Genetic algorithm Demo

Using System;
Using System.Collections.Generic;
Using System.Text;

Namespace Gene
{
Class Intelligenttestsystem
{
Public

static int population=100; Number of population
static int maxnumber = 2; The number of genes used per individual (chromosome)

double[,] chromosome = new double[population, MaxNumber]; The population of each individual (chromosome)

double[] Fitness = new Double[population]; Degree of adaptability

int questiontypenumber = 0;
int[] Questiontype = new Int[maxnumber];

double[] SampleX = new DOUBLE[10];

double[] Sampley = new DOUBLE[10];

public void initial ()
{
Random rand = new Random (Unchecked (5 * (int) DateTime.Now.Ticks));

for (int i = 0; i < population; i++)//randomly generated chromosomes
{
for (int j = 0; J < 2; J + +)
{
Chromosome[i, J] = (rand. Next (200)-100)/10.0;
}
}

for (int i = 0; i < x; i++)//function 10 coordinate points y=3*sin (x*x) +5*x;
{
Samplex[i] = i;
Sampley[i] = 3 * Math.sin (samplex[i] * samplex[i]) + 5 * samplex[i];
}

}

public double calculatesinglefitness (int n)
{
double[] y = new double[10];

Double A = chromosome[n, 0];

Double b = chromosome[n, 1];

Double c = 0.0; Save error sum of squares

for (int i = 0; i <; i++)
{
Y[i] = A * Math.sin (samplex[i] * Samplex[i]) + b * Samplex[i];

c = C + (Sampley[i]-y[i]) * (Sampley[i]-y[i]);
}

Fitness[n] =-C;

return fitness[n];

}

public void calculatefitness ()
{
for (int i = 0; i < population; i++) calculatesinglefitness (i);
}

public void sort ()
{
Double instead = 0;

Double ch0 = 0;

Double ch1 = 0;

Bubble sort
for (int j=0;j<population; j + +)
{
for (int i = population-1; I >0; i--)
{
if (Fitness[i] > Fitness[i-1])
{
Swap the two adjacent

Switching Fitness
instead = fitness[i-1];

FITNESS[I-1] = fitness[i];

Fitness[i] = instead;

Swap chromosomes
Ch0 = chromosome[i-1,0];

CH1 = chromosome[i-1, 1];

Chromosome[i-1, 0] = chromosome[i, 0];

Chromosome[i-1, 1] = chromosome[i, 1];

Chromosome[i, 0] = Ch0;

Chromosome[i, 1] = CH1;
}

}
}

}

public void Intercross (int father, int mother, int son, int daughter)
{
chromosome[son,0] = chromosome[father,0];
chromosome[son,1] = chromosome[mother,1];

Chromosome[daughter, 0] = 0.5 * chromosome[father, 0] + 0.5 * chromosome[mother, 0];
Chromosome[daughter, 1] = 0.5 * Chromosome[father, 1] + 0.5 * chromosome[mother, 1];

}

public void aberrance (int father, int son)
{
Random rand = new Random (Unchecked (5* (int) DateTime.Now.Ticks));

Chromosome[son, 0] = chromosome[father, 0] + (rand. Next (200)-100)/100.0;
Chromosome[son, 1] = Chromosome[father, 1] + (rand. Next (200)-100)/100.0;
}

public void Generate ()
{

Random rand = new Random (Unchecked (7 * (int) DateTime.Now.Ticks));

for (int k = +; k <; k+=2)
{
int father = Rand. Next (30);
int mother = Rand. Next (30);

int son = k;
int daughter = k+1;

Intercross (father, mother, son, daughter);
}

for (int k = Bayi; k < K + +)
{
int father = Rand. Next (30);

int son = k;

Aberrance (father, son);
}
}

}
}

Genetic algorithm Demo