生成一个MPI并行化遗传算法的C++程序代码

我可以回答这个问题。以下是一个MPI并行化遗传算法的C程序代码的示例：

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

#define POP_SIZE 100
#define GENOME_LEN 20
#define MUTATION_RATE .01
#define ELITE_SIZE 10
#define NUM_GENERATIONS 100

typedef struct {
int fitness;
char genome[GENOME_LEN];
} Individual;

void generate_population(Individual *population, int size) {
int i, j;
for (i = ; i < size; i++) {
for (j = ; j < GENOME_LEN; j++) {
population[i].genome[j] = rand() % 2;
}
}
}

int evaluate_fitness(char *genome) {
int i, fitness = ;
for (i = ; i < GENOME_LEN; i++) {
if (genome[i] == 1) {
fitness++;
}
}
return fitness;
}

void evaluate_population(Individual *population, int size) {
int i;
for (i = ; i < size; i++) {
population[i].fitness = evaluate_fitness(population[i].genome);
}
}

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

void elitism(Individual *population, Individual *elite, int elite_size) {
int i;
for (i = ; i < elite_size; i++) {
elite[i] = population[i];
}
}

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

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

void reproduce(Individual *parent1, Individual *parent2, Individual *child) {
crossover(parent1, parent2, child);
mutate(child);
}

void mate(Individual *population, Individual *offspring, int size) {
int i, parent1_index, parent2_index;
for (i = ; i < size; i++) {
parent1_index = rand() % ELITE_SIZE;
parent2_index = rand() % POP_SIZE;
reproduce(&population[parent1_index], &population[parent2_index], &offspring[i]);
}
}

void replace_population(Individual *population, Individual *elite, Individual *offspring, int size) {
int i;
for (i = ; i < ELITE_SIZE; i++) {
population[i] = elite[i];
}
for (i = ELITE_SIZE; i < POP_SIZE; i++) {
population[i] = offspring[i - ELITE_SIZE];
}
}

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

int main(int argc, char **argv) {
int i, rank, size;
Individual population[POP_SIZE], elite[ELITE_SIZE], offspring[POP_SIZE - ELITE_SIZE];
MPI_Init(&argc, &argv);
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
MPI_Comm_size(MPI_COMM_WORLD, &size);
srand(rank);
generate_population(population, POP_SIZE);
for (i = ; i < NUM_GENERATIONS; i++) {
evaluate_population(population, POP_SIZE);
sort_population(population, POP_SIZE);
elitism(population, elite, ELITE_SIZE);
mate(population, offspring, POP_SIZE - ELITE_SIZE);
replace_population(population, elite, offspring, POP_SIZE);
if (rank == ) {
printf("Generation %d:\n", i + 1);
print_population(population, POP_SIZE);
}
}
MPI_Finalize();
return ;
}