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1、这是一个非常简单的遗传算法源代码， 是由 Denis Cormier (North Carolina State University) 开发的， Sita S.Raghavan (University of North Carolina at Charlotte)修正。代码保证尽可能少， 实际上也不必查错。 对一特定的应用修正此代码，用户只需改变常数的定义并且定义“ 评价函数 ” 即可。注意代码的设计是求最大值，其中的目标函数只能取正值； 且函数值和个体的适应值之间没有区别。该系统使用比率选择、精华模型、单点杂交和均匀变异。如果用Gaussian 变异替换均匀变异，可能得到更好的效果。 代码
2、没有任何图形， 甚至也没有屏幕输出， 主要是保证在平台之间的高可移植性。读者可以从ftp.uncc.edu, 目录 coe/evol 中的文件 prog.c 中获得。要求输入的文件应该命名为,gadata.txt?；系统产生的输出文件为,galog.txt?。输入的文件由几行组成：数目对应于变量数。且每一行提供次序对应于变量的上下界。 如第一行为第一个变量提供上下界，第二行为第二个变量提供上下界，等等。/*/ /* This is a simple genetic algorithm implementation where the */ /* evaluation function take
3、s positive values only and the */ /* fitness of an individual is the same as the value of the */ /* objective function */*/ #include #include #include /* Change any of these parameters to match your needs */ #define POPSIZE 50 /* population size */ #define MAXGENS 1000 /* max. number of generations
4、*/ #define NVARS 3 /* no. of problem variables */ #define PXOVER 0.8 /* probability of crossover */ #define PMUTATION 0.15 /* probability of mutation */ #define TRUE 1 #define FALSE 0 int generation; /* current generation no. */ int cur_best; /* best individual */ FILE *galog; /* an output file */ s
5、truct genotype /* genotype (GT), a member of the population */ double geneNVARS; /* a string of variables */ 名师资料总结 - - -精品资料欢迎下载 - - - - - - - - - - - - - - - - - - 名师精心整理 - - - - - - - 第 1 页，共 21 页 - - - - - - - - - double fitness; /* GTs fitness */ double upperNVARS; /* GTs variables upper bound
6、*/ double lowerNVARS; /* GTs variables lower bound */ double rfitness; /* relative fitness */ double cfitness; /* cumulative fitness */ ; struct genotype populationPOPSIZE+1; /* population */ struct genotype newpopulationPOPSIZE+1; /* new population; */ /* replaces the */ /* old generation */ /* Dec
7、laration of procedures used by this genetic algorithm */ 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 repo
8、rt(void); /*/ /* Initialization function: Initializes the values of genes */ /* within the variables bounds. It also initializes (to zero) */ /* all fitness values for each member of the population. It */ /* reads upper and lower bounds of each variable from the */ /* input file gadata.txt. It rando
9、mly generates values */ /* between these bounds for each gene of each genotype in the */ /* population. The format of the input file gadata.txt is */ /* var1_lower_bound var1_upper bound */ /* var2_lower_bound var2_upper bound . */ /*/ void initialize(void) FILE *infile; int i, j; double lbound, ubo
10、und; 名师资料总结 - - -精品资料欢迎下载 - - - - - - - - - - - - - - - - - - 名师精心整理 - - - - - - - 第 2 页，共 21 页 - - - - - - - - - if (infile = fopen(gadata.txt,r)=NULL) fprintf(galog,nCannot open input file!n); exit(1); /* initialize variables within the bounds */ for (i = 0; i NVARS; i+) fscanf(infile, %lf,&lbound
11、); fscanf(infile, %lf,&ubound); for (j = 0; j POPSIZE; j+) populationj.fitness = 0; populationj.rfitness = 0; populationj.cfitness = 0; populationj.loweri = lbound; populationj.upperi= ubound; populationj.genei = randval(populationj.loweri, populationj.upperi); fclose(infile); /*/ /* Random value ge
12、nerator: Generates a value within bounds */ /*/ double randval(double low, double high) double val; val = (double)(rand()%1000)/1000.0)*(high - low) + low; return(val); /*/ /* Evaluation function: This takes a user defined function. */ /* Each time this is changed, the code has to be recompiled. */
13、/* The current function is: x12-x1*x2+x3 */ /*/ 名师资料总结 - - -精品资料欢迎下载 - - - - - - - - - - - - - - - - - - 名师精心整理 - - - - - - - 第 3 页，共 21 页 - - - - - - - - - void evaluate(void) int mem; int i; double xNVARS+1; for (mem = 0; mem POPSIZE; mem+) for (i = 0; i NVARS; i+) xi+1 = populationmem.genei; popu
14、lationmem.fitness = (x1*x1) - (x1*x2) + x3; /*/ /* Keep_the_best function: This function keeps track of the */ /* best member of the population. Note that the last entry in */ /* the array Population holds a copy of the best individual */ /*/ void keep_the_best() int mem; int i; cur_best = 0; /* sto
15、res the index of the best individual */ for (mem = 0; mem populationPOPSIZE.fitness) cur_best = mem; populationPOPSIZE.fitness = populationmem.fitness; /* once the best member in the population is found, copy the genes */ for (i = 0; i NVARS; i+) populationPOPSIZE.genei = populationcur_best.genei; /
16、*/ /* Elitist function: The best member of the previous generation */ /* is stored as the last in the array. If the best member of */ 名师资料总结 - - -精品资料欢迎下载 - - - - - - - - - - - - - - - - - - 名师精心整理 - - - - - - - 第 4 页，共 21 页 - - - - - - - - - /* the current generation is worse then the best member o
17、f the */ /* previous generation, the latter one would replace the worst */ /* member of the current population */ /*/ void elitist() int i; double best, worst; /* best and worst fitness values */ int best_mem, worst_mem; /* indexes of the best and worst member */ best = population0.fitness; worst =
18、population0.fitness; for (i = 0; i populationi+1.fitness) if (populationi.fitness = best) best = populationi.fitness; best_mem = i; if (populationi+1.fitness = worst) worst = populationi+1.fitness; worst_mem = i + 1; else if (populationi.fitness = best) best = populationi+1.fitness; best_mem = i + 1
19、; /* if best individual from the new population is better than */ /* the best individual from the previous population, then */ 名师资料总结 - - -精品资料欢迎下载 - - - - - - - - - - - - - - - - - - 名师精心整理 - - - - - - - 第 5 页，共 21 页 - - - - - - - - - /* copy the best from the new population; else replace the */ /*
20、 worst individual from the current population with the */ /* best one from the previous generation */ if (best = populationPOPSIZE.fitness) for (i = 0; i NVARS; i+) populationPOPSIZE.genei = populationbest_mem.genei; populationPOPSIZE.fitness = populationbest_mem.fitness; else for (i = 0; i NVARS; i
21、+) populationworst_mem.genei = populationPOPSIZE.genei; populationworst_mem.fitness = populationPOPSIZE.fitness; /*/ /* Selection function: Standard proportional selection for */ /* maximization problems incorporating elitist model - makes */ /* sure that the best member survives */ /*/ void select(
22、void) int mem, i, j, k; double sum = 0; double p; /* find total fitness of the population */ for (mem = 0; mem POPSIZE; mem+) sum += populationmem.fitness; /* calculate relative fitness */ for (mem = 0; mem POPSIZE; mem+) populationmem.rfitness = populationmem.fitness/sum; population0.cfitness = pop
23、ulation0.rfitness; /* calculate cumulative fitness */ for (mem = 1; mem POPSIZE; mem+) 名师资料总结 - - -精品资料欢迎下载 - - - - - - - - - - - - - - - - - - 名师精心整理 - - - - - - - 第 6 页，共 21 页 - - - - - - - - - populationmem.cfitness = populationmem-1.cfitness + populationmem.rfitness; /* finally select survivors
24、using cumulative fitness. */ for (i = 0; i POPSIZE; i+) p = rand()%1000/1000.0; if (p population0.cfitness) newpopulationi = population0; else for (j = 0; j = populationj.cfitness & ppopulationj+1.cfitness) newpopulationi = populationj+1; /* once a new population is created, copy it back */ for (i =
25、 0; i POPSIZE; i+) populationi = newpopulationi; /*/ /* Crossover selection: selects two parents that take part in */ /* the crossover. Implements a single point crossover */ /*/ void crossover(void) int i, mem, one; int first = 0; /* count of the number of members chosen */ double x; for (mem = 0;
26、mem POPSIZE; +mem) x = rand()%1000/1000.0; if (x 1) if(NVARS = 2) point = 1; else point = (rand() % (NVARS - 1) + 1; for (i = 0; i point; i+) swap(&populationone.genei, &populationtwo.genei); /*/ /* Swap: A swap procedure that helps in swapping 2 variables */ /*/ void swap(double *x, double *y) doub
27、le temp; temp = *x; *x = *y; *y = temp; /*/ 名师资料总结 - - -精品资料欢迎下载 - - - - - - - - - - - - - - - - - - 名师精心整理 - - - - - - - 第 8 页，共 21 页 - - - - - - - - - /* Mutation: Random uniform mutation. A variable selected for */ /* mutation is replaced by a random value between lower and */ /* upper bounds of
28、this variable */ /*/ 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) /* find the bounds on the variable to be mutated */ lbound = populationi.lowerj; hbound = populationi.upperj; populationi.gen
29、ej = randval(lbound, hbound); /*/ /* Report function: Reports progress of the simulation. Data */ /* dumped into the output file are separated by commas */ /*/ void report(void) int i; double best_val; /* best population fitness */ double avg; /* avg population fitness */ double stddev; /* std. devi
30、ation of population fitness */ double sum_square; /* sum of square for std. calc */ double square_sum; /* square of sum for std. calc */ double sum; /* total population fitness */ sum = 0.0; sum_square = 0.0; for (i = 0; i POPSIZE; i+) 名师资料总结 - - -精品资料欢迎下载 - - - - - - - - - - - - - - - - - - 名师精心整理
31、- - - - - - - 第 9 页，共 21 页 - - - - - - - - - sum += populationi.fitness; sum_square += populationi.fitness * populationi.fitness; avg = sum/(double)POPSIZE; square_sum = avg * avg * POPSIZE; stddev = sqrt(sum_square - square_sum)/(POPSIZE - 1); best_val = populationPOPSIZE.fitness; fprintf(galog, n%
32、5d, %6.3f, %6.3f, %6.3f nn, generation, best_val, avg, stddev); /*/ /* Main function: Each generation involves selecting the best */ /* members, performing crossover & mutation and then */ /* evaluating the resulting population, until the terminating */ /* condition is satisfied */ /*/ void main(voi
33、d) int i; if (galog = fopen(galog.txt,w)=NULL) exit(1); generation = 0; fprintf(galog, n generation best average standard n); fprintf(galog, number value fitness deviation n); initialize(); evaluate(); keep_the_best(); while(generationMAXGENS) generation+; select(); crossover(); mutate(); report();
34、名师资料总结 - - -精品资料欢迎下载 - - - - - - - - - - - - - - - - - - 名师精心整理 - - - - - - - 第 10 页，共 21 页 - - - - - - - - - evaluate(); elitist(); fprintf(galog,nn Simulation completedn); fprintf(galog,n Best member: n); for (i = 0; i NVARS; i+) fprintf (galog,n var(%d) = %3.3f,i,populationPOPSIZE.genei); fprintf
35、(galog,nn Best fitness = %3.3f,populationPOPSIZE.fitness); fclose(galog); printf(Successn); /*/ /*/ /* This is a simple genetic algorithm implementation where the */ /* evaluation function takes positive values only and the */ /* fitness of an individual is the same as the value of the */ /* objecti
36、ve function */*/ #include #include #include /* Change any of these parameters to match your needs */ #define POPSIZE 50 /* population size */ #define MAXGENS 1000 /* max. number of generations */ #define NVARS 3 /* no. of problem variables */ #define PXOVER 0.8 /* probability of crossover */ #define
37、 PMUTATION 0.15 /* probability of mutation */ #define TRUE 1 #define FALSE 0 int generation; /* current generation no. */ int cur_best; /* best individual */ FILE *galog; /* an output file */ struct genotype /* genotype (GT), a member of the population */ double geneNVARS; /* a string of variables *
38、/ double fitness; /* GTs fitness */ 名师资料总结 - - -精品资料欢迎下载 - - - - - - - - - - - - - - - - - - 名师精心整理 - - - - - - - 第 11 页，共 21 页 - - - - - - - - - double upperNVARS; /* GTs variables upper bound */ double lowerNVARS; /* GTs variables lower bound */ double rfitness; /* relative fitness */ double cfitn
39、ess; /* cumulative fitness */ ; struct genotype populationPOPSIZE+1; /* population */ struct genotype newpopulationPOPSIZE+1; /* new population; */ /* replaces the */ /* old generation */ /* Declaration of procedures used by this genetic algorithm */ void initialize(void); double randval(double, dou
40、ble); 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); /*/ /* Initialization function: Initializes the values of genes */ /* within the variables bound
41、s. It also initializes (to zero) */ /* all fitness values for each member of the population. It */ /* reads upper and lower bounds of each variable from the */ /* input file gadata.txt. It randomly generates values */ /* between these bounds for each gene of each genotype in the */ /* population. Th
42、e format of the input file gadata.txt is */ /* var1_lower_bound var1_upper bound */ /* var2_lower_bound var2_upper bound . */ /*/ void initialize(void) FILE *infile; int i, j; double lbound, ubound; if (infile = fopen(gadata.txt,r)=NULL) 名师资料总结 - - -精品资料欢迎下载 - - - - - - - - - - - - - - - - - - 名师精心整
43、理 - - - - - - - 第 12 页，共 21 页 - - - - - - - - - fprintf(galog,nCannot open input file!n); exit(1); /* initialize variables within the bounds */ for (i = 0; i NVARS; i+) fscanf(infile, %lf,&lbound); fscanf(infile, %lf,&ubound); for (j = 0; j POPSIZE; j+) populationj.fitness = 0; populationj.rfitness
44、= 0; populationj.cfitness = 0; populationj.loweri = lbound; populationj.upperi= ubound; populationj.genei = randval(populationj.loweri, populationj.upperi); fclose(infile); /*/ /* Random value generator: Generates a value within bounds */ /*/ double randval(double low, double high) double val; val =
45、 (double)(rand()%1000)/1000.0)*(high - low) + low; return(val); /*/ /* Evaluation function: This takes a user defined function. */ /* Each time this is changed, the code has to be recompiled. */ /* The current function is: x12-x1*x2+x3 */ /*/ 名师资料总结 - - -精品资料欢迎下载 - - - - - - - - - - - - - - - - - -
46、名师精心整理 - - - - - - - 第 13 页，共 21 页 - - - - - - - - - void evaluate(void) int mem; int i; double xNVARS+1; for (mem = 0; mem POPSIZE; mem+) for (i = 0; i NVARS; i+) xi+1 = populationmem.genei; populationmem.fitness = (x1*x1) - (x1*x2) + x3; /*/ /* Keep_the_best function: This function keeps track of
47、the */ /* best member of the population. Note that the last entry in */ /* the array Population holds a copy of the best individual */ /*/ void keep_the_best() int mem; int i; cur_best = 0; /* stores the index of the best individual */ for (mem = 0; mem populationPOPSIZE.fitness) cur_best = mem; pop
48、ulationPOPSIZE.fitness = populationmem.fitness; /* once the best member in the population is found, copy the genes */ for (i = 0; i NVARS; i+) populationPOPSIZE.genei = populationcur_best.genei; /*/ /* Elitist function: The best member of the previous generation */ /* is stored as the last in the ar
49、ray. If the best member of */ /* the current generation is worse then the best member of the */ 名师资料总结 - - -精品资料欢迎下载 - - - - - - - - - - - - - - - - - - 名师精心整理 - - - - - - - 第 14 页，共 21 页 - - - - - - - - - /* previous generation, the latter one would replace the worst */ /* member of the current pop
50、ulation */ /*/ void elitist() int i; double best, worst; /* best and worst fitness values */ int best_mem, worst_mem; /* indexes of the best and worst member */ best = population0.fitness; worst = population0.fitness; for (i = 0; i populationi+1.fitness) if (populationi.fitness = best) best = popula