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。代码没有任何图形，甚至也
2、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 takes positive values only
3、 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 */#define NVARS 3 /* no. of p
4、roblem variables */#define PXOVER 0.8 /* probability of crossover */#define PMUTATION 0.15 /* probability of mutation */#define TRUE 1#define FALSE 0int generation; /* current generation no. */int cur_best; /* best individual */FILE *galog; /* an output file */struct genotype /* genotype (GT), a mem
5、ber of the population */double geneNVARS; /* a string of variables */double fitness; /* GTs fitness */double upperNVARS; /* GTs variables upper bound */double lowerNVARS; /* GTs variables lower bound */double rfitness; /* relative fitness */double cfitness; /* cumulative fitness */;struct genotype p
6、opulationPOPSIZE+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, double);void evaluate(void);void keep_the_best(void);void eli
7、tist(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 bounds. It also initializes (to zero) */* all fitness values for each member
8、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. The format of the input file gadata.txt is */* var1_lower_bound var1_upper bound */*
9、 var2_lower_bound var2_upper bound . */*/void initialize(void)FILE *infile;int i, j;double lbound, ubound;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 populationPOPSIZE.fitness)cur_best = mem;po
10、pulationPOPSIZE.fitness = populationmem.fitness;/* once the best member in the population is found, copy the genes */for (i = 0; i populationi+1.fitness) if (populationi.fitness = best)best = populationi.fitness;best_mem = i;if (populationi+1.fitness = best)best = populationi+1.fitness;best_mem = i
11、+ 1;/* if best individual from the new population is better than */* the best individual from the previous population, then */* copy the best from the new population; else replace the */* worst individual from the current population with the */* best one from the previous generation */if (best = pop
12、ulationPOPSIZE.fitness)for (i = 0; i = populationj.cfitness elsepoint = (rand() % (NVARS - 1) + 1;for (i = 0; i #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
13、. of problem variables */#define PXOVER 0.8 /* probability of crossover */#define PMUTATION 0.15 /* probability of mutation */#define TRUE 1#define FALSE 0int generation; /* current generation no. */int cur_best; /* best individual */FILE *galog; /* an output file */struct genotype /* genotype (GT),
14、 a member of the population */double geneNVARS; /* a string of variables */double fitness; /* GTs fitness */double upperNVARS; /* GTs variables upper bound */double lowerNVARS; /* GTs variables lower bound */double rfitness; /* relative fitness */double cfitness; /* cumulative fitness */;struct geno
15、type 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, double);void evaluate(void);void keep_the_best(void);vo
16、id 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 bounds. It also initializes (to zero) */* all fitness values for each m
17、ember 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. The format of the input file gadata.txt is */* var1_lower_bound var1_upper bou
18、nd */* var2_lower_bound var2_upper bound . */*/void initialize(void)FILE *infile;int i, j;double lbound, ubound;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 populationPOPSIZE.fitness)cur_best =
19、mem;populationPOPSIZE.fitness = populationmem.fitness;/* once the best member in the population is found, copy the genes */for (i = 0; i populationi+1.fitness) if (populationi.fitness = best)best = populationi.fitness;best_mem = i;if (populationi+1.fitness = best)best = populationi+1.fitness;best_me
20、m = i + 1;/* if best individual from the new population is better than */* the best individual from the previous population, then */* copy the best from the new population; else replace the */* worst individual from the current population with the */* best one from the previous generation */if (best
21、 = populationPOPSIZE.fitness)for (i = 0; i = populationj.cfitness elsepoint = (rand() % (NVARS - 1) + 1;for (i = 0; i point; i+)swap(/*/* Swap: A swap procedure that helps in swapping 2 variables */*/void swap(double *x, double *y)double temp;temp = *x;*x = *y;*y = temp;/*/* Mutation: Random uniform
22、 mutation. A variable selected for */* mutation is replaced by a random value between lower and */* upper bounds of 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 o
23、n the variable to be mutated */lbound = populationi.lowerj;hbound = populationi.upperj; populationi.genej = 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; /* be
24、st population fitness */double avg; /* avg population fitness */double stddev; /* std. deviation 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
25、 = 0; i POPSIZE; i+)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%5d, %6.3f, %6.3f, %6.3f nn“, g
26、eneration, best_val, avg, stddev);/*/* Main function: Each generation involves selecting the best */* members, performing crossover 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“
27、);initialize();evaluate();keep_the_best();while(generationMAXGENS)generation+;select();crossover();mutate();report();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(galog,“nn Best fitness = %3.3f“,populationPOPSIZE.fitness);fclose(galog);printf(“Successn“);/*/
