遗传算法Demo

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using System;
using System.Collections.Generic;
using System.Text;

namespace Gene
{
class IntelligentTestSystem
{
public

static int population=100; // 种群个数
static int maxNumber = 2; //每个个体（染色体）所用的基因个数

double[,] chromosome = new double[population, maxNumber]; //每个个体（染色体）的种群

double[] fitness = new double[population]; //适应度

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++) //随机生成染色体
{
for (int j = 0; j < 2; j++)
{
chromosome[i, j] = (rand.Next(200) - 100 ) / 10.0;
}
}

for (int i = 0; i < 10; i++) //函数的10个坐标点 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; //保存误差平方和

for (int i = 0; i < 10; 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;

//冒泡排序
for(int j=0;j<population ;j++)
{
for (int i = population-1; i >0; i--)
{
if (fitness[i] > fitness[i - 1])
{
//交换邻近的两个

//交换适应度
instead = fitness[i - 1];

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

fitness[i] = instead;

//交换染色体
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 = 31; k < 80; 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 = 81; k < 100; k ++)
{
int father = rand.Next(30);

int son = k;

aberrance(father, son);
}
}

}
}

遗传算法Demo

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原文地址：http://www.cnblogs.com/iamai/p/4800593.html