用c++人工智能学习 遗传算法实现图片再现源代码

遗传算法可以用于图像重建和图像生成的问题。以下是一些实现图像重建的遗传算法的源代码示例：

首先，需要将图像转换为一个二进制字符串表示。可以将每个像素点视为一个二进制数，然后将所有像素点组成的二进制序列表示整个图像。接下来，可以使用遗传算法对这个二进制序列进行优化，使得它能够重建出原始图像。

以下是一个使用遗传算法进行图像重建的示例代码：

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

#define POPSIZE 1000
#define ELITRATE 0.10
#define MUTATIONRATE 0.25
#define MAXGENERATIONS 1000

typedef struct {
    char *gene; // 二进制串
    double fitness; // 适应度
} Individual;

typedef struct {
    Individual *individuals;
    int size;
} Population;

char *target; // 目标图像的二进制串表示
int width, height; // 图像的宽度和高度

// 初始化种群
Population *initialize_population() {
    Population *pop = (Population *) malloc(sizeof(Population));
    pop->individuals = (Individual *) malloc(sizeof(Individual) * POPSIZE);
    pop->size = POPSIZE;
    for (int i = 0; i < POPSIZE; i++) {
        pop->individuals[i].gene = (char *) malloc(sizeof(char) * strlen(target));
        for (int j = 0; j < strlen(target); j++) {
            if ((double) rand() / RAND_MAX < 0.5) {
                pop->individuals[i].gene[j] = '0';
            } else {
                pop->individuals[i].gene[j] = '1';
            }
        }
    }
    return pop;
}

// 计算适应度
void calculate_fitness(Population *pop) {
    for (int i = 0; i < pop->size; i++) {
        double error = 0.0;
        for (int j = 0; j < strlen(target); j++) {
            if (pop->individuals[i].gene[j] != target[j]) {
                error++;
            }
        }
        pop->individuals[i].fitness = 1.0 / (error + 1.0);
    }
}

// 选择优秀的个体
void select(Population *pop, Population *new_pop) {
    int elitcount = (int) (ELITRATE * pop->size);
    for (int i = 0; i < elitcount; i++) {
        int elite_idx = 0;
        double elite_fitness = 0.0;
        for (int j = 0; j < pop->size; j++) {
            if (pop->individuals[j].fitness > elite_fitness) {
                elite_fitness = pop->individuals[j].fitness;
                elite_idx = j;
            }
        }
        new_pop->individuals[i] = pop->individuals[elite_idx];
        pop->individuals[elite_idx].fitness = 0.0;
    }
}

// 交叉操作
void crossover(Population *pop, Population *new_pop) {
    int elitcount = (int) (ELITRATE * pop->size);
    for (int i = elitcount; i < pop->size; i++) {
        int idx1 = rand() % pop->size;
        int idx2 = rand() % pop->size;
        int spos = rand() % strlen(target);
        new_pop->individuals[i].gene = (char *) malloc(sizeof(char) * strlen(target));
        for (int j = 0; j < strlen(target); j++) {
            if (j < spos) {
                new_pop->individuals[i].gene[j] = pop->individuals[idx1].gene[j];
            } else {
                new_pop->individuals[i].gene[j] = pop->individuals[idx2].gene[j];
            }
        }
    }
}

// 变异操作
void mutate(Population *pop) {
    for (int i = (int) (ELITRATE * pop->size); i < pop->size; i++) {
        for (int j = 0; j < strlen(target); j++) {
            if ((double) rand() / RAND_MAX < MUTATIONRATE) {
                if (pop->individuals[i].gene[j] == '0') {
                    pop->individuals[i].gene[j] = '1';
                } else {
                    pop->individuals[i].gene[j] = '0';
                }
            }
        }
    }
}

// 找到最佳个体
Individual find_best_individual(Population *pop) {
    Individual best = pop->individuals[0];
    for (int i = 1; i < pop->size; i++) {
        if (pop->individuals[i].fitness > best.fitness) {
            best = pop->individuals[i];
        }
    }
    return best;
}

int main(int argc, char *argv[]) {
    srand((unsigned) time(NULL));

    // 读取目标图像
    FILE *fp;
    fp = fopen("image.bin", "rb");
    fread(&width, sizeof(int), 1, fp);
    fread(&height, sizeof(int), 1, fp);
    target = (char *) malloc(sizeof(char) * width * height * 8);
    fread(target, sizeof(char), width * height * 8, fp);
    fclose(fp);

    // 初始化种群
    Population *pop = initialize_population();

    // 进化过程
    for (int i = 0; i < MAXGENERATIONS; i++) {
        Population *new_pop = (Population *) malloc(sizeof(Population));
        new_pop->individuals = (Individual *) malloc(sizeof(Individual) * POPSIZE);
        new_pop->size = POPSIZE;

        // 计算适应度
        calculate_fitness(pop);

        // 选择优秀的个体
        select(pop, new_pop);

        // 交叉操作
        crossover(pop, new_pop);

        // 变异操作
        mutate(new_pop);

        // 更新种群
        free(pop->individuals);
        free(pop);
        pop = new_pop;

        // 输出最佳个体
        Individual best = find_best_individual(pop);
        printf("Generation %d: fitness = %f\n", i, best.fitness);
    }

    // 输出最终结果
    Individual best = find_best_individual(pop);
    printf("Best individual: fitness = %f\n", best.fitness);
    for (int i = 0; i < strlen(target); i++) {
        printf("%c", best.gene[i]);
    }
    printf("\n");

    return 0;
}

以上代码仅作为参考，实际上需要根据具体的问题进行修改和优化。