双种群遗传算法代码实例

双种群遗传算法是一种优化搜索策略，它结合了两个独立的种群（通常是随机初始化的一组解），通常用于解决复杂的组合优化问题。这种算法通常包含两个阶段：一种是适应度较高的种群（称为精英种群）和另一种是普通种群。以下是简化的Python代码示例：

import numpy as np

class DualPopulationGA:
    def __init__(self, problem_size, elite_ratio, crossover_rate, mutation_rate):
        self.problem_size = problem_size
        self.elite_ratio = elite_ratio
        self.crossover_rate = crossover_rate
        self.mutation_rate = mutation_rate
        self.elite_population = None
        self.normal_population = None

    def initialize(self, population_size):
        self.elite_population = np.random.uniform(0, 1, (int(population_size * self.elite_ratio), self.problem_size))
        self.normal_population = np.random.uniform(0, 1, (population_size - int(population_size * self.elite_ratio), self.problem_size))

    # 简化版选择、交叉和变异操作
    def evolve(self, fitness_func):
        self.update_fitness(fitness_func)
        elite_selected = self.select_elites()
        offspring = self.cross_over_and_mutate(elite_selected)
        self.normal_population = self.normal_population + offspring
        self.normal_population = self.normalize_population()

    # ...其他辅助函数省略...

    def select_elites(self):
        elite_fitness = [fitness_func(individual) for individual in self.elite_population]
        elite_indices = np.argsort(-np.array(elite_fitness))[:int(self.elite_ratio * len(self.elite_population))]
        return self.elite_population[elite_indices]

    # ...交叉和变异的具体实现省略...

# 使用示例
ga = DualPopulationGA(problem_size=10, elite_ratio=0.2, crossover_rate=0.8, mutation_rate=0.01)
ga.initialize(population_size=100)
for _ in range(num_iterations):
    ga.evolve(fitness_func)

这个例子中，`fitness_func`是一个评估个体适应度的函数，`update_fitness`、`cross_over_and_mutate`等是算法的核心部分，包括计算适应度值、基因重组和突变等过程。实际应用中，你需要根据具体的优化问题调整这些细节。