public void DisplayPopulation(int[,] population, Fitness fitness, String selectorName = null, String crossoverName = null, Double rate = 0)
{
var fitnessNo = fitness.GetFitnessMethod();
var fitnessIndex = _genomeLength;
fitness.ResetTotalPoints();
_topGenerationCandidate = 0;
//Console.WriteLine(message);
Console.WriteLine("--------------------");
Console.WriteLine("Fitness Function: " + fitness.GetFitnessName());
if (selectorName != null)
{
Console.WriteLine("Selection Function: " + selectorName);
}
if (crossoverName != null)
{
Console.WriteLine("Crossover Operator: " + crossoverName);
}
if (rate > 0)
{
Console.WriteLine("Mutation Rate: " + rate);
}
Console.WriteLine("--------------------");
for (var i = 0; i < _populationSize; i++)
{
Console.Write("Genome " + (i + 1) + ": ");
fitness.ResetRankPoints();
for (var j = 0; j < _genomeLength; j++)
{
Console.Write(population[i, j] + " ");
//Only for Digit Ranking
if (fitnessNo == 1)
{
fitness.AddRankPoints(population[i, j]);
//population[i, fitnessIndex] = fitness.GetRankPoints();
}
else if (fitnessNo == 2 && population[i, j] == 1)
{
fitness.AddRankPoints(fitness.CalculateBinaryFitness(_genomeLength, j));
}
}
Console.Write("Fitness: " + fitness.GetRankPoints());
population[i, fitnessIndex] = fitness.GetRankPoints();
if (population[i, fitnessIndex] > _topGenerationCandidate)
{
_topGenerationCandidate = fitness.GetRankPoints();
}
Console.WriteLine();
}
if (_topGenerationCandidate > _topCycleCandidate)
{
_topCycleCandidate = _topGenerationCandidate;
}
Console.WriteLine();
Console.WriteLine("Total Fitness: " + fitness.GetTotalPoints());
Console.WriteLine("Average Fitness: " + fitness.CalculateAverageFitness(_populationSize));
Console.WriteLine("Top Candidate: " + _topGenerationCandidate);
Console.WriteLine("Best Cycle Candidate: " + _topCycleCandidate);
}
public static void Main(string[] args)
{
var optionsNo = 0;
Console.WriteLine("*****START*******");
Console.WriteLine();
Initializer initializer = new Initializer();
Fitness fitness = new Fitness();
Selector selector = new Selector();
Crossover crossover = new Crossover();
StartUp();
while (optionsNo != 9)
{
switch (DisplayMenu())
{
case 1:
Console.WriteLine("GENERATION 1");
initializer.DisplayPopulation(initializer.GetPopulation(), fitness);
break;
case 2:
FullCycle();
break;
case 3:
fitness.SetFitnessMethod();
initializer.DisplayPopulation(initializer.GetPopulation(), fitness);
Wait();
break;
case 8:
initializer.CreatePopulation();
initializer.DisplayPopulation(initializer.GetPopulation(), fitness);
Wait();
break;
case 9:
Console.WriteLine("This Program is over. Bye!");
break;
default:
Console.WriteLine("Try Again!");
break;
}
}
void StartUp()
{
initializer.CreatePopulation();
fitness.SetFitnessMethod();
initializer.DisplayPopulation(initializer.GetPopulation(), fitness);
Wait();
}
int DisplayMenu()
{
Console.WriteLine();
Console.WriteLine("OPTIONS: ");
Console.WriteLine("------------------");
Console.WriteLine("1- Display Population");
Console.WriteLine("2- Full Cycle");
Console.WriteLine("3- Change Fitness Function");
Console.WriteLine("8- Re-Initiation");
Console.WriteLine("9- Exit");
Console.WriteLine();
optionsNo = initializer.CheckIntegerInput("Please Select an Option:");
return(optionsNo);
}
void Wait()
{
Console.WriteLine();
Console.WriteLine("Next...");
Console.ReadLine();
}
void FullCycle()
{
//Set Up Generation Cycle and Parameters
var isPhaseSkip = false;
var isFound = false;
var inputNo = 0;
initializer.ResetStats();
selector.SetNoOfCycles();
selector.SetSelectionType();
crossover.SetCrossoverMethod();
selector.SetMutationRate();
while (isFound == false)
{
Console.WriteLine("Would you like to skip the individual steps on each generations (Selection, Mutation, etc) and just the final results of each generation ? ");
Console.WriteLine("1 - Yes");
Console.WriteLine("2 - No");
inputNo = initializer.CheckIntegerInput();
if (inputNo == 1 || inputNo == 0)
{
isFound = true;
if (inputNo == 1)
{
isPhaseSkip = true;
}
}
else
{
Console.WriteLine("Please choose a enter option");
}
}
//For Each Cycle
for (int i = 0; i < selector.GetNotOfCycles(); i++)
{
Console.WriteLine("GENERATION " + (i + 1));
//Selection Phase
if (selector.GetSelectionType() == 2)
{
Selector.OrderSamples(initializer.GetPopulation(), initializer.GetGenomeLength(), initializer.GetPopulationSize());
initializer.SetSelectionCandidates(Selector.RankSelection(initializer));
}
//Roulette Wheel Selection
else
{
initializer.SetSelectionCandidates(Selector.RouletteWheel(initializer, fitness.GetTotalPoints()));
}
if (isPhaseSkip == false)
{
//Display Selected Candidates
Console.WriteLine("After Selection...");
initializer.DisplayPopulation(initializer.GetPopulation(), fitness, selector.GetSelectionName(), crossover.GetCrossoverName(), selector.GetMutationRate());
Wait();
}
//Reproduction Phase
if (crossover.GetCrossoverMethod() == 1)
{
//Single Point Crossover
initializer.SetSelectionCandidates(crossover.SinglePointCrossover(initializer));
}
else
{
//Uniform Crossover
initializer.SetSelectionCandidates(crossover.UniformCrossover(initializer));
}
if (isPhaseSkip == false)
{
Console.WriteLine("After Crossover");
initializer.DisplayPopulation(initializer.GetPopulation(), fitness, selector.GetSelectionName(), crossover.GetCrossoverName(), selector.GetMutationRate());
Wait();
}
initializer.SetSelectionCandidates(selector.Mutation(initializer));
if (isPhaseSkip == false)
{
//Mutation Phase
Console.WriteLine("After Mutation...");
initializer.DisplayPopulation(initializer.GetPopulation(), fitness, selector.GetSelectionName(), crossover.GetCrossoverName(), selector.GetMutationRate());
Wait();
}
if (isPhaseSkip)
{
initializer.DisplayPopulation(initializer.GetPopulation(), fitness, selector.GetSelectionName(), crossover.GetCrossoverName(), selector.GetMutationRate());
Wait();
}
//Q FOR QUIT
}
initializer.ResetStats();
}
}
