用C语言代码实现遗传算法

以下是一个简单的用C语言实现遗传算法的示例代码：

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

#define POPULATION_SIZE 10
#define CHROMOSOME_LENGTH 5
#define MAX_GENERATIONS 100
#define MUTATION_RATE 0.1

typedef struct {
    int genes[CHROMOSOME_LENGTH];
    int fitness;
} Individual;

int calculateFitness(Individual individual) {
    int fitness = 0;
    // 计算适应度函数，根据具体问题进行定义
    // 这里简单地求解二进制数的值作为适应度
    for (int i = 0; i < CHROMOSOME_LENGTH; i++) {
        fitness += individual.genes[i] << (CHROMOSOME_LENGTH - 1 - i);
    }
    return fitness;
}

void initializePopulation(Individual population[]) {
    // 随机初始化种群
    for (int i = 0; i < POPULATION_SIZE; i++) {
        for (int j = 0; j < CHROMOSOME_LENGTH; j++) {
            population[i].genes[j] = rand() % 2;
        }
        population[i].fitness = calculateFitness(population[i]);
    }
}

Individual selectParent(Individual population[]) {
    // 轮盘赌选择父代
    int totalFitness = 0;
    for (int i = 0; i < POPULATION_SIZE; i++) {
        totalFitness += population[i].fitness;
    }
    int randomFitness = rand() % totalFitness;
    
    int sumFitness = 0;
    for (int i = 0; i < POPULATION_SIZE; i++) {
        sumFitness += population[i].fitness;
        if (sumFitness >= randomFitness) {
            return population[i];
        }
    }
    return population[POPULATION_SIZE - 1];
}

Individual crossover(Individual parent1, Individual parent2) {
    // 单点交叉生成子代
    int crossoverPoint = rand() % CHROMOSOME_LENGTH;
    
    Individual child;
    for (int i = 0; i < CHROMOSOME_LENGTH; i++) {
        if (i < crossoverPoint) {
            child.genes[i] = parent1.genes[i];
        } else {
            child.genes[i] = parent2.genes[i];
        }
    }
    child.fitness = calculateFitness(child);
    
    return child;
}

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

Individual findBestIndividual(Individual population[]) {
    // 找到最优个体
    int bestFitness = population[0].fitness;
    int bestIndex = 0;
    for (int i = 1; i < POPULATION_SIZE; i++) {
        if (population[i].fitness > bestFitness) {
            bestFitness = population[i].fitness;
            bestIndex = i;
        }
    }
    return population[bestIndex];
}

void printIndividual(Individual individual) {
    // 打印个体的基因和适应度
    for (int i = 0; i < CHROMOSOME_LENGTH; i++) {
        printf("%d ", individual.genes[i]);
    }
    printf("-> %d\n", individual.fitness);
}

int main() {
    srand(time(NULL));
    
    Individual population[POPULATION_SIZE];
    
    initializePopulation(population);
    
    for (int generation = 0; generation < MAX_GENERATIONS; generation++) {
        Individual newPopulation[POPULATION_SIZE];
        for (int i = 0; i < POPULATION_SIZE; i++) {
            Individual parent1 = selectParent(population);
            Individual parent2 = selectParent(population);
            
            Individual child = crossover(parent1, parent2);
            
            mutate(&child);
            
            newPopulation[i] = child;
        }
        
        for (int i = 0; i < POPULATION_SIZE; i++) {
            population[i] = newPopulation[i];
        }
        
        printf("Generation %d: ", generation + 1);
        printIndividual(findBestIndividual(population));
    }
    
    return 0;
}

以上代码是一个简单的遗传算法的实现示例。你可以根据具体问题的需求进行修改和扩展。