www.pudn.com > algorithms.rar > 遗传算法-matlab.txt, change:2011-03-23,size:15063b


 这是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.edu,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 &&  
                              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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