ga.cpp

该段代码是有关遗传算法的编程代码

/***************************************************************/ 
/* 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 <[color=#555555] = lbound; 
         population[j].upper= ubound; 
         population[j].gene = randval(population[j].lower, 
                                 population[j].upper); 
         } 
    } 

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 [color=#555555]; 
      
    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  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 [color=#555555] = population[cur_best].gene; 
} 

/****************************************************************/ 
/* 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 [color=#555555].fitness > population[i+1].fitness) 
          {       
          if (population.fitness >= best) 
                { 
                best = population.fitness; 
                best_mem = i; 
                } 
          if (population[i+1].fitness [color=#555555].fitness [color=#555555].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 [color=#555555] = population[best_mem].gene; 
  population[POPSIZE].fitness = population[best_mem].fitness; 
  } 
else 
  { 
  for (i = 0; i [color=#555555] = population[POPSIZE].gene; 
  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 [color=#555555] = population[0];       
    else 
          { 
          for (j = 0; j = population[j].cfitness && 
                            p[color=#555555] = population[j+1]; 
          } 
    } 
/* once a new population is created, copy it back */ 

for (i = 0; i [color=#555555] = newpopulation;       
} 

/***************************************************************/ 
/* 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  1) 
 { 
 if(NVARS == 2) 
       point = 1; 
 else 
       point = (rand() % (NVARS - 1)) + 1; 

 for (i = 0; i [color=#555555], &population[two].gene); 

 } 
} 

/*************************************************************/ 
/* 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 [color=#555555].lower[j]; 
                hbound = population.upper[j];   
                population.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 [color=#555555].fitness; 
    sum_square += population.fitness * population.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 nn", 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[color=#555555]); 
 } 
fprintf(galog,"nn Best fitness = %3.3f",population[POPSIZE].fitness); 
fclose(galog); 
printf("Successn"); 
}