包含相似度和浓度的免疫遗传算法代码实现

以下是一个简单的免疫遗传算法的代码实现，其中包含相似度和浓度的概念：

import random

# 定义免疫遗传算法的相关参数
population_size = 50
chromosome_length = 10
mutation_rate = 0.01
crossover_rate = 0.6
num_generations = 100

# 初始化种群
def initialize_population(size, length):
    population = []
    for _ in range(size):
        chromosome = [random.randint(0, 1) for _ in range(length)]
        population.append(chromosome)
    return population

# 计算相似度
def calculate_similarity(chromosome1, chromosome2):
    num_similar = sum([1 for gene1, gene2 in zip(chromosome1, chromosome2) if gene1 == gene2])
    similarity = num_similar / len(chromosome1)
    return similarity

# 计算浓度
def calculate_density(population, chromosome):
    total_similarity = sum([calculate_similarity(chromosome, other_chromosome) for other_chromosome in population])
    density = total_similarity / len(population)
    return density

# 选择个体
def selection(population):
    selected_population = []
    for _ in range(len(population)):
        chromosome1 = random.choice(population)
        chromosome2 = random.choice(population)
        if calculate_density(population, chromosome1) > calculate_density(population, chromosome2):
            selected_population.append(chromosome1)
        else:
            selected_population.append(chromosome2)
    return selected_population

# 交叉操作
def crossover(parent1, parent2):
    crossover_point = random.randint(1, len(parent1)-1)