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unsigned char fflg[MAX_FOOD]; /* 食物有无状态标志变量 */ unsigned char world[MAX_WX][MAX_WY]; /* 虚拟环境的数据 */ unsigned char /* 各中行为模式数据 */ life1[5][5]={{0,0,1,0,0},{0,1,0,1,0},{1,0,0,0,1},{0,1,0,1,0},{0,0,1,0,0}}; unsigned char life2[5][5]={{1,1,1,1,1},{1,0,0,0,1},{1,0,0,0,1},{1,0,0,0,1},{1,1,1,1,1}}; unsigned char food1[5][5]={{0,0,0,1,0},{0,0,1,1,0},{0,1,0,1,0},{0,0,1,1,0},{0,0,0,1,0}}; unsigned char food2[5][5]={{0,0,0,1,0},{0,0,1,1,0},{0,1,1,1,0},{0,0,1,1,0},{0,0,0,1,0}}; int pop_size; /* 个体总数 */ int iatr[MAX_POP][4]; /* 个体属性 */ /* iatr[][0] 个体当前位置x坐标 */ /* iatr[][1] 个体当前位置y坐标 */ /* iatr[][2] 内部能量 */ /* iatr[][3] 年龄属性 */ int food_size; /* 食物总数 */ int fatr[MAX_FOOD][4]; /* 食物属性 */ /* fatr[][0] 食物当前位置x坐标 */ /* fatr[][1] 食物当前位置y坐标 */ /* fatr[][2]=0 : 植物性 =1:动物性 */ /* fatr[][3] 新鲜程度 */ int wx,wy; /* 虚拟环境的长宽度 */ void uni_crossover(gene,g1,g2,g3,ratio1,g_length) /* 均匀交叉 */ unsigned char *gene; /* 遗传基因 */ int g1,g2,g3; /* g1 g2 父个体编号 g3 子个体编号 */ double ratio1; /* 父个体g1被选中的概率 */ int g_length; /* 个体遗传基因的位长 */ { unsigned char *gene1; /* 父1遗传基因的指针 */ unsigned char *gene2; /* 父2遗传基因的指针 */ unsigned char *gene3; /* 子遗传基因的指针 */ double rnd,r1; int i; gene1=gene+g_length*g1; gene2=gene+g_length*g2; gene3=gene+g_length*g3; r1=(int)(10000.0*ratio1); for(i=0;i 您已经评价过！ 您已经评价过！ } void g_draw_world() /* 显示虚拟环境画面 */ { int i,j; for(i=0;i 您已经评价过！ 您已经评价过！ x=220; for(i=0;i<12;i++) { y=202+i*16; for(j=bits [ i][0];j<=bits [ i][1];j++) if(g[j]==0) g_text(x+(j-bits [ i][0])*16,y,4,"0"); else g_text(x+(j-bits [ i][0])*16,y,4,"1"); } } } void g_disp_char(x,y,x1,y1,x2,y2,v) int x,y,x1,y1,x2,y2; unsigned char v; { char c[10]; if(x>=x1&& x<=x2-8 && y>=y1 && y<=y2-10) { switch(v) { case 0: strcpy(c,"0");break; case 1: strcpy(c,"+");break; case 2: strcpy(c,"-");break; case 3: strcpy(c,"x"); } g_text(x,y,15,c); } } void remove_life(n) /* 消除第n个个体 */ int n; { iflg[n]=0; world[iatr[n][0]][iatr[n][1]]=0; g_disp_unit(iatr[n][0],iatr[n][1],0); if(food_size+1<=MAX_FOOD) { food_size++; fatr[food_size-1][0]=iatr[n][0]; fatr[food_size-1][1]=iatr[n][1]; fatr[food_size-1][2]=1; fatr[food_size-1][3]=0; fflg[food_size-1]=1; world[iatr[n][0]][iatr[n][1]]=5; g_disp_unit(iatr[n][0],iatr[n][1],5); } } void remove_food(n) /* 消除第n个食物 */ int n; { fflg[n]=0; world[fatr[n][0]][fatr[n][1]]=0; g_disp_unit(fatr[n][0],fatr[n][1],0); } void make_lives_and_foods() /* 设置虚拟环境中生物与食物 */ { int x,y,i,j; pop_size=0; food_size=0; for(y=0;y 您已经评价过！ 您已经评价过！ } } int get_world(x,y) /*坐标(x,y)处环境值 */ int x,y; { if(x>=0 && x 您已经评价过！ 您已经评价过！ /* 计算双方的 Attack量 */ sa1=(double)decode_gene(n,19,3); da1=(double)decode_gene(n,22,3); sa2=(double)decode_gene(n2,19,3); da2=(double)decode_gene(n2,22,3); rnd1=(double)random(1001)/1000.0; rnd2=(double)random(1001)/1000.0; attack1=(double)iatr[n][2]+sa1*20.0/7.0*rnd1+da1*20.0/7.0*rnd2; rnd1=(double)random(1001)/1000.0; rnd2=(double)random(1001)/1000.0; attack2=(double)iatr[n2][2]+sa2*20.0/7.0*rnd1+da2*20.0/7.0*rnd2; /* 减少内部能量 */ La1=decode_gene(n,25,3); La2=decode_gene(n2,25,3); rnd1=(double)random(1001)/1000.0; iatr[n][2]=iatr[n][2]-(int)((double)La1*rnd1); rnd2=(double)random(1001)/1000.0; iatr[n2][2]=iatr[n2][2]-(int)((double)La2*rnd2); if(attack1>=attack2) /* 胜者: n 败者:n2 */ iatr[n2][2]=iatr[n2][2]-40; else /* 胜者: n2 败者:n */ iatr[n][2]=iatr[n][2]-40; if(iatr[n][2]<=0) remove_life(n); if(iatr[n2][2]<=0) remove_life(n2); } void act2_eat(n) /* 个体n获取行动范围内的食物 */ int n; { int sft[8][2]={{1,0},{1,1},{0,1},{-1,1}, {-1,0},{-1,-1},{0,-1},{1,-1}}; int x1,y1,x2,y2,n2,ef; int found,rndnum; x1=iatr[n][0];y1=iatr[n][1]; /* 获取食物位置(x2,y2) */ found=0; while(found==0) { rndnum=random(8); x2=x1+sft[rndnum][0]; y2=y1+sft[rndnum][1]; if(get_world(x2,y2)==3||get_world(x2,y2)==5) found=1; } /* 增加内部能量 */ ef=decode_gene(n,28,4); /* 食物吸取效率 */ iatr[n][2]=iatr[n][2]+(int)(40.0*(50.0+(double)ef*50.0/15.0)/100.0); if(iatr[n][2]>100) iatr[n][2]=100; /* 检查食物号n2 */ found=0;n2=0; while(found==0) { if(fatr[n2][0]==x2 && fatr[n2][1]==y2 && fflg[n2]==1) found=1; else n2++; } remove_food(n2); } void act3_makechild(n) /* 个体n与行动范围内的其他生物个体交配产生子个体 */ int n; { int i,j,k,x,y,x2,y2,found,n2,trial; int x3,y3; double rnd; if(pop_size+1<=MAX_POP) { x=iatr[n][0];y=iatr[n][1]; found=0; while(found==0) { x2=x+random(3)-1; y2=y+random(3)-1; if(x2!=x||y2!=y) if(get_world(x2,y2)==gene[n][0]+1); found=1; } /* 检查交配对象个体号n2 */ found=0; n2=0; while(found==0) { if((iatr[n2][0]==x2 || iatr[n2][1]==y2) && iflg[n2]==1) found=1; else n2++; if(n2>=pop_size-1) return; } /* 确定产生个体位置 */ found=0;trial=0; while(found==0 && trial<50) { i=random(3)-1;j=random(3)-1; k=random(2); if(k==0) { x3=x+i;y3=y+j;} else { x3=x2+i;y3=y2+j;} if(get_world(x3,y3)==0) found=1; trial++; } if(found==1) { /* 个体 n与个体 n2产生子个体 */ pop_size++; /* 均匀交叉 */ uni_crossover(gene,n,n2,pop_size-1,0.5,G_LENGTH); /* 变异 */ for(i=1;i 您已经评价过！ 您已经评价过！ cr=decode_gene(n,16,3); rnd=(double)random(10001)/10000.0; if(rnd<=(double)cr/7.0) { action=random(3); while(act[action]==0) action=random(3); } else { ca=decode_gene(n,13,3); /* ca行动特点 */ if(ca<3) pattern=0;else pattern=ca-2; /* 基本行动模式pattern 0-5 */ i=0; action=pat[pattern] [ i]-1; while( act[action]==0) { i++; action=pat[pattern] [ i]-1; } } switch(action+1) { case 1: act1_attack(n);break; case 2: act2_eat(n);break; case 3: act3_makechild(n); } } void init_flags() /* 状态标志初始化 */ { int i; for(i=0;ifor(i=0;i 您已经评价过！ 您已经评价过！ java jgap实例 package demo; import java.io.File; import java.io.FileNotFoundException; import org.jgap.Chromosome; import org.jgap.Configuration; import org.jgap.FitnessFunction; import org.jgap.Gene; import org.jgap.Genotype; import org.jgap.IChromosome; import org.jgap.UnsupportedRepresentationException; import org.jgap.data.DataTreeBuilder; import org.jgap.data.IDataCreators; import org.jgap.impl.DefaultConfiguration; import org.jgap.impl.IntegerGene; import org.jgap.xml.XMLDocumentBuilder; import org.jgap.xml.XMLManager; import org.w3c.dom.Document; import examples.MinimizingMakeChangeFitnessFunction; /** * Copyright: youhow.net (c) 2005-2008 * Company: youhow.net * * 遗传算法DEMO * 从文件读取数字进行排序 * * @version 1.0 (2008-6-12 neo(starneo@gmail.com)) */ public class GaDemo { /** * @param args */ public static void main(String[] args) throws Exception { //构型 Configuration conf = new DefaultConfiguration(); // Care that the fittest individual of the current population is // always taken to the next generation. // Consider: With that, the pop. size may exceed its original // size by one sometimes! // ....- conf.setPreservFittestIndividual(true); // Set the fitness function we want to use, which is our // MinimizingMakeChangeFitnessFunction. We construct it with // the target amount of change passed in to this method. // ...-- FitnessFunction myFunc = new GaDemoFitnessFunction(); conf.setFitnessFunction(myFunc); Gene[] sampleGenes = new Gene[2]; sampleGenes[0] = new IntegerGene(conf,0,9999999); sampleGenes[1] = new IntegerGene(conf,0,9999999); IChromosome sampleChromosome = new Chromosome(conf, sampleGenes); conf.setSampleChromosome(sampleChromosome); conf.setPopulationSize(80); Genotype population; try { Document doc = XMLManager.readFile(new File("GADEMO.xml")); population = XMLManager.getGenotypeFromDocument(conf, doc); } catch (UnsupportedRepresentationException uex) { // JGAP codebase might have changed between two consecutive runs. // ....-- population = Genotype.randomInitialGenotype(conf); } catch (FileNotFoundException fex) { population = Genotype.randomInitialGenotype(conf); } // Evolve the population. Since we don't know what the best answer // is going to be, we just evolve the max number of times. // ....--- long startTime = System.currentTimeMillis(); for (int i = 0; i < 50; i++) { population.evolve(); } long endTime = System.currentTimeMillis(); System.out.println("Total evolution time: " + (endTime - startTime) + " ms"); IChromosome bestSolutionSoFar = population.getFittestChromosome(); System.out.println("\t" + MinimizingMakeChangeFitnessFunction.getNumberOfCoinsAtGene(bestSolutionSoFar, 0)); System.out.println("\t" + MinimizingMakeChangeFitnessFunction.getNumberOfCoinsAtGene(bestSolutionSoFar, 1)); DataTreeBuilder builder = DataTreeBuilder.getInstance(); IDataCreators doc2 = builder.representGenotypeAsDocument(population); // create XML document from generated tree XMLDocumentBuilder docbuilder = new XMLDocumentBuilder(); Document xmlDoc = (Document) docbuilder.buildDocument(doc2); XMLManager.writeFile(xmlDoc, new File("GADEMO.xml")); } } 您已经评价过！ 您已经评价过！ package demo; import org.jgap.FitnessFunction; import org.jgap.IChromosome; /** * Copyright: youhow.net (c) 2005-2008 * Company: youhow.net * * GaDemoFitnessFunction class说明 * * @version 1.0 (2008-6-12 neo(starneo@gmail.com)) */ public class GaDemoFitnessFunction extends FitnessFunction { /** * Comment for 您已经评价过！ 您已经评价过！serialVersionUID */ private static final long serialVersionUID = 1L; /** * @see org.jgap.FitnessFunction#evaluate(org.jgap.IChromosome) */ @Override protected double evaluate(IChromosome a_subject) { int totalCoins = 1; int numberOfGenes = a_subject.size(); for (int i = 0; i < numberOfGenes; i++) { totalCoins += (Integer) a_subject.getGene(i).getAllele(); } return totalCoins; } }/*全部完成*/
