遗传算法matlab实现源程序

clc;clear;%各份订单基本数据phen=[1 2 3 4 5 6 7 8 9 10 11 12 13 14 41,52,-23,-46,-143,-74,-56,101,73,74,95,86,-35,32 65,23,-76,104,34,38,4,-23,55,-49,39,89,-86,52 7716,9887,12188,8819,4002,6119,3284,4607,5600,4587,9821,13024,6547,2684 500,400,1000,120,0,235,654,241,0,361,120,254,300,150 1,4,2,2,4,4,3,3,3,1,4,5,1,3 2.7,1.8,4,2.5,1.6,1,3.6,5,4.2,1.9,6.4,2.8,1.4,8]; hromlength=14; popsize=30; maxgen=500; pc=0.8; pm=0.04; for kem=1:popsizepopulation(kem,:)=randperm(hromlength); endpopulation;%评价目标函数值for uim=1:popsize vector=population(uim,:); obj(uim)=hanshu(hromlength,vector,phen);end%obj%min(obj)clear uim;objmin=min(obj);for sequ=1:popsize if obj(sequ)==objmin opti=population(sequ,:); endendclear sequ;fmax=22000;%==for gen=1:maxgen%选择操作%将求最小值的函数转化为适应度函数for indivi=1:popsize obj1(indivi)=1/obj(indivi);endclear indivi;%适应度函数累加总合total=0;for indivi=1:popsize total=total+obj1(indivi);endclear indivi;%每条染色体被选中的几率for indivi=1:popsize fitness1(indivi)=obj1(indivi)/total;endclear indivi;%各条染色体被选中的范围for indivi=1:popsize fitness(indivi)=0; for j=1:indivi fitness(indivi)=fitness(indivi)+fitness1(j); endendclear j;fitness;%选择适应度高的个体for ranseti=1:popsize ran=rand; while (ran>1||ran<0) ran=rand; end ran; if ran<=fitness(1) newpopulation(ranseti,:)=population(1,:); else for fet=2:popsize if (ran>fitness(fet-1))&&(ran<=fitness(fet)) newpopulation(ranseti,:)=population(fet,:); end end endendclear ran;newpopulation;%交叉for int=1:2:popsize-1 popmoth=newpopulation(int,:); popfath=newpopulation(int+1,:); popcross(int,:)=popmoth; popcross(int+1,:)=popfath; randnum=rand; if(randnum< P> cpoint1=round(rand*hromlength); cpoint2=round(rand*hromlength); while (cpoint2==cpoint1) cpoint2=round(rand*hromlength); end if cpoint1>cpoint2 tem=cpoint1; cpoint1=cpoint2; cpoint2=tem; end cpoint1; cpoint2; for term=cpoint1+1:cpoint2 for ss=1...