EDA.ToString() + " Emotion: " + Emotion + ";";
return(result);
}
static void Main(string[] args)
{
#region ReadingData
int cities;
cities = Convert.ToInt32(Console.ReadLine());
int arcs = cities * cities - cities;
Queue <Tuple <int, int, int> > inputData = new Queue <Tuple <int, int, int> >();
for (int i = 0; i < arcs; i++)
{
//Very bad practice. Bad Fernando, bad.
inputData.Enqueue(Tuple.Create(Convert.ToInt32(Console.ReadLine()), Convert.ToInt32(Console.ReadLine()), Convert.ToInt32(Console.ReadLine())));
}
#endregion
//Creating Graph
EvalFunctions nue = new EvalFunctions(200, 200, 200);
Graph chart = new Graph(inputData, cities);
Random rd = new Random();
ParetoFront frontier = new ParetoFront();
int eda_proportion = (int)(0.6 * cities * 4);
int elitism_proportion = (int)(0.1 * cities * 4);
int random_proportion = elitism_proportion;
int sa_proportion = (int)(0.2 * cities * 4);
Population current_population, next_population;
current_population = new Population(cities * 4);
current_population.CreatePopulation(cities);
for (int generations = 0; generations < 100; generations++)
{
next_population = new Population(cities * 4);
EDA new_distribution;
SimulatedAnnealing new_process;
//Estimating new distribution;
new_distribution = new EDA(current_population.GetSample(cities));
//Individuals created with EDA
for (int i = 0; i < eda_proportion; i++)
{
next_population.Inhabitants[i] = new_distribution.CreateIndividual();
}
//Individuals created with Simulated Annealing.
for (int i = 0; i < sa_proportion; i++)
{
new_process = new SimulatedAnnealing(
current_population.Inhabitants[rd.Next() % current_population.Size]
);
new_process.Cooling(2000, 3);
next_population.Inhabitants[i + eda_proportion] = new_process.historical_best_solution;
}
//Individuals created with Knuth Algorithm.
for (int i = 0; i < random_proportion; i++)
{
int[] new_path;
new_path = current_population.Inhabitants[current_population.Size - i - 1].Path.GetRandomPermutation();
next_population.Inhabitants[i + eda_proportion + sa_proportion] = new Individual(new_path);
var fitness = EvalFunctions.GetFitness(next_population.Inhabitants[i + eda_proportion + sa_proportion]);
next_population.Inhabitants[i + eda_proportion + sa_proportion].FitnessD = fitness.Item1;
next_population.Inhabitants[i + eda_proportion + sa_proportion].FitnessT = fitness.Item2;
next_population.Inhabitants[i + eda_proportion + sa_proportion].FitnessC = fitness.Item3;
next_population.Inhabitants[i + eda_proportion + sa_proportion].Fitness = fitness.Item4;
}
//Individuals selected with elitism
for (int i = 1; i <= elitism_proportion; i++)
{
next_population.Inhabitants[next_population.Size - i] = current_population.Inhabitants[current_population.Size - i];
}
//Making next population the current population.
current_population = next_population;
current_population.Sort();
//Updating Pareto's front.
frontier.UpdateFront(current_population.Inhabitants.OrderBy(i => i.FitnessC).Last());
frontier.UpdateFront(current_population.Inhabitants.OrderBy(i => i.FitnessD).Last());
frontier.UpdateFront(current_population.Inhabitants.OrderBy(i => i.FitnessT).Last());
}
foreach (Individual i in frontier.front)
{
Console.WriteLine("{0}\n\n", i.ToString());
}
Console.WriteLine();
}
