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 [【技术文档】]一个简单实用的遗传算法c程序（转载） |
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。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 && p<population[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 *//***************************************************************/
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");}/***************************************************************/
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| 回复:一个简单实用的遗传算法c程序（转载） |
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1234(游客)发表评论于2009/5/28 21:54:45 |
为什么我的输出文件是空的？
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| 回复:一个简单实用的遗传算法c程序（转载） |
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armigy(游客)发表评论于2009/3/10 10:10:15 |
能不能把你的文件包发给我,我的邮箱是wufengqing1984@163.com
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| 回复:一个简单实用的遗传算法c程序（转载） |
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song(游客)发表评论于2009/3/7 20:06:03 |
能不能把一份gadata.txt的例子发给我,非常感谢，初学遗传算法，想理解使用，我的邮箱是:hust_song880222@126.com
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| 回复:一个简单实用的遗传算法c程序（转载） |
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second(游客)发表评论于2009/2/11 14:08:02 |
能不能把gadata.txt 发给我,我的邮箱是:secondxx@gmail.com
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| 回复:一个简单实用的遗传算法c程序（转载） |
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GeGe(游客)发表评论于2006/4/23 12:55:41 |
格式：
lbound
ubound
lbound
ubound
lbound
ubound
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| 回复:一个简单实用的遗传算法c程序（转载） |
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rongerhou(游客)发表评论于2006/4/17 21:26:18 |
也帮我发一份gadata.txt的准确格式吗？
sunnyeeee88.student@sina.com
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| 回复:一个简单实用的遗传算法c程序（转载） |
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11nong(游客)发表评论于2006/3/21 10:38:37 |
very good
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| 回复:一个简单实用的遗传算法c程序（转载） |
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宽 容(游客)发表评论于2006/3/9 21:07:37 |
能把gadata.txt文件也发到我的邮箱里吗？
orangeron@163.com
谢谢，毕业设计急用!!
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| 回复:一个简单实用的遗传算法c程序（转载） |
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fengjie(游客)发表评论于2006/1/9 20:32:05 |
能不能把你的文件包发给我,我的邮箱是:fengjie12132003@yahoo.com.cn
谢谢
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| 回复:一个简单实用的遗传算法c程序（转载） |
对不起，不是我的原创，我无能为力。不过它不是有说明么？
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