c语言遗传算法二元函数,C语言遗传算法
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,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。
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/**************************************************************************/
/* 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");
}
C语言编写二元一次函数，ax+b=0求解！！！！！！！
#include stdio.h
#include stdlib.h
int main(int argc, char const *argv[])
{
int a, b;
printf("请输入一次方程的系数a和b(以逗号隔开):");
scanf("%d,%d", a, b);
if (a == 0); //分母为0，无解
else
{
char ch = b 0 ? '-' : '+';
printf("%dx%c%d=0的根是:x=", a, ch, abs(b));
printf("%d\n", -b / a);
}
return 0;
}
如何使用遗传算法或神经网络在MATLAB 中求二元函数最小值
% 2008年4月12日修改
%**********************%主函数*****************************************
function main()
global chrom lchrom oldpop newpop varible fitness popsize sumfitness %定义全局变量
global pcross pmutation temp bestfit maxfit gen bestgen length epop efitness val varible2 varible1
global maxgen po pp mp np val1
length=18;
lchrom=30; %染色体长度
popsize=30; %种群大小
pcross=0.6; %交叉概率
pmutation=0.01; %变异概率
maxgen=1000; %最大代数
mp=0.1; %保护概率
%
initpop; % 初始种群
%
for gen=1:maxgen
generation;
end
%
best;
bestfit % 最佳个体适应度值输出
bestgen % 最佳个体所在代数输出
x1= val1(bestgen,1)
x2= val1(bestgen,2)
gen=1:maxgen;
figure
plot(gen,maxfit(1,gen)); % 进化曲线
title('精英保留');
%
%********************** 产生初始种群 ************************************
%
function initpop()
global lchrom oldpop popsize
oldpop=round(rand(popsize,lchrom)); %生成的oldpop为30行12列由0,1构成的矩阵
%其中popsize为种群中个体数目lchrom为染色体编码长度
%
%*************************%产生新一代个体**********************************
%
function generation()
global epop oldpop popsize mp
objfun; %计算适应度值
n=floor(mp*popsize); %需要保留的n个精英个体
for i=1:n
epop(i,:)=oldpop((popsize-n+i),:);
% efitness(1,i)=fitness(1,(popsize-n+i))
end
select; %选择操作
crossover;
mutation;
elite; %精英保留
%
%************************%计算适应度值************************************
%
function objfun()
global lchrom oldpop fitness popsize chrom varible varible1 varible2 length
global maxfit gen epop mp val1
a1=-3; b1=3;
a2=-2;b2=2;
fitness=0;
for i=1:popsize
%前一未知数X1
if length~=0
chrom=oldpop(i,1:length);% before代表节点位置
c=decimal(chrom);
varible1(1,i)=a1+c*(b1-a1)/(2.^length-1); %对应变量值
%后一未知数
chrom=oldpop(i,length+1:lchrom);% before代表节点位置
c=decimal(chrom);
varible2(1,i)=a2+c*(b2-a2)/(2.^(lchrom-length)-1); %对应变量值
else
chrom=oldpop(i,:);
c=decimal(chrom);
varible(1,i)=a1+c*(b1-a1)/(2.^lchrom-1); %对应变量值
end
%两个自变量
fitness(1,i)=4*varible1(1,i)^2-2.1*varible1(1,i)^4+1/3*varible1(1,i)^6+varible1(1,i)*varible2(1,i)-4*varible2(1,i)^2+4*varible2(1,i)^4;
%fitness(1,i) = 21.5+varible1(1,i)*sin(4*pi*varible1(1,i))+varible2(1,i) *sin(20*pi*varible2(1,i));
%一个自变量
%fitness(1,i) = 20*cos(0.25*varible(1,i))-12*sin(0.33*varible(1,i))+40 %个体适应度函数值
end
lsort; % 个体排序
maxfit(1,gen)=max(fitness); %求本代中的最大适应度值maxfit
val1(gen,1)=varible1(1,popsize);
val1(gen,2)=varible2(1,popsize);
%************************二进制转十进制**********************************
%
function c=decimal(chrom)
c=0;
for j=1:size(chrom,2)
c=c+chrom(1,j)*2.^(size(chrom,2)-j);
end
%
%************************* 个体排序 *****************************
% 从小到大顺序排列
%
function lsort()
global popsize fitness oldpop epop efitness mp val varible2 varible1
for i=1:popsize
j=i+1;
while j=popsize
if fitness(1,i)fitness(1,j)
tf=fitness(1,i); % 适应度值
tc=oldpop(i,:); % 基因代码
fitness(1,i)=fitness(1,j); % 适应度值互换
oldpop(i,:)=oldpop(j,:); % 基因代码互换
fitness(1,j)=tf;
oldpop(j,:)=tc;
end
j=j+1;
end
val(1,1)=varible1(1,popsize);
val(1,2)=varible2(1,popsize);
end
%*************************转轮法选择操作**********************************
%
function select()
global fitness popsize sumfitness oldpop temp mp np
sumfitness=0; %个体适应度之和
for i=1:popsize % 仅计算(popsize-np-mp)个个体的选择概率
sumfitness=sumfitness+fitness(1,i);
end
%
for i=1:popsize % 仅计算(popsize-np-mp)个个体的选择概率
p(1,i)=fitness(1,i)/sumfitness; % 个体染色体的选择概率
end
%
q=cumsum(p); % 个体染色体的累积概率（内部函数），共(popsize-np-mp)个
%
b=sort(rand(1,popsize)); % 产生(popsize-mp)个随机数，并按升序排列。mp为保护个体数
j=1;
k=1;
while j=popsize % 从(popsize-mp-np)中选出(popsize-mp)个个体，并放入temp(j,:)中；
if b(1,j)q(1,k)
temp(j,:)=oldpop(k,:);
j=j+1;
else
k=k+1;
end
end
%
j=popsize+1; % 从统一挪过来的（popsize-np-mp）以后个体——优秀个体中选择
for i=(popsize+1):popsize % 将mp个保留个体放入交配池temp(i,:)，以保证群体数popsize
temp(i,:)=oldpop(j,:);
j=j+1;
end
%
%**************************%交叉操作***************************************
%
function crossover()
global temp popsize pcross lchrom mp
n=floor(pcross*popsize); %交叉发生的次数（向下取整）
if rem(n,2)~=0 % 求余
n=n+1; % 保证为偶数个个体，便于交叉操作
end
%
j=1;
m=0;
%
% 对(popsize-mp)个个体将进行随机配对，满足条件者将进行交叉操作（按顺序选择要交叉的对象）
%
for i=1:popsize
p=rand; % 产生随机数
if ppcross % 满足交叉条件
parent(j,:)=temp(i,:); % 选出1个父本
k(1,j)=i;
j=j+1; % 记录父本个数
m=m+1 ; % 记录杂交次数
if (j==3)(m=n) % 满足两个父本(j==3)，未超过交叉次数(m=n)
pos=round(rand*(lchrom-1))+1; % 确定随机位数（四舍五入取整）
for i=1:pos
child1(1,i)=parent(1,i);
child2(1,i)=parent(2,i);
end
for i=(pos+1):lchrom
child1(1,i)=parent(2,i);
child2(1,i)=parent(1,i);
end
i=k(1,1);
j=k(1,2);
temp(i,:)=child1(1,:);
temp(j,:)=child2(1,:);
j=1;
end
end
end
%
%****************************%变异操作*************************************
%
function mutation()
global popsize lchrom pmutation temp newpop oldpop mp
m=lchrom*popsize; % 总的基因数
n=round(pmutation*m); % 变异发生的次数
for i=1:n % 执行变异操作循环
k=round(rand*(m-1))+1; %确定变异位置（四舍五入取整）
j=ceil(k/lchrom); % 确定个体编号（取整）
l=rem(k,lchrom); %确定个体中变位基因的位置（求余）
if l==0
temp(j,lchrom)=~temp(j,lchrom); % 取非操作
else
temp(j,l)=~temp(j,l); % 取非操作
end
end
for i=1:popsize
oldpop(i,:)=temp(i,:); %产生新的个体
end
%
%*********************%精英选择%*******************************************
%
function elite()
global epop oldpop mp popsize
objfun; %计算适应度值
n=floor(mp*popsize); %需要保留的n个精英个体
for i=1:n
oldpop(i,:)=epop(i,:);
% efitness(1,i)=fitness(1,(popsize-n+i))
end;
%
%*********************%最佳个体********************************************
%
function best()
global maxfit bestfit gen maxgen bestgen
bestfit=maxfit(1,1);
gen=2;
while gen=maxgen
if bestfitmaxfit(1,gen)
bestfit=maxfit(1,gen);
bestgen=gen;
end
gen=gen+1;
end
%**************************************************************************
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