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// Tests for genetic.h - genetic algorithm generation step
typedef char *ga_individual_t;
#define GENETIC_IMPLEMENTATION
#include "../include/genetic.h"
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <time.h>
#include <float.h>
#define ASSERT(cond, msg) \
do { \
if (!(cond)) { \
fprintf(stderr, "FAIL: %s (test_genetic.c:%d)\n", msg, __LINE__); \
failures++; \
} \
} while (0)
static int failures = 0;
#define TARGET "HELLO"
#define POP_N 20
static void crossover_fn(ga_individual_t *child, ga_individual_t p1, ga_individual_t p2) {
if (!child || !p1 || !p2)
return;
uint32_t l1 = strlen(p1);
uint32_t l2 = strlen(p2);
if (l1 == 0 || l2 == 0)
return;
uint32_t c1 = rand() % l1;
uint32_t c2 = rand() % l2;
uint32_t len = c1 + (l2 - c2);
char *s = malloc(len + 1);
if (!s) {
free(*child);
*child = NULL;
return;
}
memcpy(s, p1, c1);
memcpy(s + c1, p2 + c2, l2 - c2);
s[len] = '\0';
free(*child);
*child = s;
}
static void mutation_fn(ga_individual_t *ind) {
if (!ind || !*ind)
return;
uint32_t l = strlen(*ind);
if (l == 0)
return;
(*ind)[rand() % l] = (char)(rand() % 26 + 'A');
}
static double fitness_fn(ga_individual_t ind, void const *data) {
if (!ind)
return DBL_MAX;
char const *target = (char const *)data;
double error = 0;
while (*ind && *target) {
error += abs((int)(*target++ - *ind++));
}
while (*ind) error += abs((int)*ind++);
while (*target) error += abs((int)*target++);
return error;
}
static void free_population(char *population[], int n) {
for (int i = 0; i < n; i++)
free(population[i]);
}
void test_generation() {
srand(42);
char *population[POP_N];
for (int i = 0; i < POP_N; i++) {
uint32_t len = 4 + rand() % 3;
char *s = malloc(len + 1);
if (!s) { free_population(population, i); exit(1); }
for (uint32_t j = 0; j < len; j++) {
s[j] = (char)(rand() % 26 + 'A');
}
s[len] = '\0';
population[i] = s;
}
double error = ga_generation(population, POP_N, 0.5, 1.0, fitness_fn, TARGET,
crossover_fn, mutation_fn);
ASSERT(error >= 0, "error non-negative");
for (int i = POP_N - 1; i >= 0; i--) {
free(population[i]);
}
}
void test_convergence() {
srand(42);
char *population[POP_N];
for (int i = 0; i < POP_N; i++) {
uint32_t len = strlen(TARGET);
char *s = malloc(len + 1);
if (!s) { free_population(population, i); exit(1); }
for (uint32_t j = 0; j < len; j++) {
s[j] = (char)(rand() % 26 + 'A');
}
s[len] = '\0';
population[i] = s;
}
double error = 1;
int generations = 0;
while (error > 0 && generations < 500) {
error = ga_generation(population, POP_N, 0.5, 1.0, fitness_fn, TARGET,
crossover_fn, mutation_fn);
generations++;
}
ASSERT(generations < 500, "converges within 500 generations");
for (int i = 0; i < POP_N; i++) {
free(population[i]);
}
}
void test_selection_rate() {
srand(42);
char *population[POP_N];
for (int i = 0; i < POP_N; i++) {
char *s = malloc(6);
if (!s) { free_population(population, i); exit(1); }
strcpy(s, "HELLO");
population[i] = s;
}
double error = ga_generation(population, POP_N, 0.5, 0.1, fitness_fn, TARGET,
crossover_fn, mutation_fn);
ASSERT(error >= 0, "low mutation rate returns non-negative error");
ASSERT(error == 0, "all-population-matching-target has zero error");
for (int i = 0; i < POP_N; i++) {
free(population[i]);
}
}
int main() {
test_generation();
test_convergence();
test_selection_rate();
printf("test_genetic: %d failures\n", failures);
return failures;
}
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