/// <summary>
///
/// </summary>
/// <param name="F"></param>
/// <param name="theta"></param>
/// <param name="vectorLength"></param>
/// <param name="iterationNumber"></param>
public static void CompactGeneticAlgorithm(targetFunctionDelegate F, double theta, int vectorLength, int iterationNumber)
{
int bestYetResult = 0;
int[] bestYetVector;
double[] p = InitialProbabilityVector(vectorLength);
int[] x1 = Population.RandomIndividual(p);
int[] x2 = Population.RandomIndividual(p);
int x1Result = F(x1);
int x2Result = F(x2);
int[] betterX, worseX;
int betterResult = 0;
using (TextWriter tw = new StreamWriter("wynikiCGA.csv", false))
{
for (int i = 0; i < iterationNumber; i++)
{
betterX = (x1Result <= x2Result) ? x2 : x1;
worseX = (x1Result <= x2Result) ? x1 : x2;
betterResult = (x1Result <= x2Result) ? x2Result : x1Result;
if (betterResult > bestYetResult)
{
bestYetVector = betterX;
bestYetResult = betterResult;
}
tw.WriteLine(String.Format("{0}\t{1}", i, x1Result));
for (int k = 0; k < vectorLength; k++)
{
if (betterX[k] == 1 && worseX[k] == 0)
p[k] += theta;
if (betterX[k] == 0 && worseX[k] == 1)
p[k] -= theta;
}
x1 = Population.RandomIndividual(p);
x2 = Population.RandomIndividual(p);
x1Result = F(x1);
x2Result = F(x2);
}
}
}
/// <summary>
///
/// </summary>
/// <param name="F"></param>
/// <param name="populationCount"></param>
/// <param name="M"></param>
public static void SimpleGeneticAlgorithm(targetFunctionDelegate F, int populationCount, int M)
{
double[] p = InitialProbabilityVector(populationCount);
int[][] population = new int[populationCount][];
int[] results = new int[populationCount];
for (int i = 0; i < populationCount; i++)
{
population[i] = Population.RandomIndividual(p);
results[i] = F(population[i]);
}
for (int i = 0; i < 1000; i++)
{
//BlockSelection(population, M);
//UniformCrossover(population);
}
}
