写一个遗传算法的代码，用c语言

以下是一个简单的遗传算法的代码示例，使用 C 语言实现：

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

#define POPULATION_SIZE 10
#define GENOME_SIZE 5
#define MUTATION_RATE 0.1
#define ELITISM_COUNT 1

typedef struct {
    int fitness;
    int genome[GENOME_SIZE];
} Individual;

void initialize_population(Individual *population) {
    int i, j;
    for (i = 0; i < POPULATION_SIZE; i++) {
        population[i].fitness = 0;
        for (j = 0; j < GENOME_SIZE; j++) {
            population[i].genome[j] = rand() % 2; // randomly initialize genome
        }
    }
}

void calculate_fitness(Individual *individual) {
    int i;
    individual->fitness = 0;
    for (i = 0; i < GENOME_SIZE; i++) {
        individual->fitness += individual->genome[i];
    }
}

void evaluate_population(Individual *population) {
    int i;
    for (i = 0; i < POPULATION_SIZE; i++) {
        calculate_fitness(&population[i]);
    }
}

void crossover(Individual *parent1, Individual *parent2, Individual *child) {
    int i, crossover_point;
    crossover_point = rand() % GENOME_SIZE;
    for (i = 0; i < crossover_point; i++) {
        child->genome[i] = parent1->genome[i];
    }
    for (i = crossover_point; i < GENOME_SIZE; i++) {
        child->genome[i] = parent2->genome[i];
    }
}

void mutate(Individual *individual) {
    int i;
    for (i = 0; i < GENOME_SIZE; i++) {
        if ((double) rand() / RAND_MAX < MUTATION_RATE) {
            individual->genome[i] = !individual->genome[i];
        }
    }
}

void sort_population_by_fitness(Individual *population) {
    int i, j;
    Individual temp;
    for (i = 0; i < POPULATION_SIZE - 1; i++) {
        for (j = i + 1; j < POPULATION_SIZE; j++) {
            if (population[i].fitness < population[j].fitness) {
                temp = population[i];
                population[i] = population[j];
                population[j] = temp;
            }
        }
    }
}

void elitism(Individual *population, Individual *next_generation) {
    int i;
    for (i = 0; i < ELITISM_COUNT; i++) {
        next_generation[i] = population[i]; // copy the best individuals from the current population to the next generation
    }
}

void selection(Individual *population, Individual *parents) {
    int i, j, fitness_sum = 0;
    for (i = 0; i < POPULATION_SIZE; i++) {
        fitness_sum += population[i].fitness;
    }
    for (i = 0; i < 2; i++) {
        int random_value = rand() % fitness_sum;
        for (j = 0; j < POPULATION_SIZE; j++) {
            random_value -= population[j].fitness;
            if (random_value <= 0) {
                parents[i] = population[j];
                break;
            }
        }
    }
}

void evolve_population(Individual *current_generation, Individual *next_generation) {
    int i;
    Individual parents[2];
    elitism(current_generation, next_generation);
    for (i = ELITISM_COUNT; i < POPULATION_SIZE; i++) {
        selection(current_generation, parents);
        crossover(&parents[0], &parents[1], &next_generation[i]);
        mutate(&next_generation[i]);
    }
}

void print_population(Individual *population) {
    int i, j;
    for (i = 0; i < POPULATION_SIZE; i++) {
        printf("Individual %d: fitness=%d, genome=[", i, population[i].fitness);
        for (j = 0; j < GENOME_SIZE; j++) {
            printf("%d", population[i].genome[j]);
        }
        printf("]\n");
    }
}

int main() {
    srand(time(NULL));
    Individual current_generation[POPULATION_SIZE];
    Individual next_generation[POPULATION_SIZE];
    initialize_population(current_generation);
    evaluate_population(current_generation);
    sort_population_by_fitness(current_generation);
    print_population(current_generation);
    evolve_population(current_generation, next_generation);
    evaluate_population(next_generation);
    sort_population_by_fitness(next_generation);
    print_population(next_generation);
    return 0;
}

这个代码实现了一个简单的遗传算法，用于解决二进制字符串中 1 的个数最大化问题。其中，每个个体都是一个长度为 5 的二进制字符串，遗传算法的过程包括初始化种群、计算适应度、选择、交叉、变异、排序和精英保留，模拟了自然选择和遗传的过程。