GA-遗传算法-C#代码.doc

using System;using System.IO;using System.Collections;using System.Collections.Generic;using System.Text;using System.ComponentModel;using System.Data;using System.Data.OleDb;namespace ConsoleApplication1 public class Genetic_Algorithm Random rand=new Random(); int MaxTime;/最大运行时间 int popsize;/种群数量 int ChromosomeLength;/染色体长度 double CrossRate;/交叉率 double MutateRate;/变异率 double f;/适应度值 int selected;/定义selected数组，用于表示需要进行交叉操作的染色体序号 double wheel;/轮盘 int, pregeneration;/上一代 int, nextgeneration;/下一代 int Best;/定义当前最优解 int convergence;/定义当前最优解的已持续代数 int, timeconstrait; public Genetic_Algorithm(int populationsize, int chromolength)/GA-构造函数，变量初始化 rand = new Random(System.DateTime.Now.Millisecond); MaxTime = 50; popsize=populationsize; ChromosomeLength = chromolength; CrossRate = 0.8; MutateRate = 0.2; f = new double2*popsize; selected = new intpopsize; wheel = new doublepopsize + 1; pregeneration = new intpopsize, ChromosomeLength;/当前的染色体种群 nextgeneration = new intpopsize, ChromosomeLength;/下一代（子代）染色体种群 Best = new intChromosomeLength; convergence = 1; timeconstrait = new int20, 2 2, 6 , 1, 2 , 3, 4 , 1, 4 , 4, 7 , 3, 5 , 2, 6 , 3, 5 , 1, 4 , 3, 7 , 5, 7 , 2, 7 , 2, 4 , 4, 5 , 2, 5 , 4, 6 , 3, 5 , 1, 4 , 1, 5 , 3, 6 ; public void RunGA()/运行 int i; CreateFirstPop();/产生初始种群 i = 0; bool quit = true; while (quit) for (; i MaxTime; i+) Console.WriteLine(The 0th Generation., i + 1); CalFitness(ref pregeneration, popsize);/计算适应值 PrintResult();/输出每步的结果 WheelSelect();/此步确定了selectedi的值 CreateNextGeneration();/产生子代，包括被选择为selectedi的染色体的交叉，还有变异 ProduceNext(); Console.WriteLine(Press q to quit, press Enter to continue.); if (Console.Read() = q) quit = false; else MaxTime += 50; void CreateFirstPop()/产生初始种群 Console.WriteLine(Creating first generation.n); int i,j,r; for(i=0;ipopsize;i+) for(j=0;jChromosomeLength;j+) r=rand.Next(1,11); pregenerationi, j = r; void CreateNextGeneration()/产生下一代种群（经交叉、变异） int i; for (i = 0; i popsize; i+=2) Crossover(selectedi, selectedi + 1, i, i + 1);/将序号为selectedi和selectedi + 1的染色体进行交叉，产生的子代放在pregeneration中i和i+1的位置 Mutation(ref nextgeneration);/变异 void CalFitness(ref int, curgeneration,int number)/计算适应度值的函数 for (int i = 0; i number; i+) double fitness = 0; for (int j = 0; j ChromosomeLength; j+) fitness += Math.Abs(curgenerationi, j-j-1); fi = fitness; void FindMax(ref double f, out int max)/寻找数组中最大值 int i; max = 0; for (i = 1; i fmax) max = i; void FindMin(ref double f, out int min)/寻找数组中最小值 int i; min = 0; for (i = 1; i popsize; i+) if (fi fmin) min = i; void WheelSelect() /轮盘选择popsize个染色体（可重复），并将序号放入selected中，作为交叉的染色体 int i,j ,r; double sum; wheel0 = 0; sum = 0; for (i = 0; i popsize; i+) sum += fi; wheeli + 1 = wheeli + fi; for (i = 0; i popsize; i+) r = rand.Next(int)sum); for (j = 0; j wheelj & r 将序号为selectedi和selectedi + 1（这里形参是p1，p2）的染色体进行交叉，产生的子代放在pregeneration中i和i+1（这里形参是c1，c2）的位置 double dr = rand.NextDouble(); if (dr CrossRate) int covering_code = new intChromosomeLength; for (int i = 0; i ChromosomeLength; i+) covering_codei = rand.Next(0, 2); for (int i = 0; i ChromosomeLength; i+) if (covering_codei = 0) nextgenerationc1, i = pregenerationp1, i; nextgenerationc2, i = pregenerationp2, i; else nextgenerationc1, i = pregenerationp2, i; nextgenerationc2, i = pregenerationp1, i; else for (int i = 0; i ChromosomeLength; i+) nextgenerationc1, i = pregenerationp1, i; nextgenerationc2, i = pregenerationp2, i; void Mutation(ref int, curgeneration)/变异 int is_not_mutation; double dr; for (int i = 0; i popsize; i+) dr = rand.NextDouble(); if (dr MutateRate) for (int j = 0; j ChromosomeLength; j+) is_not_mutation = rand.Next(0, 2); if (is_not_mutation = 1) curgenerationi, j = rand.Next(1, 11); void PrintResult()/计算每次迭代后种群中最优解及其适应度值，平均适应值 int i,j; int min; double average; average = 0; for (i = 0; i popsize; i+) average += fi; average = (double) average / popsize; Console.Write(Average profit is 0n, average); FindMin(ref f, out min); /计算稳定的次数 for (j = 0; j ChromosomeLength; j+) if (pregenerationmin, j != Bestj) convergence = 1; goto G2; convergence+; G2: for (j = 0; j ChromosomeLength; j+) Bestj = pregenerationmin, j; /打印相关的数据 Console.Write(染色体 ); for (j = 0; j 父代和子代中popsize个最优的解进入下一代 int, temgeneration=new int 2*popsize,ChromosomeLength;/定义临时种群，用来将父代和子代放在一起，进行选优 /将父代放入临时种群 for (int i = 0; i = popsize - 1; i+) for (int j = 0; j = ChromosomeLength - 1; j+) temgenerationi, j = pregenerationi, j; /将子代放入临时种群 for (int i = 0; i = popsize - 1; i+) for (int j = 0; j = ChromosomeLength - 1; j+) temgenerationi + popsize, j = nextgenerationi, j; CalFitness(ref temgeneration, popsize * 2);/计算临时种群（父代和子代）的各染色体适应值 int tem=new int ChromosomeLength;/定义临时染色体，用来染色体排序时的交换. /根据临时种群（父代和子代）的各染色体适应值，进行排序 for (int i = 0; i 2*popsize - 1; i+) for (int j = i + 1; j fj) double tem_f = fi; fi = fj; fj = tem_f; for (int k = 0; k ChromosomeLength; k+) temk = temgenerationi, k; temgenerationi, k = temgenerationj, k; temgenerationj, k = temk; /取临时种群中前popsize个好的染色体作为下一代种群，并将子代变为父代 for (int i = 0; i = popsize - 1; i+) for (int j = 0; j = ChromosomeLength - 1; j+) pregenerationi, j = temgenerationi, j; class Program static vo