prog.txt

这是一个非常简单的遗传算法源代码

#include <stdio.h> 
#include <stdlib.h> 
#include <math.h> 

/* 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 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 gene[NVARS]; /* a string of variables */ 
double fitness; /* GT's fitness */ 
double upper[NVARS]; /* GT's variables upper bound */ 
double lower[NVARS]; /* GT's variables lower bound */ 
double rfitness; /* relative fitness */ 
double cfitness; /* cumulative fitness */ 
}; 

struct genotype population[POPSIZE+1]; /* population */ 
struct genotype newpopulation[POPSIZE+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 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 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. 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, 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 < NVARS; i++) 
{ 
fscanf(infile, "%lf",&lbound); 
fscanf(infile, "%lf",&ubound); 

for (j = 0; j < POPSIZE; j++) 
{ 
population[j].fitness = 0; 
population[j].rfitness = 0; 
population[j].cfitness = 0; 
population[j].lower[i] = lbound; 
population[j].upper[i]= ubound; 
population[j].gene[i] = randval(population[j].lower[i], 
population[j].upper[i]); 
} 
} 

fclose(infile); 
} 

/***********************************************************/ 
/* Random value generator: 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. */ 
/* The current function is: x[1]^2-x[1]*x[2]+x[3] */ 
/*************************************************************/ 

void evaluate(void) 
{ 
int mem; 
int i; 
double x[NVARS+1]; 

for (mem = 0; mem < POPSIZE; mem++) 
{ 
for (i = 0; i < NVARS; i++) 
x[i+1] = population[mem].gene[i]; 

population[mem].fitness = (x[1]*x[1]) - (x[1]*x[2]) + x[3]; 
} 
} 

/***************************************************************/ 
/* 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; /* stores the index of the best individual */ 

for (mem = 0; mem < POPSIZE; mem++) 
{ 
if (population[mem].fitness > population[POPSIZE].fitness) 
{ 
cur_best = mem; 
population[POPSIZE].fitness = population[mem].fitness; 
} 
} 
/* once the best member in the population is found, copy the genes */ 
for (i = 0; i < NVARS; i++) 
population[POPSIZE].gene[i] = population[cur_best].gene[i]; 
} 

/****************************************************************/ 
/* Elitist function: The best member of the previous generation */ 
/* is stored as the last in the array. If the best member of */ 
/* the current generation is worse then the best member of 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 = population[0].fitness; 
worst = population[0].fitness; 
for (i = 0; i < POPSIZE - 1; ++i) 
{ 
if(population[i].fitness > population[i+1].fitness) 
{ 
if (population[i].fitness >= best) 
{ 
best = population[i].fitness; 
best_mem = i; 
} 
if (population[i+1].fitness <= worst) 
{ 
worst = population[i+1].fitness; 
worst_mem = i + 1; 
} 
} 
else 
{ 
if (population[i].fitness <= worst) 
{ 
worst = population[i].fitness; 
worst_mem = i; 
} 
if (population[i+1].fitness >= best) 
{ 
best = population[i+1].fitness; 
best_mem = 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 >= population[POPSIZE].fitness) 
{ 
for (i = 0; i < NVARS; i++) 
population[POPSIZE].gene[i] = population[best_mem].gene[i]; 
population[POPSIZE].fitness = population[best_mem].fitness; 
} 
else 
{ 
for (i = 0; i < NVARS; i++) 
population[worst_mem].gene[i] = population[POPSIZE].gene[i]; 
population[worst_mem].fitness = population[POPSIZE].fitness; 
} 
} 
/**************************************************************/ 
/* Selection function: Standard proportional selection for */ 
/* maximization problems incorporating elitist model - makes */ 
/* sure that the best member survives */ 
/**************************************************************/ 

void select(void) 
{ 
int mem, i, j, k; 
double sum = 0; 
double p; 

/* find total fitness of the population */ 
for (mem = 0; mem < POPSIZE; mem++) 
{ 
sum += population[mem].fitness; 
} 

/* calculate relative fitness */ 
for (mem = 0; mem < POPSIZE; mem++) 
{ 
population[mem].rfitness = population[mem].fitness/sum; 
} 
population[0].cfitness = population[0].rfitness; 

/* calculate cumulative fitness */ 
for (mem = 1; mem < POPSIZE; mem++) 
{ 
population[mem].cfitness = population[mem-1].cfitness + 
population[mem].rfitness; 
} 

/* finally select survivors using cumulative fitness. */ 

for (i = 0; i < POPSIZE; i++) 
{ 
p = rand()%1000/1000.0; 
if (p < population[0].cfitness) 
newpopulation[i] = population[0]; 
else 
{ 
for (j = 0; j < POPSIZE;j++) 
if (p >= population[j].cfitness && 
p<population[j+1].cfitness) 
newpopulation[i] = population[j+1]; 
} 
} 
/* once a new population is created, copy it back */ 

for (i = 0; i < POPSIZE; i++) 
population[i] = newpopulation[i]; 
} 

/***************************************************************/ 
/* 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; mem < POPSIZE; ++mem) 
{ 
x = rand()%1000/1000.0; 
if (x < PXOVER) 
{ 
++first; 
if (first % 2 == 0) 
Xover(one, mem); 
else 
one = mem; 
} 
} 
} 
/**************************************************************/ 
/* Crossover: performs crossover of the two selected parents. */ 
/**************************************************************/ 

void Xover(int one, int two) 
{ 
int i; 
int point; /* crossover point */ 

/* select crossover point */ 
if(NVARS > 1) 
{ 
if(NVARS == 2) 
point = 1; 
else 
point = (rand() % (NVARS - 1)) + 1; 

for (i = 0; i < point; i++) 
swap(&population[one].gene[i], &population[two].gene[i]); 

} 
} 

/*************************************************************/ 
/* 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 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 on the variable to be mutated */ 
lbound = population[i].lower[j]; 
hbound = population[i].upper[j]; 
population[i].gene[j] = 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. 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 = 0; i < POPSIZE; i++) 
{ 
sum += population[i].fitness; 
sum_square += population[i].fitness * population[i].fitness; 
} 

avg = sum/(double)POPSIZE; 
square_sum = avg * avg * POPSIZE; 
stddev = sqrt((sum_square - square_sum)/(POPSIZE - 1)); 
best_val = population[POPSIZE].fitness; 

fprintf(galog, "\n%5d, %6.3f, %6.3f, %6.3f \n\n", 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(void) 
{ 
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(generation<MAXGENS) 
{ 
generation++; 
select(); 
crossover(); 
mutate(); 
report(); 
evaluate(); 
elitist(); 
} 
fprintf(galog,"\n\n Simulation completed\n"); 
fprintf(galog,"\n Best member: \n"); 

for (i = 0; i < NVARS; i++) 
{ 
fprintf (galog,"\n var(%d) = %3.3f",i,population[POPSIZE].gene[i]); 
} 
fprintf(galog,"\n\n Best fitness = %3.3f",population[POPSIZE].fitness); 
fclose(galog); 
printf("Success\n"); 
} 
/***************************************************************/