nsga(c++).txt

单目标遗传算法c++ 本程序以调通

// 这是使用应用程序向导生成的 VC++ 
// 应用程序项目的主项目文件。
//新建控制台应用程序
#include "stdafx.h"

#using <mscorlib.dll>

#include <stdio.h> 
#include <stdlib.h> 
#include <math.h> 

using namespace System;




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



/* 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); 
	 printf("%lf\n",lbound );
     printf("%lf\n",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[j] = 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                                     */ 
/**************************************************************/ 

int _tmain(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"); 
int a;
scanf("%d",&a);
} 
/***************************************************************/