再写一个遗传算法简单c语言代码

遗传算法是一种模拟自然选择过程的优化搜索算法，通常用于解决复杂问题的全局最优解。以下是一个简单的C语言版遗传算法示例，用于求解最小化函数`f(x) = x^2`的问题：

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

// 模拟个体结构
typedef struct {
    double chromosome[50]; // 假设我们有50维的解空间
} Individual;

// 函数适应度评估
double fitness(Individual ind) {
    return ind.chromosome[0] * ind.chromosome[0];
}

// 交叉操作
Individual crossover(Individual parent1, Individual parent2) {
    int cross_point = rand() % (sizeof(individual.chromosome) - 1);
    Individual child;
    for (int i = 0; i < sizeof(child.chromosome); ++i) {
        if (i <= cross_point)
            child.chromosome[i] = parent1.chromosome[i];
        else
            child.chromosome[i] = parent2.chromosome[i];
    }
    return child;
}

// 变异操作
void mutate(Individual &ind) {
    for (int i = 0; i < sizeof(ind.chromosome); ++i) {
        ind.chromosome[i] += ((double)rand() / RAND_MAX) * 2 - 1; // 随机变异
    }
}

// 主遗传算法循环
void genetic_algorithm(int generations, Individual population[], int size) {
    srand(time(NULL)); // 设置随机种子

    // 初始化种群
    for (int i = 0; i < size; ++i) {
        for (int j = 0; j < sizeof(population[i].chromosome); ++j) {
            population[i].chromosome[j] = rand() / RAND_MAX; // 随机生成初始值
        }
    }

    for (int gen = 0; gen < generations; ++gen) {
        // 选择
        double* fitness_values = new double[size];
        for (int i = 0; i < size; ++i) {
            fitness_values[i] = fitness(population[i]);
        }
        sort(fitness_values, fitness_values + size);
        Individual elite = population[fitness_values[0]];

        // 交叉和变异
        for (int i = 1; i < size; ++i) {
            Individual parent1 = population[rand() % size];
            Individual parent2 = population[rand() % size];
            Individual child = crossover(parent1, parent2);
            mutate(child);
            population[i] = child;
        }

        // 更新精英
        population[0] = elite;

        printf("Generation %d: Best fitness = %.2lf\n", gen+1, fitness(elite));
    }
}

int main() {
    const int population_size = 50;
    const int generations = 100;
    Individual* population = (Individual*)malloc(sizeof(Individual) * population_size);

    genetic_algorithm(generations, population, population_size);

    free(population);
    return 0;
}

这个程序创建了一个种群，通过交叉、变异等步骤进行迭代，寻找适应度函数`x^2`的最小值。注意这只是一个简化的例子，实际应用中可能需要更复杂的编码和问题特定的适应度函数。