protected override void PerformMutate(IChromosome chromosome, float probability)
{
Random r = new Random();
if (r.NextDouble() <= probability)
{
var test = chromosome as Test;
var possibleQuesitons = dataAccess.GetQuestionsWhichContainDomains(oblasti);
var fFunc = new TestFitness(oblasti, zastupljenost, 0);
double fitness = fFunc.Evaluate(test);
int bestIndex = -1;
int qIndex;
do
{
qIndex = r.Next(possibleQuesitons.Count);
for (int index = 0; index < test.questions.Count; index++)
{
var q = test.CreateNew() as Test;
q.questions[index] = possibleQuesitons[qIndex];
if (!q.HasDuplicate() && fFunc.Evaluate(q) >= fitness)
{
bestIndex = index;
}
}
} while (bestIndex == -1);
test.questions[bestIndex] = possibleQuesitons[qIndex];
}
}
protected override IList <IChromosome> PerformCross(IList <IChromosome> parents)
{
List <IChromosome> tests = new List <IChromosome>();
Random r = new Random();
for (int i = 0; i < ParentsNumber - 1; i += 2)
{
Test test1 = parents[i] as Test;
Test test2 = parents[i + 1] as Test;
List <Question> allQuestions = new List <Question>();
allQuestions.AddRange(test1.questions);
allQuestions.AddRange(test2.questions);
allQuestions = allQuestions.Distinct().ToList();
Test bestChild = null;
TestFitness fFunc = new TestFitness(oblasti, zastupljenost, 0);
int BrojPermutacija = test1.questions.Count * 50;
for (int j = 0; j < BrojPermutacija; j++)
{
MiSe.Shuffle.ShuffleOps.ShuffleInPlace(allQuestions, r);
Test temp = new Test(allQuestions.Take(test1.questions.Count).ToList(), test1.questions.Count);
if (bestChild == null)
{
bestChild = temp;
}
else
{
if (fFunc.Evaluate(temp) > fFunc.Evaluate(bestChild))
{
bestChild = temp;
}
}
}
tests.Add(bestChild);
}
return(tests);
}
public Test UseAlgorithm(List <int> oblasti, List <double> zastupljenost, int Test_Length)
{
Random r = new Random(10);
IDataAccess dataAccess = DataAccess.DataAccess.GetInstance();
List <Question> qs = new List <Question>();
for (int i = 0; i < Test_Length; i++)
{
qs.Add(dataAccess.GetQuestionById(1 + r.Next(1000)));
}
Test adamTest = new Test(qs, Test_Length);
List <double> zastupljenost_kao_br_pitanja = new List <double>();
foreach (var vrednost in zastupljenost)
{
zastupljenost_kao_br_pitanja.Add(Math.Round(vrednost * Test_Length + 0.01));
}
var fitness = new TestFitness(oblasti, zastupljenost_kao_br_pitanja, 0);
IGenerationStrategy generationStrategy = new PerformanceGenerationStrategy(3);
var population = new TestPopulation(20, 50, adamTest, oblasti)
{
GenerationStrategy = generationStrategy
};
ISelection selection = new EliteSelection();
ICrossover crossover = new TestCrossover(oblasti, zastupljenost_kao_br_pitanja, 8, 4);
IMutation mutation = new TestMutation(oblasti, zastupljenost_kao_br_pitanja);
ITermination termination = new OrTermination(new ITermination[] { new TestTermination(100, oblasti),
new FitnessThresholdTermination(oblasti.Count),
new GenerationNumberTermination(3000) });
GeneticAlgorithm ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = termination,
CrossoverProbability = 1f,
MutationProbability = 0.7f
};
double oldFitness = 0;
List <Test> tests = new List <Test>();
//Callback
ga.GenerationRan += (sender, arg) =>
{
if (ga.BestChromosome.Fitness >= oldFitness)
{
oldFitness = ga.BestChromosome.Fitness.Value;
tests.Add(ga.BestChromosome as Test);
//Console.WriteLine($"Current fitness: {ga.BestChromosome.Fitness} for generation : {ga.GenerationsNumber}");
}
};
ga.Start();
if (oldFitness == 0)
{
return(null);
}
var lista = tests.Where(x => !x.HasDuplicate()).OrderByDescending(x => x.Fitness.Value).ToList();
Test bestChromosome = lista.FirstOrDefault();
foreach (var q in bestChromosome.questions)
{
q.Izabrano = true;
}
return(bestChromosome);
}