用遗传算法实现大学排课 c语言代码

以下是一份参考代码实现，但请注意，这只是一个简单的示例，实际应用中可能需要更复杂的实现和优化，因此仅供参考。

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

#define MAX_COURSES 100
#define MAX_TIMESLOTS 50
#define MAX_ROOMS 30
#define MAX_TEACHERS 20
#define MAX_STUDENTS 200

typedef struct {
    int course_id;
    int timeslot_id;
    int teacher_id;
    int room_id;
} Gene;

typedef struct {
    Gene genes[MAX_COURSES];
    int fitness;
} Chromosome;

typedef struct {
    Chromosome chromosomes[MAX_STUDENTS];
    int size;
} Population;

int course_conflicts[MAX_COURSES][MAX_COURSES];
int teacher_conflicts[MAX_TEACHERS][MAX_TEACHERS];
int student_conflicts[MAX_STUDENTS][MAX_STUDENTS];

int course_capacity[MAX_COURSES];
int room_capacity[MAX_ROOMS];

int course_teacher[MAX_COURSES];
int teacher_courses[MAX_TEACHERS][MAX_COURSES];

int student_courses[MAX_STUDENTS][MAX_COURSES];
int course_students[MAX_COURSES][MAX_STUDENTS];

int num_courses, num_timeslots, num_rooms, num_teachers, num_students;

int get_random(int min, int max) {
    return min + rand() % (max - min + 1);
}

void randomize_population(Population *population) {
    int i, j;
    for (i = 0; i < population->size; i++) {
        Chromosome *chromosome = &population->chromosomes[i];
        for (j = 0; j < num_courses; j++) {
            chromosome->genes[j].course_id = j;
            chromosome->genes[j].timeslot_id = get_random(0, num_timeslots - 1);
            chromosome->genes[j].teacher_id = course_teacher[j];
            chromosome->genes[j].room_id = get_random(0, num_rooms - 1);
        }
    }
}

int calculate_fitness(Chromosome *chromosome) {
    int i, j, conflicts = 0;
    for (i = 0; i < num_courses; i++) {
        Gene *gene1 = &chromosome->genes[i];
        for (j = i + 1; j < num_courses; j++) {
            Gene *gene2 = &chromosome->genes[j];
            if (gene1->timeslot_id == gene2->timeslot_id &&
                (gene1->room_id == gene2->room_id ||
                 course_conflicts[gene1->course_id][gene2->course_id] ||
                 teacher_conflicts[gene1->teacher_id][gene2->teacher_id] ||
                 student_conflicts[course_students[gene1->course_id][0]][course_students[gene2->course_id][0]])) {
                conflicts++;
            }
        }
        if (course_capacity[gene1->course_id] > room_capacity[gene1->room_id]) {
            conflicts++;
        }
    }
    return num_courses * num_courses - conflicts;
}

void evaluate_population(Population *population) {
    int i;
    for (i = 0; i < population->size; i++) {
        Chromosome *chromosome = &population->chromosomes[i];
        chromosome->fitness = calculate_fitness(chromosome);
    }
}

void sort_population(Population *population) {
    int i, j;
    for (i = 0; i < population->size - 1; i++) {
        for (j = 0; j < population->size - i - 1; j++) {
            if (population->chromosomes[j].fitness < population->chromosomes[j + 1].fitness) {
                Chromosome temp = population->chromosomes[j];
                population->chromosomes[j] = population->chromosomes[j + 1];
                population->chromosomes[j + 1] = temp;
            }
        }
    }
}

int select_parent(int total_fitness, Population *population) {
    int i, sum = 0;
    for (i = 0; i < population->size; i++) {
        sum += population->chromosomes[i].fitness;
        if (sum >= total_fitness) {
            return i;
        }
    }
    return population->size - 1;
}

void crossover(Chromosome *parent1, Chromosome *parent2, Chromosome *child1, Chromosome *child2) {
    int i, crossover_point = get_random(0, num_courses - 1);
    for (i = 0; i < crossover_point; i++) {
        child1->genes[i] = parent1->genes[i];
        child2->genes[i] = parent2->genes[i];
    }
    for (i = crossover_point; i < num_courses; i++) {
        child1->genes[i] = parent2->genes[i];
        child2->genes[i] = parent1->genes[i];
    }
}

void mutate(Chromosome *chromosome) {
    int i, j;
    for (i = 0; i < num_courses; i++) {
        if (get_random(0, 100) < 5) {
            chromosome->genes[i].timeslot_id = get_random(0, num_timeslots - 1);
        }
        if (get_random(0, 100) < 5) {
            chromosome->genes[i].room_id = get_random(0, num_rooms - 1);
        }
    }
}

void generate_children(Population *population) {
    int i, total_fitness = 0;
    for (i = 0; i < population->size; i++) {
        total_fitness += population->chromosomes[i].fitness;
    }
    for (i = 0; i < population->size; i += 2) {
        int parent1_index = select_parent(total_fitness, population);
        int parent2_index = select_parent(total_fitness, population);
        Chromosome *parent1 = &population->chromosomes[parent1_index];
        Chromosome *parent2 = &population->chromosomes[parent2_index];
        Chromosome *child1 = &population->chromosomes[i];
        Chromosome *child2 = &population->chromosomes[i + 1];
        crossover(parent1, parent2, child1, child2);
        mutate(child1);
        mutate(child2);
    }
}

void print_schedule(Chromosome *chromosome) {
    int i, j;
    printf("Course\tTime\tTeacher\tRoom\n");
    for (i = 0; i < num_courses; i++) {
        Gene *gene = &chromosome->genes[i];
        printf("%d\t%d\t%d\t%d\n", gene->course_id, gene->timeslot_id, gene->teacher_id, gene->room_id);
    }
}

int main() {
    srand(time(NULL));
    num_courses = 10;
    num_timeslots = 5;
    num_rooms = 3;
    num_teachers = 4;
    num_students = 20;
    randomize_population(&population);
    evaluate_population(&population);
    sort_population(&population);
    print_schedule(&population.chromosomes[0]);
    return 0;
}

在实际应用中，需要根据具体需求进行调整和优化，并且需要根据实际数据来初始化相关参数和数据结构。