import math def calculate_similarity(chromosome1, chromosome2, current_iteration, total_iterations): a = math.sqrt(1 - ((current_iteration / total_iterations) ** 2)) num_similar = sum([1 for gene1, gene2 in zip(chromosome1, chromosome2) if gene1 == gene2]) similarity = a * (num_similar / len(chromosome1)) if similarity > 0.9: return "相似" else: return "不相似" def calculate_density(population, chromosome, a): similar_count = sum([1 for other_chromosome in population if calculate_similarity(chromosome, other_chromosome) >= a]) density = similar_count / len(population) return density接下来我需要进行免疫选择概率，免疫选择概率为聚合适应度，且聚合适应度为包含浓度和适应度的函数，请帮我形成代码

下面是根据您的要求编写的代码，用于计算免疫选择概率以及聚合适应度：

import math

def calculate_similarity(chromosome1, chromosome2, current_iteration, total_iterations):
    a = math.sqrt(1 - ((current_iteration / total_iterations) ** 2))
    num_similar = sum([1 for gene1, gene2 in zip(chromosome1, chromosome2) if gene1 == gene2])
    similarity = a * (num_similar / len(chromosome1))
    return similarity

def calculate_density(population, chromosome, a):
    similar_count = sum([1 for other_chromosome in population if calculate_similarity(chromosome, other_chromosome, current_iteration, total_iterations) >= a])
    density = similar_count / len(population)
    return density

def calculate_aggregated_fitness(population, a):
    aggregated_fitness = []
    for chromosome in population:
        fitness = calculate_fitness(chromosome)  # 计算适应度
        density = calculate_density(population, chromosome, a)  # 计算浓度
        aggregated_fitness.append(fitness + density)  # 聚合适应度为适应度 + 浓度
    return aggregated_fitness

在上面的代码中，我添加了一个名为`calculate_aggregated_fitness`的新函数，用于计算每个染色体的聚合适应度。在该函数中，我们首先计算每个染色体的适应度（`calculate_fitness`函数表示），然后计算该染色体与整个种群中其他染色体的相似性浓度（`calculate_density`函数表示）。最后，将适应度和浓度相加得到聚合适应度，并将其存储在一个列表中。

请注意，`current_iteration`和`total_iterations`参数需要根据您的实际情况进行传递。

希望这可以满足您的需求！如果您有任何其他问题，请随时提问。