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1、遗传算法作业一：用浮点数编码解决以下问题。f(x,x)=,icos01)Xi*,icos01)Xi1212i=1i=1其中一10X,X1012求minf(x,x)12二：构造过程。第一步：确定决策变量和约束条件。题目中已给出了该问题的决策变量及约束条件。第二步：建立优化模型。既是题目给出所要求的。第三步：确定编码方法。第四步：确定解码方法。第五步：确定个体评价方法。这里可将个体的适应度直接取为对应的目标函数值，并且不再对它做其他变化处理，即有F(X)=f(x1,x2)第六步：设计遗传算子。选择运算使用比例运算算子；交叉运算是用单点交叉算子；变异运算使用基本位变异算子。第七步：确定遗传算法的运行
2、参数。对于本例，设计基本遗传算法的运行参数如下MAXGENS=80POPSIZE=20NVARS=2PXOVER=0.2PMUTATION=0.1三：代码如下。/*最大世代数*/*种群大小*/*变量个数*/*交叉概率*/*变异概率*/#includestdafx.h#include#include#include#defineMAXGENS80#definePOPSIZE20#defineNVARS2#definePXOVER0.2#definePMUTATION0.1#defineFALSE0#defineTRUE1intgeneration;/*最大世代数*/*种群大小*/*变量个数*/*
3、交叉概率*/*变异概率*/*当前世代*/intcur_best;/*最佳个体*/structgenotypedoublegeneNVARS;doublefitness;doubleupperNVARS;doublelowerNVARS;doublerfitness;doublecfitness;structgenotypepopulationPOPSIZE+1;structgenotypenewpopulationPOPSIZE+1;voidinitialize(void);doublerandval(double,double);voidevaluate(void);voidkeep_the
4、_best(void);voidelitist(void);voidselect(void);voidcrossover(void);voidXover(int,int);voidswap(double*,double*);voidmutate(void);voidreport(void);voidinitialize(void)inti,j;doublelbound,ubound;for(i=0;iNVARS;i+)scanf(%lf,&lbound);scanf(%lf,&ubound);for(j=0;jPOPSIZE;j+)populationj.fitness=0;populatio
5、nj.rfitness=0;populationj.cfitness=0;populationj.loweri=lbound;populationj.upperi=ubound;populationj.genei=randval(populationj.loweri,populationj.upperi);doublerandval(doublelow,doublehigh)doubleval;val=(double)(rand()%1000)/1000.0)*(high-low)+low;return(val);voidevaluate(void)intmem;inti;intX=0;int
6、Y=0;doublexNVARS+1;for(mem=0;memPOPSIZE;mem+)for(i=0;iNVARS;i+)xi+1=populationmem.genei;for(i=1;i=5;i+)X=X+(i*cos(i+1)*x1+i);Y=Y+(i*cos(i+1)*x2+i);populationmem.fitness=(-1)*X*Y;voidkeep_the_best()intmem;inti;cur_best=0;for(mem=0;mempopulationPOPSIZE.fitness)cur_best=mem;populationPOPSIZE.fitness=po
7、pulationmem.fitness;for(i=0;iNVARS;i+)populationPOPSIZE.genei=populationcur_best.genei;voidelitist()inti;doublebest,worst;intbest_mem,worst_mem;best=population0.fitness;worst=population0.fitness;for(i=0;ipopulationi+1.fitness)if(populationi.fitness=best)best=populationi.fitness;best_mem=i;if(populat
8、ioni+1.fitness=worst)worst=populationi+1.fitness;worst_mem=i+1;elseif(populationi.fitness=best)best=populationi+1.fitness;best_mem=i+1;if(best=populationPOPSIZE.fitness)for(i=0;iNVARS;i+)populationPOPSIZE.genei=populationbest_mem.genei;populationPOPSIZE.fitness=populationbest_mem.fitness;elsefor(i=0
9、;iNVARS;i+)populationworst_mem.genei=populationPOPSIZE.genei;populationworst_mem.fitness=populationPOPSIZE.fitness;voidselect(void)intmem,i,j,k;doublesum=0;doublep;for(mem=0;memPOPSIZE;mem+)sum+=populationmem.fitness;for(mem=0;memPOPSIZE;mem+)populationmem.rfitness=populationmem.fitness/sum;populati
10、on0.cfitness=population0.rfitness;for(mem=1;memPOPSIZE;mem+)populationmem.cfitness=populationmem-1.cfitness+populationmem.rfitness;for(i=0;iPOPSIZE;i+)p=rand()%1000/1000.0;if(ppopulation0.cfitness)newpopulationi=population0;elsefor(j=0;j=populationj.cfitness&ppopulationj+1.cfitness)newpopulationi=po
11、pulationj+1;for(i=0;iPOPSIZE;i+)populationi=newpopulationi;voidcrossover(void)inti,mem,one;intfirst=0;doublex;for(mem=0;memPOPSIZE;+mem)x=rand()%1000/1000.0;if(x1)if(NVARS=2)point=1;elsepoint=(rand()%(NVARS-1)+1;for(i=0;ipoint;i+)swap(&populationone.genei,&populationtwo.genei);voidswap(double*x,doub
12、le*y)doubletemp;temp=*x;*x=*y;*y=temp;voidmutate(void)inti,j;doublelbound,hbound;doublex;for(i=0;iPOPSIZE;i+)for(j=0;jNVARS;j+)x=rand()%1000/1000.0;if(xPMUTATION)lbound=populationi.lowerj;hbound=populationi.upperj;populationi.genej=randval(lbound,hbound);voidreport(void)inti;doublebest_val;doubleavg;doublestddev;doublesum_square;doublesquare_sum;doublesum;sum=0.0;sum_square=0.0;for(i=0;iPOPSIZE;i+)sum+=populationi.fitness;sum_square+=populationi.fitness*populationi.
