public SingleObjectivePopulation(OptimisationTemplate Template, GAParameters gaParameters, int populationSize, bool empty)
: base(Template, gaParameters, populationSize)
{
//initialising population chromosomes
chromosomes = new SingleObjectiveChromosome[populationSize];
for (int i = 0; i < Size; i++)
{
chromosomes[i] = new SingleObjectiveChromosome(Template, gaParameters, empty);
}
//initialising best chromosome
bestChromosome = new SingleObjectiveChromosome(Template, gaParameters, true);
}
public override bool ApplyOn()
{
//initialising selection operator
if (GAParameters.SelectionOprMethod == SelectionOprMethods.TournamentSelectionWithoutReplacement)
{
selectionOpr = new TournamentSelectionOprWithoutReplacement(OptimisationTemplate, GAParameters);
}
else if (GAParameters.SelectionOprMethod == SelectionOprMethods.TournamentSelectionWithReplacement)
{
}
//initialising crossover operator
if (GAParameters.CrossoverOprMethod == CrossoverOprMethods.OnePointCrossover)
{
crossoverOpr = new OnePointCrossoverOpr(OptimisationTemplate, GAParameters);
}
else if (GAParameters.CrossoverOprMethod == CrossoverOprMethods.TwoPointCrossover)
{
}
else if (GAParameters.CrossoverOprMethod == CrossoverOprMethods.UniformCrossover)
{
}
else if (GAParameters.CrossoverOprMethod == CrossoverOprMethods.SimulatedBinaryCrossover)
{
crossoverOpr = new SimulatedBinaryCrossoverOpr(OptimisationTemplate, GAParameters);
}
//initialising mutation operator
if (GAParameters.MutationOprMethod == MutationOprMethods.SelectiveMutation)
{
mutationOpr = new SelectiveMutationOpr(OptimisationTemplate, GAParameters);
}
else if (GAParameters.MutationOprMethod == MutationOprMethods.GenewiseMutation)
{
}
else if (GAParameters.MutationOprMethod == MutationOprMethods.PolynomialMutation)
{
mutationOpr = new PolynomialMutationOpr(OptimisationTemplate, GAParameters);
}
//initialising elitism operator
if (GAParameters.ElitismOprMethod == ElitismOprMethods.ProportionalElitism)
{
//this.elitismOpr = new ProportionalElitismOpr(Template, gaParameters);
}
else if (GAParameters.ElitismOprMethod == ElitismOprMethods.UnproportionalElitism)
{
//this.elitismOpr = new UnproportionalElitismOpr(Template, gaParameters);
}
var bestChromosome = new SingleObjectiveChromosome(OptimisationTemplate, GAParameters, false);
string result = "";
//for (int j = 0; j < this.BestChromosome.Genes.Length; j++)
//result += this.BestChromosome.Genes[j] + "\t";
//result += this.BestChromosome.ObjectiveValue + "\t";
//for (int j = 0; j < this.BestChromosome.ConstraintViolationValues.Length; j++)
//result += this.BestChromosome.ConstraintViolationValues[j] + "\t";
System.Console.WriteLine(result);
parentPopulation = new SingleObjectivePopulation(OptimisationTemplate, GAParameters, false);
childPopulation = new SingleObjectivePopulation(OptimisationTemplate, GAParameters, false);
TextWriter solutionsFileWriter = new StreamWriter(GAParameters.EvaluatedSolutionsFile);
int generationID = 0;
parentPopulation.Evaluate();
/*
* System.Console.WriteLine("after evaluating population");
* System.Console.WriteLine(this.parentPopulation);
* System.Console.WriteLine(this.childPopulation);
*/
//writing to console and text file
System.Console.WriteLine("Generation: " + generationID);
double[] x;
double y;
double[] c;
result = "";
for (int i = 0; i < parentPopulation.Size; i++)
{
x = ((SingleObjectiveChromosome)(parentPopulation[i])).Genes;
y = ((SingleObjectiveChromosome)(parentPopulation[i])).ObjectiveValue;
c = ((SingleObjectiveChromosome)(parentPopulation[i])).ConstraintViolationValues;
result = "";
result += y;
System.Console.WriteLine(result);
result = "";
for (int j = 0; j < x.Length; j++)
{
result += x[j] + "\t";
}
result += y + "\t";
for (int j = 0; j < c.Length; j++)
{
result += c[j] + "\t";
}
solutionsFileWriter.WriteLine(result);
System.Console.WriteLine(result);
}
System.Console.WriteLine("");
while (generationID < GAParameters.NoOfGenerations)
{
//selection
int[] matingPoolIndex = selectionOpr.ApplySelectionOpr(parentPopulation);
/*
* for (int i = 0; i < matingPoolIndex.Length; i++)
* System.Console.Write(matingPoolIndex[i] + "***");
*/
for (int i = 0; i < parentPopulation.Size; i++)
{
childPopulation[i] = (SingleObjectiveChromosome)parentPopulation[matingPoolIndex[i]];
}
/*
* System.Console.WriteLine("after selection");
* System.Console.WriteLine(this.parentPopulation);
* System.Console.WriteLine(this.childPopulation);
*/
//crossover
for (int i = 0; i < parentPopulation.Size; i += 2)
{
if (GARandom.Flip(GAParameters.CrossoverProbability))
{
crossoverOpr.ApplyCrossoverOpr(childPopulation[i], childPopulation[i + 1]);
}
}
/*
* System.Console.WriteLine("after crossover");
* System.Console.WriteLine(this.parentPopulation);
* System.Console.WriteLine(this.childPopulation);
*/
//mutation
for (int i = 0; i < parentPopulation.Size; i++)
{
//if (GARandom.coinFlip(gaSet.getMutationProbability())) {
mutationOpr.ApplyMutationOpr(childPopulation[i]);
//}
}
/*
* System.Console.WriteLine("after mutation");
* System.Console.WriteLine(this.parentPopulation);
* System.Console.WriteLine(this.childPopulation);
*/
childPopulation.Evaluate();
/*
* System.Console.WriteLine("after evaluating childPopulation");
* System.Console.WriteLine(this.parentPopulation);
* System.Console.WriteLine(this.childPopulation);
*/
//this.newPopulation.computeObjectiveStatistics();
//this.newPopulation.computeFitnessStatistics();
for (int i = 0; i < childPopulation.Size; i++)
{
x = ((SingleObjectiveChromosome)(childPopulation[i])).Genes;
y = ((SingleObjectiveChromosome)(childPopulation[i])).ObjectiveValue;
c = ((SingleObjectiveChromosome)(childPopulation[i])).ConstraintViolationValues;
result = "";
for (int j = 0; j < x.Length; j++)
{
result += x[j] + "\t";
}
result += y + "\t";
for (int j = 0; j < c.Length; j++)
{
result += c[j] + "\t";
}
solutionsFileWriter.WriteLine(result);
}
bool useElitism = true;
if (useElitism)
{
var elitismOpr = new ProportionalElitismOpr(OptimisationTemplate, GAParameters, parentPopulation, childPopulation);
elitismOpr.ApplyElitismOpr();
}
else
{
}
/*
* System.Console.WriteLine("after elitism operator");
* System.Console.WriteLine(this.parentPopulation);
* System.Console.WriteLine(this.childPopulation);
*/
parentPopulation.Evaluate();
/*
* System.Console.WriteLine("after elitism operator and then evaluating");
* System.Console.WriteLine(this.parentPopulation);
* System.Console.WriteLine(this.childPopulation);
*/
//this.parentPopulation.ComputeObjectiveStatistics();
//this.parentPopulation.ComputeFitnessStatistics();
/*
* generationID++;
* System.Console.WriteLine("Generation: " + generationID);
* fileWriter.WriteLine("Generation: " + generationID);
* System.Console.WriteLine("Best Solution:");
* fileWriter.WriteLine("Best Solution:");
* parentPopulation.ComputeBestChromosome();
* System.Console.WriteLine(parentPopulation.BestChromosome);
* fileWriter.WriteLine(parentPopulation.BestChromosome);
*/
generationID++;
//writing to console
System.Console.WriteLine("Generation: " + generationID);
// Best chromosome
System.Console.WriteLine("Best Solution:");
parentPopulation.ComputeBestChromosome();
parentPopulation.GetBestChromosome().EvaluateObjective();
parentPopulation.GetBestChromosome().EvaluateConstraints();
x = parentPopulation.GetBestChromosome().Genes;
y = parentPopulation.GetBestChromosome().ObjectiveValue;
c = parentPopulation.GetBestChromosome().ConstraintViolationValues;
result = "";
for (int j = 0; j < x.Length; j++)
{
result += x[j] + "\t";
}
result += y + "\t";
for (int j = 0; j < c.Length; j++)
{
result += c[j] + "\t";
}
System.Console.WriteLine(result);
System.Console.WriteLine("");
}
solutionsFileWriter.Close();
return(true);
}
