Python编写几个用遗传算法解决图着色问题的代码

以下是使用Python编写的几个用遗传算法解决图着色问题的代码示例：

1. 遗传算法求解图着色问题：

import random

def genetic_algorithm(graph, colors, pop_size, elite_size, mutation_rate, generations):
    pop = create_population(pop_size, graph)
    for i in range(generations):
        pop_fitness = rank_population(pop, graph)
        elite = get_elite(pop, elite_size, pop_fitness)
        next_gen = breed_population(elite, pop_size, mutation_rate)
        pop = next_gen
    best = pop_fitness[0]
    return best

def create_population(pop_size, graph):
    pop = []
    for i in range(pop_size):
        chromo = []
        for j in range(len(graph)):
            chromo.append(random.choice(range(len(colors))))
        pop.append(chromo)
    return pop

def rank_population(pop, graph):
    fitness = []
    for chromo in pop:
        score = 0
        for i in range(len(graph)):
            for j in range(len(graph)):
                if graph[i][j] and chromo[i] == chromo[j]:
                    score -= 1
        fitness.append((score, chromo))
    fitness.sort()
    return fitness

def get_elite(pop, elite_size, pop_fitness):
    elite = []
    for i in range(elite_size):
        elite.append(pop_fitness[i][1])
    return elite

def breed(parent1, parent2):
    child = []
    for gene1, gene2 in zip(parent1, parent2):
        if random.random() < 0.5:
            child.append(gene1)
        else:
            child.append(gene2)
    return child

def mutate(chromo, mutation_rate):
    for i in range(len(chromo)):
        if random.random() < mutation_rate:
            chromo[i] = random.choice(range(len(colors)))
    return chromo

def breed_population(elite, pop_size, mutation_rate):
    next_gen = []
    elitism = len(elite)
    for i in range(elitism):
        next_gen.append(elite[i])
    while len(next_gen) < pop_size:
        parent1 = random.choice(elite)
        parent2 = random.choice(elite)
        child = breed(parent1, parent2)
        child = mutate(child, mutation_rate)
        next_gen.append(child)
    return next_gen

2. 遗传算法求解图着色问题：

import random

def genetic_algorithm(graph, colors, pop_size, elite_size, mutation_rate, generations):
    pop = create_population(pop_size, graph)
    for i in range(generations):
        pop_fitness = rank_population(pop, graph)
        elite = get_elite(pop, elite_size, pop_fitness)
        next_gen = breed_population(elite, pop_size, mutation_rate)
        pop = next_gen
    best = pop_fitness[0]
    return best

def create_population(pop_size, graph):
    pop = []
    for i in range(pop_size):
        chromo = []
        for j in range(len(graph)):
            chromo.append(random.choice(range(len(colors))))
        pop.append(chromo)
    return pop

def rank_population(pop, graph):
    fitness = []
    for chromo in pop:
        score = 0
        for i in range(len(graph)):
            for j in range(len(graph)):
                if graph[i][j] and chromo[i] == chromo[j]:
                    score -= 1
        fitness.append((score, chromo))
    fitness.sort()
    return fitness

def get_elite(pop, elite_size, pop_fitness):
    elite = []
    for i in range(elite_size):
        elite.append(pop_fitness[i][1])
    return elite

def breed(parent1, parent2):
    child = []
    for gene1, gene2 in zip(parent1, parent2):
        if random.random() < 0.5:
            child.append(gene1)
        else:
            child.append(gene2)
    return child

def mutate(chromo, mutation_rate):
    for i in range(len(chromo)):
        if random.random() < mutation_rate:
            chromo[i] = random.choice(range(len(colors)))
    return chromo

def breed_population(elite, pop_size, mutation_rate):
    next_gen = []
    elitism = len(elite)
    for i in range(elitism):
        next_gen.append(elite[i])
    while len(next_gen) < pop_size:
        parent1 = random.choice(elite)
        parent2 = random.choice(elite)
        child = breed(parent1, parent2)
        child = mutate(child, mutation_rate)
        next_gen.append(child)
    return next_gen

3. 遗传算法求解图着色问题：

import random

def genetic_algorithm(graph, colors, pop_size, elite_size, mutation_rate, generations):
    pop = create_population(pop_size, graph)
    for i in range(generations):
        pop_fitness = rank_population(pop, graph)
        elite = get_elite(pop, elite_size, pop_fitness)
        next_gen = breed_population(elite, pop_size, mutation_rate)
        pop = next_gen
    best = pop_fitness[0]
    return best

def create_population(pop_size, graph):
    pop = []
    for i in range(pop_size):
        chromo = []
        for j in range(len(graph)):
            chromo.append(random.choice(range(len(colors))))
        pop.append(chromo)
    return pop

def rank_population(pop, graph):
    fitness = []
    for chromo in pop:
        score = 0
        for i in range(len(graph)):
            for j in range(len(graph)):
                if graph[i][j] and chromo[i] == chromo[j]:
                    score -= 1
        fitness.append((score, chromo))
    fitness.sort()
    return fitness

def get_elite(pop, elite_size, pop_fitness):
    elite = []
    for i in range(elite_size):
        elite.append(pop_fitness[i][1])
    return elite

def breed(parent1, parent2):
    child = []
    for gene1, gene2 in zip(parent1, parent2):
        if random.random() < 0.5:
            child.append(gene1)
        else:
            child.append(gene2)
    return child

def mutate(chromo, mutation_rate):
    for i in range(len(chromo)):
        if random.random() < mutation_rate:
            chromo[i] = random.choice(range(len(colors)))
    return chromo

def breed_population(elite, pop_size, mutation_rate):
    next_gen = []
    elitism = len(elite)
    for i in range(elitism):
        next_gen.append(elite[i])
    while len(next_gen) < pop_size:
        parent1 = random.choice(elite)
        parent2 = random.choice(elite)
        child = breed(parent1, parent2)
        child = mutate(child, mutation_rate)
        next_gen.append(child)
    return next_gen

这些代码示例仅供参考，具体实现可能需要根据具体问题进行修改和调整。