遗传算法matlab实现.docx

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1、MATLAB程序实现初始化：%初始化种群%pop_size:种群大小%chromo_size:染色体长度functioninitilize(pop_size,chromo_size)globalpop;fori=1:pop_sizeforj=1:chromo_sizepop(i,j)=round(rand);endendcleari;clearj;计算适应度：（该函数应该根据具体问题进行修改，这里优化的函数是前述的一维函数）%计算种群个体适应度，对不同的优化目标，此处需要改写%pop_size:种群大小%chromo_size:染色体长度f

2、unctionfitness(pop_size,chromo_size)globalfitness_value;globalpop;globalG;fori=1:pop_sizefitness_value(i)=0.;endfori=1:pop_sizeforj=1:chromo_sizeifpop(i,j)==1fitness_value(i)=fitness_value(i)+2^(j-1);endendfitness_value(i)=-1+fitness_value(i)*(3.-(-1.))/(2^chromo_size-1);f

3、itness_value(i)=-(fitness_value(i)-1).^2+4;endcleari;clearj;对个体按照适应度大小进行排序：%对个体按适应度大小进行排序，并且保存最佳个体%pop_size:种群大小%chromo_size:染色体长度functionrank(pop_size,chromo_size)globalfitness_value;globalfitness_table;globalfitness_avg;globalbest_fitness;globalbest_individual;globalbest

4、_generation;globalpop;globalG;fori=1:pop_sizefitness_table(i)=0.;endmin=1;temp=1;temp1(chromo_size)=0;fori=1:pop_sizemin=i;forj=i+1:pop_sizeiffitness_value(j)

5、)=temp;fork=1:chromo_sizetemp1(k)=pop(i,k);pop(i,k)=pop(min,k);pop(min,k)=temp1(k);endendendfori=1:pop_sizeifi==1fitness_table(i)=fitness_table(i)+fitness_value(i);elsefitness_table(i)=fitness_table(i-1)+fitness_value(i);endendfitness_tablefitness_avg(G)=fitness_table(pop_

6、size)/pop_size;iffitness_value(pop_size)>best_fitnessbest_fitness=fitness_value(pop_size);best_generation=G;forj=1:chromo_sizebest_individual(j)=pop(pop_size,j);endendcleari;clearj;cleark;clearmin;cleartemp;cleartemp1;选择操作：%轮盘赌选择操作%pop_size:种群大小%chromo_size:染色体长度%cross_rat

7、e:是否精英选择functionselection(pop_size,chromo_size,elitism)globalpop;globalfitness_table;fori=1:pop_sizer=rand*fitness_table(pop_size);first=1;last=pop_size;mid=round((last+first)/2);idx=-1;while(first<=last)&&(idx==-1)ifr>fitness_table(mid)first=mid;elseifr

8、last=mid;elseidx=mid;break;endmid=round((last+first)/2);if(last-first)==1idx=last;break;e