遗传算法C语言程序实例

以下是一个简单的遗传算法的C语言程序实例：

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

#define POP_SIZE 50 // 种群大小
#define NUM_VARS 1 // 变量个数
#define NUM_GENERATIONS 100 // 迭代次数
#define CROSSOVER_PROB 0.8 // 交叉概率
#define MUTATION_PROB 0.1 // 变异概率

// 目标函数
double func(double x) {
    return 2 * sin(x) + cos(3 * x);
}

// 个体结构体
typedef struct {
    double vars[NUM_VARS]; // 变量数组
    double fitness; // 适应度
} Individual;

// 初始化种群
void initializePopulation(Individual population[]) {
    for (int i = 0; i < POP_SIZE; i++) {
        for (int j = 0; j < NUM_VARS; j++) {
            population[i].vars[j] = (double)rand() / RAND_MAX * 10 - 5;
        }
    }
}

// 计算适应度
void calculateFitness(Individual population[]) {
    for (int i = 0; i < POP_SIZE; i++) {
        population[i].fitness = func(population[i].vars[0]);
    }
}

// 选择
void selection(Individual population[]) {
    // 排序种群
    for (int i = 0; i < POP_SIZE - 1; i++) {
        for (int j = 0; j < POP_SIZE - i - 1; j++) {
            if (population[j].fitness > population[j+1].fitness) {
                Individual temp = population[j];
                population[j] = population[j+1];
                population[j+1] = temp;
            }
        }
    }
    // 选择前一半个体作为父代
    for (int i = 0; i < POP_SIZE / 2; i++) {
        population[i + POP_SIZE / 2] = population[i];
    }
}

// 交叉
void crossover(Individual parent1, Individual parent2, Individual* offspring1, Individual* offspring2) {
    int point = rand() % NUM_VARS;
    for (int i = 0; i < NUM_VARS; i++) {
        if (i <= point) {
            offspring1->vars[i] = parent1.vars[i];
            offspring2->vars[i] = parent2.vars[i];
        } else {
            offspring1->vars[i] = parent2.vars[i];
            offspring2->vars[i] = parent1.vars[i];
        }
    }
}

// 变异
void mutation(Individual* individual) {
    int point = rand() % NUM_VARS;
    individual->vars[point] += ((double)rand() / RAND_MAX) * 0.1;
}

// 更新种群
void updatePopulation(Individual population[]) {
    for (int i = POP_SIZE / 2; i < POP_SIZE; i++) {
        if ((double)rand() / RAND_MAX < CROSSOVER_PROB) {
            int parent1Index = rand() % (POP_SIZE / 2);
            int parent2Index = rand() % (POP_SIZE / 2);
            crossover(population[parent1Index], population[parent2Index], &population[i], &population[i+1]);
            i++;
        } else {
            int parentIndex = rand() % (POP_SIZE / 2);
            population[i] = population[parentIndex];
            mutation(&population[i]);
        }
    }
}

// 打印最优解
void printBestSolution(Individual population[]) {
    double bestFitness = population[0].fitness;
    double bestSolution = population[0].vars[0];
    for (int i = 1; i < POP_SIZE; i++) {
        if (population[i].fitness < bestFitness) {
            bestFitness = population[i].fitness;
            bestSolution = population[i].vars[0];
        }
    }
    printf("最小值为 %f，对应的解为 %f\n", bestFitness, bestSolution);
}

int main() {
    Individual population[POP_SIZE];

    // 初始化种群
    initializePopulation(population);

    // 遗传算法迭代
    for (int i = 0; i < NUM_GENERATIONS; i++) {
        // 计算适应度
        calculateFitness(population);

        // 选择
        selection(population);

        // 更新种群
        updatePopulation(population);
    }

    // 输出结果
    printBestSolution(population);

    return 0;
}

请注意，这只是一个简单的示例程序，具体的遗传算法实现可能会根据实际情况有所调整。