public Individual(Individual i)
{
this.chromosome_ = new Chromosome(i.chromosome);
this.strategy_ = i.strategy;
this.isFeasible_ = i.isFeasible;
this.fitness_ = i.fitness;
}
public void Add(Individual individual)
{
Individual i = new Individual(individual);
population.Add(i);
}
int Compare(Individual x, Individual y)
{
if (x.fitness > y.fitness)
return -1;
else if (x.fitness == y.fitness)
return 0;
else
return 1;
}
protected Tuple<Individual, Individual> crossover(Individual ind1, Individual ind2, int index1, int index2)
{
// we forgot about crossover probability
if (GA_GT.random.NextDouble() < GA_GT.crossoverRate)
{
for (int i = index1; i <= index2; i++)
{
bool temp = ind1.chromosome[i];
ind1.chromosome[i] = ind2.chromosome[i];
ind2.chromosome[i] = temp;
}
}
return new Tuple<Individual, Individual>(ind1, ind2);
}
public Population UniformCrossover(Population parents)
{
Population offspring = new Population();
List<int> unused = new List<int>();
for (int i = 0; i < parents.Count; i++)
{
unused.Add(i);
}
int chromosomeSize = getChromosomeSize();
for (int i = 0; i < parents.Count; i++)
{
if (!unused.Contains(i))
continue;
unused.Remove(i);
int randomIndex = unused.ElementAt(GA_GT.random.Next() % unused.Count);
unused.Remove(randomIndex);
if (GA_GT.random.NextDouble() >= GA_GT.crossoverRate)
{
offspring.Add(parents.getIndividual(i));
offspring.Add(parents.getIndividual(randomIndex));
}
else
{
Chromosome ch1 = new Chromosome(chromosomeSize);
Chromosome ch2 = new Chromosome(chromosomeSize);
for (int j = 0; j < chromosomeSize; j++)
{
if (GA_GT.random.NextDouble() < 0.5)
{
ch1[j] = parents.getIndividual(i)[j];
ch2[j] = parents.getIndividual(randomIndex)[j];
}
else
{
ch1[j] = parents.getIndividual(randomIndex)[j];
ch2[j] = parents.getIndividual(i)[j];
}
}
Individual ind1 = new Individual(ch1);
ind1.strategy = parents.getIndividual(i).strategy;
ind1.Update(GA_GT.knapsackList);
Individual ind2 = new Individual(ch2);
ind2.strategy = parents.getIndividual(randomIndex).strategy;
ind2.Update(GA_GT.knapsackList);
offspring.Add(ind1);
offspring.Add(ind2);
}
}
return offspring;
}
public void RandomPopulation(double cheaterRate, int chromosomeSize, int populationSize)
{
int numberOfCheaters = (int)(populationSize * cheaterRate);
for (int i = 0; i < populationSize; i++)
{
Chromosome chromosome = new Chromosome(chromosomeSize);
Individual temp;
if (i < numberOfCheaters)
temp = new Individual(chromosome, false, (double)0, true); // cheater
else
temp = new Individual(chromosome, true, (double)0, true); // cooperator
population.Add(temp);
}
}
public static void WriteOutput(string path, Individual ind)
{
Thread.CurrentThread.CurrentCulture = new CultureInfo("en-US", false); //dots instead of commas as decimal marks
//StreamWriter(path) to overwrite the file, StreamWriter(path, true) to append
using (StreamWriter sw = new StreamWriter(path, true))
{
sw.Write(ind.Value + " " + ind.fitness + " " + ind.strategy + " ");
for (int i = 0; i < ind.chromosome.Count; i++)
{
if (ind[i])
sw.Write("1 ");
else
sw.Write("0 ");
}
sw.WriteLine();
}
}
public double FitnessValue(Individual secondInd, KnapsackList knapsackList)
{
bool strategy1 = this.strategy;
bool strategy2 = secondInd.strategy;
if (strategy1)
{
if (strategy2)
{
return fitnessCooperativeCooperative(knapsackList);
}
else
{
return fitnessCooperativeDefector(knapsackList);
}
}
else
{
if (strategy2)
{
return fitnessDefectorCooperative(knapsackList);
}
else
{
return fitnessDefectorDefector(knapsackList);
}
}
}
