c实现遗传算法
intDecode(chars[],intD[]){
inti,j,k,list[8]={150,200,250,300,350,500,400,450};
if(strlen(s)!=51)return0;
for(i=0;i<51;i+=3){
k=0;
for(j=i;j<i+3;++j)
k=2*k+s[j]-'0';
D[i/3]=list[k];
}
return1;
}
❷ 遗传算法的C语言实现
一个非常简单的遗传算法源代码，是由Denis Cormier (North Carolina State University)开发的，Sita S.Raghavan (University of North Carolina at Charlotte)修正。代码保证尽可能少，实际上也不必查错。对一特定的应用修正此代码，用户只需改变常数的定义并且定义“评价函数”即可。注意代码的设计是求最大值，其中的目标函数只能取正值；且函数值和个体的适应值之间没有区别。该系统使用比率选择、精华模型、单点杂交和均匀变异。如果用Gaussian变异替换均匀变异，可能得到更好的效果。代码没有任何图形，甚至也没有屏幕输出，主要是保证在平台之间的高可移植性。读者可以从ftp.uncc.e,目录 coe/evol中的文件prog.c中获得。要求输入的文件应该命名为‘gadata.txt’；系统产生的输出文件为‘galog.txt’。输入的文件由几行组成：数目对应于变量数。且每一行提供次序——对应于变量的上下界。如第一行为第一个变量提供上下界，第二行为第二个变量提供上下界，等等。
/**************************************************************************/
/* This is a simple genetic algorithm implementation where the */
/* evaluation function takes positive values only and the */
/* fitness of an indivial is the same as the value of the */
/* objective function */
/**************************************************************************/
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
/* Change any of these parameters to match your needs */
#define POPSIZE 50 /* population size */
#define MAXGENS 1000 /* max. number of generations */
#define NVARS 3 /* no. of problem variables */
#define PXOVER 0.8 /* probability of crossover */
#define PMUTATION 0.15 /* probability of mutation */
#define TRUE 1
#define FALSE 0
int generation; /* current generation no. */
int cur_best; /* best indivial */
FILE *galog; /* an output file */
struct genotype /* genotype (GT), a member of the population */
{
double gene[NVARS]; /* a string of variables */
double fitness; /* GT's fitness */
double upper[NVARS]; /* GT's variables upper bound */
double lower[NVARS]; /* GT's variables lower bound */
double rfitness; /* relative fitness */
double cfitness; /* cumulative fitness */
};
struct genotype population[POPSIZE+1]; /* population */
struct genotype newpopulation[POPSIZE+1]; /* new population; */
/* replaces the */
/* old generation */
/* Declaration of proceres used by this genetic algorithm */
void initialize(void);
double randval(double, double);
void evaluate(void);
void keep_the_best(void);
void elitist(void);
void select(void);
void crossover(void);
void Xover(int,int);
void swap(double *, double *);
void mutate(void);
void report(void);
/***************************************************************/
/* Initialization function: Initializes the values of genes */
/* within the variables bounds. It also initializes (to zero) */
/* all fitness values for each member of the population. It */
/* reads upper and lower bounds of each variable from the */
/* input file `gadata.txt'. It randomly generates values */
/* between these bounds for each gene of each genotype in the */
/* population. The format of the input file `gadata.txt' is */
/* var1_lower_bound var1_upper bound */
/* var2_lower_bound var2_upper bound ... */
/***************************************************************/
void initialize(void)
{
FILE *infile;
int i, j;
double lbound, ubound;
if ((infile = fopen("gadata.txt","r"))==NULL)
{
fprintf(galog,"\nCannot open input file!\n");
exit(1);
}
/* initialize variables within the bounds */
for (i = 0; i < NVARS; i++)
{
fscanf(infile, "%lf",&lbound);
fscanf(infile, "%lf",&ubound);
for (j = 0; j < POPSIZE; j++)
{
population[j].fitness = 0;
population[j].rfitness = 0;
population[j].cfitness = 0;
population[j].lower[i] = lbound;
population[j].upper[i]= ubound;
population[j].gene[i] = randval(population[j].lower[i],
population[j].upper[i]);
}
}
fclose(infile);
}
/***********************************************************/
/* Random value generator: Generates a value within bounds */
/***********************************************************/
double randval(double low, double high)
{
double val;
val = ((double)(rand()%1000)/1000.0)*(high - low) + low;
return(val);
}
/*************************************************************/
/* Evaluation function: This takes a user defined function. */
/* Each time this is changed, the code has to be recompiled. */
/* The current function is: x[1]^2-x[1]*x[2]+x[3] */
/*************************************************************/
void evaluate(void)
{
int mem;
int i;
double x[NVARS+1];
for (mem = 0; mem < POPSIZE; mem++)
{
for (i = 0; i < NVARS; i++)
x[i+1] = population[mem].gene[i];
population[mem].fitness = (x[1]*x[1]) - (x[1]*x[2]) + x[3];
}
}
/***************************************************************/
/* Keep_the_best function: This function keeps track of the */
/* best member of the population. Note that the last entry in */
/* the array Population holds a of the best indivial */
/***************************************************************/
void keep_the_best()
{
int mem;
int i;
cur_best = 0; /* stores the index of the best indivial */
for (mem = 0; mem < POPSIZE; mem++)
{
if (population[mem].fitness > population[POPSIZE].fitness)
{
cur_best = mem;
population[POPSIZE].fitness = population[mem].fitness;
}
}
/* once the best member in the population is found, the genes */
for (i = 0; i < NVARS; i++)
population[POPSIZE].gene[i] = population[cur_best].gene[i];
}
/****************************************************************/
/* Elitist function: The best member of the previous generation */
/* is stored as the last in the array. If the best member of */
/* the current generation is worse then the best member of the */
/* previous generation, the latter one would replace the worst */
/* member of the current population */
/****************************************************************/
void elitist()
{
int i;
double best, worst; /* best and worst fitness values */
int best_mem, worst_mem; /* indexes of the best and worst member */
best = population[0].fitness;
worst = population[0].fitness;
for (i = 0; i < POPSIZE - 1; ++i)
{
if(population[i].fitness > population[i+1].fitness)
{
if (population[i].fitness >= best)
{
best = population[i].fitness;
best_mem = i;
}
if (population[i+1].fitness <= worst)
{
worst = population[i+1].fitness;
worst_mem = i + 1;
}
}
else
{
if (population[i].fitness <= worst)
{
worst = population[i].fitness;
worst_mem = i;
}
if (population[i+1].fitness >= best)
{
best = population[i+1].fitness;
best_mem = i + 1;
}
}
}
/* if best indivial from the new population is better than */
/* the best indivial from the previous population, then */
/* the best from the new population; else replace the */
/* worst indivial from the current population with the */
/* best one from the previous generation */
if (best >= population[POPSIZE].fitness)
{
for (i = 0; i < NVARS; i++)
population[POPSIZE].gene[i] = population[best_mem].gene[i];
population[POPSIZE].fitness = population[best_mem].fitness;
}
else
{
for (i = 0; i < NVARS; i++)
population[worst_mem].gene[i] = population[POPSIZE].gene[i];
population[worst_mem].fitness = population[POPSIZE].fitness;
}
}
/**************************************************************/
/* Selection function: Standard proportional selection for */
/* maximization problems incorporating elitist model - makes */
/* sure that the best member survives */
/**************************************************************/
void select(void)
{
int mem, i, j, k;
double sum = 0;
double p;
/* find total fitness of the population */
for (mem = 0; mem < POPSIZE; mem++)
{
sum += population[mem].fitness;
}
/* calculate relative fitness */
for (mem = 0; mem < POPSIZE; mem++)
{
population[mem].rfitness = population[mem].fitness/sum;
}
population[0].cfitness = population[0].rfitness;
/* calculate cumulative fitness */
for (mem = 1; mem < POPSIZE; mem++)
{
population[mem].cfitness = population[mem-1].cfitness +
population[mem].rfitness;
}
/* finally select survivors using cumulative fitness. */
for (i = 0; i < POPSIZE; i++)
{
p = rand()%1000/1000.0;
if (p < population[0].cfitness)
newpopulation[i] = population[0];
else
{
for (j = 0; j < POPSIZE;j++)
if (p >= population[j].cfitness &&
p<population[j+1].cfitness)
newpopulation[i] = population[j+1];
}
}
/* once a new population is created, it back */
for (i = 0; i < POPSIZE; i++)
population[i] = newpopulation[i];
}
/***************************************************************/
/* Crossover selection: selects two parents that take part in */
/* the crossover. Implements a single point crossover */
/***************************************************************/
void crossover(void)
{
int i, mem, one;
int first = 0; /* count of the number of members chosen */
double x;
for (mem = 0; mem < POPSIZE; ++mem)
{
x = rand()%1000/1000.0;
if (x < PXOVER)
{
++first;
if (first % 2 == 0)
Xover(one, mem);
else
one = mem;
}
}
}
/**************************************************************/
/* Crossover: performs crossover of the two selected parents. */
/**************************************************************/
void Xover(int one, int two)
{
int i;
int point; /* crossover point */
/* select crossover point */
if(NVARS > 1)
{
if(NVARS == 2)
point = 1;
else
point = (rand() % (NVARS - 1)) + 1;
for (i = 0; i < point; i++)
swap(&population[one].gene[i], &population[two].gene[i]);
}
}
/*************************************************************/
/* Swap: A swap procere that helps in swapping 2 variables */
/*************************************************************/
void swap(double *x, double *y)
{
double temp;
temp = *x;
*x = *y;
*y = temp;
}
/**************************************************************/
/* Mutation: Random uniform mutation. A variable selected for */
/* mutation is replaced by a random value between lower and */
/* upper bounds of this variable */
/**************************************************************/
void mutate(void)
{
int i, j;
double lbound, hbound;
double x;
for (i = 0; i < POPSIZE; i++)
for (j = 0; j < NVARS; j++)
{
x = rand()%1000/1000.0;
if (x < PMUTATION)
{
/* find the bounds on the variable to be mutated */
lbound = population[i].lower[j];
hbound = population[i].upper[j];
population[i].gene[j] = randval(lbound, hbound);
}
}
}
/***************************************************************/
/* Report function: Reports progress of the simulation. Data */
/* mped into the output file are separated by commas */
/***************************************************************/
。。。。。
代码太多 你到下面呢个网站看看吧
void main(void)
{
int i;
if ((galog = fopen("galog.txt","w"))==NULL)
{
exit(1);
}
generation = 0;
fprintf(galog, "\n generation best average standard \n");
fprintf(galog, " number value fitness deviation \n");
initialize();
evaluate();
keep_the_best();
while(generation<MAXGENS)
{
generation++;
select();
crossover();
mutate();
report();
evaluate();
elitist();
}
fprintf(galog,"\n\n Simulation completed\n");
fprintf(galog,"\n Best member: \n");
for (i = 0; i < NVARS; i++)
{
fprintf (galog,"\n var(%d) = %3.3f",i,population[POPSIZE].gene[i]);
}
fprintf(galog,"\n\n Best fitness = %3.3f",population[POPSIZE].fitness);
fclose(galog);
printf("Success\n");
}
❸ 遗传算法程序
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
/* Change any of these parameters to match your needs */
#define POPSIZE 50 /* population size */
#define MAXGENS 1000 /* max. number of generations */
#define NVARS 3 /* no. of problem variables */
#define PXOVER 0.8 /* probability of crossover */
#define PMUTATION 0.15 /* probability of mutation */
#define TRUE 1
#define FALSE 0
int generation; /* current generation no. */
int cur_best; /* best indivial */
FILE *galog; /* an output file */
struct genotype /* genotype (GT), a member of the population */
{
double gene[NVARS]; /* a string of variables */
double fitness; /* GT's fitness */
double upper[NVARS]; /* GT's variables upper bound */
double lower[NVARS]; /* GT's variables lower bound */
double rfitness; /* relative fitness */
double cfitness; /* cumulative fitness */
};
struct genotype population[POPSIZE+1]; /* population */
struct genotype newpopulation[POPSIZE+1]; /* new population; */
/* replaces the */
/* old generation */
/* Declaration of proceres used by this genetic algorithm */
void initialize(void);
double randval(double, double);
void evaluate(void);
void keep_the_best(void);
void elitist(void);
void select(void);
void crossover(void);
void Xover(int,int);
void swap(double *, double *);
void mutate(void);
void report(void);
/***************************************************************/
/* Initialization function: Initializes the values of genes */
/* within the variables bounds. It also initializes (to zero) */
/* all fitness values for each member of the population. It */
/* reads upper and lower bounds of each variable from the */
/* input file `gadata.txt'. It randomly generates values */
/* between these bounds for each gene of each genotype in the */
/* population. The format of the input file `gadata.txt' is */
/* var1_lower_bound var1_upper bound */
/* var2_lower_bound var2_upper bound ... */
/***************************************************************/
void initialize(void)
{
FILE *infile;
int i, j;
double lbound, ubound;
if ((infile = fopen("gadata.txt","r"))==NULL)
{
fprintf(galog,"\nCannot open input file!\n");
exit(1);
}
/* initialize variables within the bounds */
for (i = 0; i < NVARS; i++)
{
fscanf(infile, "%lf",&lbound);
fscanf(infile, "%lf",&ubound);
for (j = 0; j < POPSIZE; j++)
{
population[j].fitness = 0;
population[j].rfitness = 0;
population[j].cfitness = 0;
population[j].lower[i] = lbound;
population[j].upper[i]= ubound;
population[j].gene[i] = randval(population[j].lower[i],
population[j].upper[i]);
}
}
fclose(infile);
}
/***********************************************************/
/* Random value generator: Generates a value within bounds */
/***********************************************************/
double randval(double low, double high)
{
double val;
val = ((double)(rand()%1000)/1000.0)*(high - low) + low;
return(val);
}
/*************************************************************/
/* Evaluation function: This takes a user defined function. */
/* Each time this is changed, the code has to be recompiled. */
/* The current function is: x[1]^2-x[1]*x[2]+x[3] */
/*************************************************************/
void evaluate(void)
{
int mem;
int i;
double x[NVARS+1];
for (mem = 0; mem < POPSIZE; mem++)
{
for (i = 0; i < NVARS; i++)
x[i+1] = population[mem].gene[i];
population[mem].fitness = (x[1]*x[1]) - (x[1]*x[2]) + x[3];
}
}
/***************************************************************/
/* Keep_the_best function: This function keeps track of the */
/* best member of the population. Note that the last entry in */
/* the array Population holds a of the best indivial */
/***************************************************************/
void keep_the_best()
{
int mem;
int i;
cur_best = 0; /* stores the index of the best indivial */
for (mem = 0; mem < POPSIZE; mem++)
{
if (population[mem].fitness > population[POPSIZE].fitness)
{
cur_best = mem;
population[POPSIZE].fitness = population[mem].fitness;
}
}
/* once the best member in the population is found, the genes */
for (i = 0; i < NVARS; i++)
population[POPSIZE].gene[i] = population[cur_best].gene[i];
}
/****************************************************************/
/* Elitist function: The best member of the previous generation */
/* is stored as the last in the array. If the best member of */
/* the current generation is worse then the best member of the */
/* previous generation, the latter one would replace the worst */
/* member of the current population */
/****************************************************************/
void elitist()
{
int i;
double best, worst; /* best and worst fitness values */
int best_mem, worst_mem; /* indexes of the best and worst member */
best = population[0].fitness;
worst = population[0].fitness;
for (i = 0; i < POPSIZE - 1; ++i)
{
if(population[i].fitness > population[i+1].fitness)
{
if (population[i].fitness >= best)
{
best = population[i].fitness;
best_mem = i;
}
if (population[i+1].fitness <= worst)
{
worst = population[i+1].fitness;
worst_mem = i + 1;
}
}
else
{
if (population[i].fitness <= worst)
{
worst = population[i].fitness;
worst_mem = i;
}
if (population[i+1].fitness >= best)
{
best = population[i+1].fitness;
best_mem = i + 1;
}
}
}
/* if best indivial from the new population is better than */
/* the best indivial from the previous population, then */
/* the best from the new population; else replace the */
/* worst indivial from the current population with the */
/* best one from the previous generation */
if (best >= population[POPSIZE].fitness)
{
for (i = 0; i < NVARS; i++)
population[POPSIZE].gene[i] = population[best_mem].gene[i];
population[POPSIZE].fitness = population[best_mem].fitness;
}
else
{
for (i = 0; i < NVARS; i++)
population[worst_mem].gene[i] = population[POPSIZE].gene[i];
population[worst_mem].fitness = population[POPSIZE].fitness;
}
}
/**************************************************************/
/* Selection function: Standard proportional selection for */
/* maximization problems incorporating elitist model - makes */
/* sure that the best member survives */
/**************************************************************/
void select(void)
{
int mem, i, j, k;
double sum = 0;
double p;
/* find total fitness of the population */
for (mem = 0; mem < POPSIZE; mem++)
{
sum += population[mem].fitness;
}
/* calculate relative fitness */
for (mem = 0; mem < POPSIZE; mem++)
{
population[mem].rfitness = population[mem].fitness/sum;
}
population[0].cfitness = population[0].rfitness;
/* calculate cumulative fitness */
for (mem = 1; mem < POPSIZE; mem++)
{
population[mem].cfitness = population[mem-1].cfitness +
population[mem].rfitness;
}
/* finally select survivors using cumulative fitness. */
for (i = 0; i < POPSIZE; i++)
{
p = rand()%1000/1000.0;
if (p < population[0].cfitness)
newpopulation[i] = population[0];
else
{
for (j = 0; j < POPSIZE;j++)
if (p >= population[j].cfitness &&
p<population[j+1].cfitness)
newpopulation[i] = population[j+1];
}
}
/* once a new population is created, it back */
for (i = 0; i < POPSIZE; i++)
population[i] = newpopulation[i];
}
/***************************************************************/
/* Crossover selection: selects two parents that take part in */
/* the crossover. Implements a single point crossover */
/***************************************************************/
void crossover(void)
{
int i, mem, one;
int first = 0; /* count of the number of members chosen */
double x;
for (mem = 0; mem < POPSIZE; ++mem)
{
x = rand()%1000/1000.0;
if (x < PXOVER)
{
++first;
if (first % 2 == 0)
Xover(one, mem);
else
one = mem;
}
}
}
/**************************************************************/
/* Crossover: performs crossover of the two selected parents. */
/**************************************************************/
void Xover(int one, int two)
{
int i;
int point; /* crossover point */
/* select crossover point */
if(NVARS > 1)
{
if(NVARS == 2)
point = 1;
else
point = (rand() % (NVARS - 1)) + 1;
for (i = 0; i < point; i++)
swap(&population[one].gene[i], &population[two].gene[i]);
}
}
/*************************************************************/
/* Swap: A swap procere that helps in swapping 2 variables */
/*************************************************************/
void swap(double *x, double *y)
{
double temp;
temp = *x;
*x = *y;
*y = temp;
}
/**************************************************************/
/* Mutation: Random uniform mutation. A variable selected for */
/* mutation is replaced by a random value between lower and */
/* upper bounds of this variable */
/**************************************************************/
void mutate(void)
{
int i, j;
double lbound, hbound;
double x;
for (i = 0; i < POPSIZE; i++)
for (j = 0; j < NVARS; j++)
{
x = rand()%1000/1000.0;
if (x < PMUTATION)
{
/* find the bounds on the variable to be mutated */
lbound = population[i].lower[j];
hbound = population[i].upper[j];
population[i].gene[j] = randval(lbound, hbound);
}
}
}
/***************************************************************/
/* Report function: Reports progress of the simulation. Data */
/* mped into the output file are separated by commas */
/***************************************************************/
void report(void)
{
int i;
double best_val; /* best population fitness */
double avg; /* avg population fitness */
double stddev; /* std. deviation of population fitness */
double sum_square; /* sum of square for std. calc */
double square_sum; /* square of sum for std. calc */
double sum; /* total population fitness */
sum = 0.0;
sum_square = 0.0;
for (i = 0; i < POPSIZE; i++)
{
sum += population[i].fitness;
sum_square += population[i].fitness * population[i].fitness;
}
avg = sum/(double)POPSIZE;
square_sum = avg * avg * POPSIZE;
stddev = sqrt((sum_square - square_sum)/(POPSIZE - 1));
best_val = population[POPSIZE].fitness;
fprintf(galog, "\n%5d, %6.3f, %6.3f, %6.3f \n\n", generation,
best_val, avg, stddev);
}
/**************************************************************/
/* Main function: Each generation involves selecting the best */
/* members, performing crossover & mutation and then */
/* evaluating the resulting population, until the terminating */
/* condition is satisfied */
/**************************************************************/
void main(void)
{
int i;
if ((galog = fopen("galog.txt","w"))==NULL)
{
exit(1);
}
generation = 0;
fprintf(galog, "\n generation best average standard \n");
fprintf(galog, " number value fitness deviation \n");
initialize();
evaluate();
keep_the_best();
while(generation<MAXGENS)
{
generation++;
select();
crossover();
mutate();
report();
evaluate();
elitist();
}
fprintf(galog,"\n\n Simulation completed\n");
fprintf(galog,"\n Best member: \n");
for (i = 0; i < NVARS; i++)
{
fprintf (galog,"\n var(%d) = %3.3f",i,population[POPSIZE].gene[i]);
}
fprintf(galog,"\n\n Best fitness = %3.3f",population[POPSIZE].fitness);
fclose(galog);
printf("Success\n");
}
/***************************************************************/
❹ 求C代码：遗传算法求函数最大值f(x)=x^2 x 从0到30
#include <stdio.h>
#include <math.h>
#include <stdlib.h>
#include <time.h>
float f(float x)
{
return x * x;
}
void main()
{
float x[10];
float f1, f2;
int i, j;
float fmax;
int xfmax;
srand(time(NULL));
xfmax = 0;
x[0] = 15.0f;
f1 = f(x[0]);
f2 = f1 + 1.0f;
for (j = 0; fabs(f1 - f2) >= 0.0001f || j < 50; j++)
{
for (i = 0; i < 10; i++)
{
if (i != xfmax)
{
x[i] = -1;
while (!(x[i] >= 0 && x[i] <= 30))
{
x[i] = x[xfmax] + ((float)rand() / RAND_MAX * 2 - 1) * (15.0f / (j * 2 + 1));
}
}
}
xfmax = -1;
for (i = 0; i < 10; i++)
{
if (xfmax < 0 || fmax < f(x[i]))
{
fmax = f(x[i]);
xfmax = i;
}
}
f2 = f1;
f1 = fmax;
}
printf("f(%f) = %f\n", x[xfmax], fmax);
}
❺ 求遗传算法（GA）C语言代码
.----来个例子，大家好理解..--
基于遗传算法的人工生命模拟
#include<stdio.h>
#include<stdlib.h>
#include<graphics.h>
#include<math.h>
#include<time.h>
#include<string.h>
#include "graph.c"
/* 宏定义 */
#define TL1 20 /* 植物性食物限制时间 */
#define TL2 5 /* 动物性食物限制时间 */
#define NEWFOODS 3 /* 植物性食物每代生成数目 */
#define MUTATION 0.05 /* 变异概率 */
#define G_LENGTH 32 /* 个体染色体长度 */
#define MAX_POP 100 /* 个体总数的最大值 */
#define MAX_FOOD 100 /* 食物总数的最大值 */
#define MAX_WX 60 /* 虚拟环境的长度最大值 */
#define MAX_WY 32 /* 虚拟环境的宽度最大值 */
#define SX1 330 /* 虚拟环境图左上角点x坐标 */
#define SY1 40 /* 虚拟环境图左上角点y坐标 */
#define GX 360 /* 个体数进化图形窗口的左上角点X坐标 */
#define GY 257 /* 个体数进化图形窗口的左上角点Y坐标 */
#define GXR 250 /* 个体数进化图形窗口的长度 */
#define GYR 100 /* 个体数进化图形窗口的宽度 */
#define GSTEP 2 /* 个体数进化图形窗口的X方向步长 */
#define R_LIFE 0.05 /* 初期产生生物数的环境比率 */
#define R_FOOD 0.02 /* 初期产生食物数的环境比率 */
#define SL_MIN 10 /* 个体寿命最小值 */
/* 全局变量 */
unsigned char gene[MAX_POP][G_LENGTH]; /* 遗传基因 */
unsigned char iflg[MAX_POP]; /* 个体死活状态标志变量 */