ga.txt

遗传算法(Genetic Algorithm,GA)是一种抽象于生物进化过程的基于自然选择和生物遗传机制的优化技术. 遗传算法的基本原理 在遗传算法的执行过程中,每一代有许多不同的种群个体

  struct   genotype   newpopulation[POPSIZE+1];   
    
  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);   
    
  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);   
        }   
    
    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);   
  }   
    
  double   randval(double   low,double   high)   
  {double   val;   
    val=((double)(rand()%1000)/1000.0)*(high-low)+low;   
    return   (val);   
  }   
  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=3/(exp(-x[1]+x[2]*x[2])+exp(-exp(-x[1]))   
  +exp(2*x[1]-x[2]));   
      }   
  }   
  void   keep_the_best()   
  {int   mem;   
    int   i;   
    cur_best=0;   
      
    for(mem=0;mem<POPSIZE;mem++)   
      {if(population[mem].fitness>   
  population[POPSIZE].fitness)   
        {cur_best=mem;   
          population[POPSIZE].fitness=population[mem].fitness;   
        }   
      }   
      for(i=0;i<NVARS;i++)   
  population[POPSIZE].gene[i]=population[cur_best].gene[i];   
  }   
  void   elitist()   
  {int   i;   
    double   best,worst;   
    int   best_mem,worst_mem;   
      
    best=population[0].fitness;   
    worst=population[0].fitness;   
    for(i=0;i<POPSIZE;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>=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;   
      }   
  }   
  void   select(void)   
  {int   mem,i,j,k;   
    double   sum=0;   
    double   p;   
    
    for(mem=0;mem<POPSIZE;mem++)   
      {sum+=population[mem].fitness;   
      }   
    for(mem=0;mem<POPSIZE;mem++)   
      {population[mem].rfitness=population[mem].fitness/sum;   
    }   
    population[0].cfitness=population[0].rfitness;   
  for(mem=1;mem<POPSIZE;mem++)   
    {population[mem].cfitness=population[mem-1].cfitness+   
  population[mem-1].rfitness;   
  }   
  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&&   
  population[j+1].cfitness)   
            newpopulation[i]=population[j+1];   
    }   
  }   
  for(i=0;i<POPSIZE;i++)   
      population[i]=newpopulation[i];   
  }   
  void   crossover(void)   
  {int   i,mem,one;   
    int   first=0;   
    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;   
  }   
  }   
  }   
  void   Xover(int   one,int   two)   
  {int   i;   
    int   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]);   
  }   
  }   
  void   swap(double   *x,double   *y)   
  {double   temp;   
    
  temp=*x;   
  *x=*y;   
  *y=temp;   
  }   
    
  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)   
    {lbound=population[i].lower[j];   
      hbound=population[i].upper[j];   
  population[i].gene[j]=randval(lbound,hbound);   
  }   
  }   
  }   
    
  void   report(void)   
  {int   i;   
    double   best_val;   
    double   avg;   
    double   stddev;   
    double   square_sum;   
    double   sum_square;   
    double   sum;   
  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*(double)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);   
  }   
  void   main(void)   
  {int   i;   
    if((galog=fopen("galog.txt","w"))==NULL)   
        {exit(1);   
        }   
  generation=0;   
  fprintf(galog,"\ngeneration   best   average   standard\n");   
  fprintf(galog,"number   value   fitnenss   deviation\n");   
      initialize();   
    evaluate();   
    keep_the_best();   
  while(generation<MAXGENS)   
  {generation++;   
    printf("%d\n",generation);   
    select();   
    crossover();   
    mutate();   
        s=population[POPSIZE].fitness;   
      report();   
    
    evaluate();   
  elitist();   
    
  }   
    
  fprintf(galog,"\n\nSimelation   completed\n");   
  fprintf(galog,"\nBest   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");   
  }