archival commit

1 files changed, 113 insertions, 0 deletions

diff --git a/example/genetic_char.c b/example/genetic_char.c
new file mode 100755
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--- /dev/null
+++ b/example/genetic_char.c
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+// example_string.c - Example training of ann.h ANN, with genetic algorithm
+
+#include <stdint.h>
+#include <stdio.h>
+#include <string.h>
+#include <time.h>
+
+typedef char *ga_individual_t;
+
+#define GENETIC_IMPLEMENTATION
+#include "../include/genetic.h"
+
+#define POPULATION_N 100
+#define RATE_SELECTION 0.5
+#define RATE_MUTATON 2.0
+#define ERROR 0
+
+#define TARGET_STRING                                                          \
+  " !\"#$%&'()*+,-./"                                                          \
+  "0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`"                         \
+  "abcdefghijklmnopqrstuvwxyz{|}~"
+
+#define generate() ((char)(rand() % (127 - 32) + 32))
+
+void crossover(char **y, char *x0, char *x1) {
+  uint32_t lx0 = strlen(x0);
+  uint32_t lx1 = strlen(x1);
+
+  uint32_t ix0 = rand() % lx0;
+  uint32_t ix1 = rand() % lx1;
+
+  uint32_t ly = ix0 + (lx1 - ix1);
+
+  char *sy = malloc(ly + 1);
+  strncpy(sy, x0, ix0);
+  strncpy(sy + ix0, x1 + ix1, lx1 - ix1);
+  sy[ly] = 0;
+
+  free(*y);
+  *y = sy;
+}
+
+void mutation(char *x) {
+  uint32_t l = strlen(x);
+  uint32_t i = rand() % l;
+
+  x[i] = generate();
+}
+
+double fitness(char *individual, void const *data) {
+  double error = 0;
+
+  char const *target = (char const *)data;
+
+  while (*individual && *target) {
+    error += abs(*target++ - *individual++);
+  }
+  while (*individual) {
+    error += *individual++;
+  }
+  while (*target) {
+    error += *target++;
+  }
+
+  return error;
+}
+
+int main(void) {
+  char const *target = TARGET_STRING;
+  char *population[POPULATION_N];
+
+  srand(time(0));
+
+  // Initialize poplation
+  for (uint64_t i = 0; i < POPULATION_N; i++) {
+    uint64_t n = rand() % 5 + 20;
+    char *str = malloc(n + 1);
+    str[n] = (uint8_t)0;
+
+    for (uint64_t j = 0; j < n; j++) {
+      str[j] = generate();
+    }
+
+    population[i] = str;
+  }
+
+  printf("TARGET:  %s\n", TARGET_STRING);
+  fflush(stdout);
+
+  time_t t0 = clock();
+
+  uint32_t n = 0;
+
+  // Conduct opitmization
+  double error = 1;
+  while (error > ERROR) {
+    error =
+        ga_generation(population, POPULATION_N, RATE_SELECTION, RATE_MUTATON,
+                      fitness, (void const *)target, crossover, mutation);
+
+    n++;
+  }
+
+  time_t t1 = clock();
+
+  printf("\nSOLUTION: %s", population[0]);
+  printf("\nITERATIONS: %d\nTIME:       %f ms\n", n,
+         1000.0 * (t1 - t0) / CLOCKS_PER_SEC);
+
+  for (int i = 0; i < POPULATION_N; i++) {
+    free(population[i]);
+  }
+}