c语言遗传算法求函数最值 c++遗传算法库

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

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#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);

}

帮帮忙，用c语言写一个遗传算法程序解决y=x*x的最大值问题，x取0--31

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,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 individual 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 individual */

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 procedures 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 copy of the best individual */

/***************************************************************/

void keep_the_best()

{

int mem;

int i;

cur_best = 0; /* stores the index of the best individual */

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, copy 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 individual from the new population is better than */

/* the best individual from the previous population, then */

/* copy the best from the new population; else replace the */

/* worst individual 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

ppopulation[j+1].cfitness)

newpopulation[i] = population[j+1];

}

}

/* once a new population is created, copy 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 procedure 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 */

/* dumped 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(generationMAXGENS)

{

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");

}

有一个C#遗传算法程序求函数极值有的地方看不太懂，请大神解读，最好详细一点。

首先y=x*x在[0,31]这个函数的极值是取31的时候，用遗传算法来解答这样的问题是有点多余的。遗传算法的主要步骤是4步，初始化种群，选择，交叉，变异。这里说的淘汰函数，很可能就是在选择选择算子，这个算子是根据最适合最优先的算法来实现。举个简单的例子，你要用数字进行遗传算法，肯定得把他转化为2进制的染色体，【0-31】就是从00000-11111，每条染色体5个基因。对于选择运算来说，每次要从种群选择最优的几个，第一次完全是随机的。假如随机选4个染色体，选的4条染色体是1,2,3,4。很明显他们的值是1,4,9,16，总和是30，那么选择4的概率就是30分之16，这样就可以尽可能的选择大的数值。这里的淘汰域3，可能是每次淘汰3条染色体，或者每次只选择3条最优的染色体，视其选择的条数而定。我看在程序里没有用到这个东西。遗传算法以及进化算法不限定于特殊的程序，每个人有不同的理解，不必拘泥于概念。

如图，如何用这个PSO算法或遗传算法来求函数极值,用C语言编写代码

需要很多的子函数 %子程序：新物种交叉操作，函数名称存储为crossover.m function scro=crossover(population,seln,pc); BitLength=size(population,2); pcc=IfCroIfMut(pc);%根据交叉概率决定是否进行交叉操作，1则是，0则否 if pcc==1 chb=round(rand*(BitLength-2))+1;%在[1，BitLength-1]范围内随机产生一个交叉位 scro(1,:)=[population(seln(1),1:chb) population(seln(2),chb+1:BitLength)] scro(2,:)=[population(seln(2),1:chb) population(seln(1),chb+1:BitLength)] else scro(1,:)=population(seln(1),:); scro(2,:)=population(seln(2),:); end %子程序：计算适应度函数，函数名称存储为fitnessfun.m function [Fitvalue,cumsump]=fitnessfun(population); global BitLength global boundsbegin global boundsend popsize=size(population,1);%有popsize个个体 for i=1:popsize x=transform2to10(population(i,:));%将二进制转换为十进制 %转化为[-2，2]区间的实数 xx=boundsbegin+x*(boundsend-boundsbegin)/(power(2,BitLength)-1); Fitvalue(i)=targetfun(xx);%计算函数值，即适应度 end %给适...

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