遺傳算法的C語言實現

一個非常簡單的遺傳算法源代碼，是由Denis Cormier (North Carolina State University)開發(fā)的，Sita S.Raghavan (University of North Carolina at Charlotte)修正。代碼保證盡可能少，實際上也不必查錯。對一特定的應用修正此代碼，用戶只需改變常數的定義并且定義“評價函數”即可。注意代碼的設計是求最大值，其中的目標函數只能取正值；且函數值和個體的適應值之間沒有區(qū)別。該系統(tǒng)使用比率選擇、精華模型、單點雜交和均勻變異。如果用Gaussian變異替換均勻變異，可能得到更好的效果。代碼沒有任何圖形，甚至也沒有屏幕輸出，主要是保證在平臺之間的高可移植性。讀者可以從,目錄 coe/evol中的文件prog.c中獲得。要求輸入的文件應該命名為‘gadata.txt’；系統(tǒng)產生的輸出文件為‘galog.txt’。輸入的文件由幾行組成：數目對應于變量數。且每一行提供次序——對應于變量的上下界。如第一行為第一個變量提供上下界，第二行為第二個變量提供上下界，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 */

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

。。。。。

代碼太多 你到下面呢個網站看看吧

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語言編寫二元一次函數，ax+b=0求解！！！！！！！

#include?stdio.h

#include?stdlib.h

int?main(int?argc,?char?const?*argv[])

{

int?a,?b;?

printf("請輸入一次方程的系數a和b(以逗號隔開):");

scanf("%d,%d",?a,?b);

if?(a?==?0);??//分母為0，無解

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;

}

如何使用遺傳算法或神經網絡在MATLAB 中求二元函數最小值

% 2008年4月12日修改

%**********************%主函數*****************************************

function main()

global chrom lchrom oldpop newpop varible fitness popsize sumfitness %定義全局變量

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; %染色體長度

popsize=30; %種群大小

pcross=0.6; %交叉概率

pmutation=0.01; %變異概率

maxgen=1000; %最大代數

mp=0.1; %保護概率

%

initpop; % 初始種群

%

for gen=1:maxgen

generation;

end

%

best;

bestfit % 最佳個體適應度值輸出

bestgen % 最佳個體所在代數輸出

x1= val1(bestgen,1)

x2= val1(bestgen,2)

gen=1:maxgen;

figure

plot(gen,maxfit(1,gen)); % 進化曲線

title('精英保留');

%

%********************** 產生初始種群 ************************************

%

function initpop()

global lchrom oldpop popsize

oldpop=round(rand(popsize,lchrom)); %生成的oldpop為30行12列由0,1構成的矩陣

%其中popsize為種群中個體數目lchrom為染色體編碼長度

%

%*************************%產生新一代個體**********************************

%

function generation()

global epop oldpop popsize mp

objfun; %計算適應度值

n=floor(mp*popsize); %需要保留的n個精英個體

for i=1:n

epop(i,:)=oldpop((popsize-n+i),:);

% efitness(1,i)=fitness(1,(popsize-n+i))

end

select; %選擇操作

crossover;

mutation;

elite; %精英保留

%

%************************%計算適應度值************************************

%

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

%前一未知數X1

if length~=0

chrom=oldpop(i,1:length);% before代表節(jié)點位置

c=decimal(chrom);

varible1(1,i)=a1+c*(b1-a1)/(2.^length-1); %對應變量值

%后一未知數

chrom=oldpop(i,length+1:lchrom);% before代表節(jié)點位置

c=decimal(chrom);

varible2(1,i)=a2+c*(b2-a2)/(2.^(lchrom-length)-1); %對應變量值

else

chrom=oldpop(i,:);

c=decimal(chrom);

varible(1,i)=a1+c*(b1-a1)/(2.^lchrom-1); %對應變量值

end

%兩個自變量

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));

%一個自變量

%fitness(1,i) = 20*cos(0.25*varible(1,i))-12*sin(0.33*varible(1,i))+40 %個體適應度函數值

end

lsort; % 個體排序

maxfit(1,gen)=max(fitness); %求本代中的最大適應度值maxfit

val1(gen,1)=varible1(1,popsize);

val1(gen,2)=varible2(1,popsize);

%************************二進制轉十進制**********************************

%

function c=decimal(chrom)

c=0;

for j=1:size(chrom,2)

c=c+chrom(1,j)*2.^(size(chrom,2)-j);

end

%

%************************* 個體排序 *****************************

% 從小到大順序排列

%

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); % 適應度值

tc=oldpop(i,:); % 基因代碼

fitness(1,i)=fitness(1,j); % 適應度值互換

oldpop(i,:)=oldpop(j,:); % 基因代碼互換

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

%*************************轉輪法選擇操作**********************************

%

function select()

global fitness popsize sumfitness oldpop temp mp np

sumfitness=0; %個體適應度之和

for i=1:popsize % 僅計算(popsize-np-mp)個個體的選擇概率

sumfitness=sumfitness+fitness(1,i);

end

%

for i=1:popsize % 僅計算(popsize-np-mp)個個體的選擇概率

p(1,i)=fitness(1,i)/sumfitness; % 個體染色體的選擇概率

end

%

q=cumsum(p); % 個體染色體的累積概率（內部函數），共(popsize-np-mp)個

%

b=sort(rand(1,popsize)); % 產生(popsize-mp)個隨機數，并按升序排列。mp為保護個體數

j=1;

k=1;

while j=popsize % 從(popsize-mp-np)中選出(popsize-mp)個個體，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; % 從統(tǒng)一挪過來的（popsize-np-mp）以后個體——優(yōu)秀個體中選擇

for i=(popsize+1):popsize % 將mp個保留個體放入交配池temp(i,:)，以保證群體數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); %交叉發(fā)生的次數（向下取整）

if rem(n,2)~=0 % 求余

n=n+1; % 保證為偶數個個體，便于交叉操作

end

%

j=1;

m=0;

%

% 對(popsize-mp)個個體將進行隨機配對，滿足條件者將進行交叉操作（按順序選擇要交叉的對象）

%

for i=1:popsize

p=rand; % 產生隨機數

if ppcross % 滿足交叉條件

parent(j,:)=temp(i,:); % 選出1個父本

k(1,j)=i;

j=j+1; % 記錄父本個數

m=m+1 ; % 記錄雜交次數

if (j==3)(m=n) % 滿足兩個父本(j==3)，未超過交叉次數(m=n)

pos=round(rand*(lchrom-1))+1; % 確定隨機位數（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

%

%****************************%變異操作*************************************

%

function mutation()

global popsize lchrom pmutation temp newpop oldpop mp

m=lchrom*popsize; % 總的基因數

n=round(pmutation*m); % 變異發(fā)生的次數

for i=1:n % 執(zhí)行變異操作循環(huán)

k=round(rand*(m-1))+1; %確定變異位置（四舍五入取整）

j=ceil(k/lchrom); % 確定個體編號（取整）

l=rem(k,lchrom); %確定個體中變位基因的位置（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,:); %產生新的個體

end

%

%*********************%精英選擇%*******************************************

%

function elite()

global epop oldpop mp popsize

objfun; %計算適應度值

n=floor(mp*popsize); %需要保留的n個精英個體

for i=1:n

oldpop(i,:)=epop(i,:);

% efitness(1,i)=fitness(1,(popsize-n+i))

end;

%

%*********************%最佳個體********************************************

%

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

%**************************************************************************

名稱欄目：c語言遺傳算法二元函數,C語言遺傳算法
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