11、遗传算法：原理、应用与挑战

遗传算法：原理、应用与挑战

1. 遗传算法基础

遗传算法是一种模拟自然选择和遗传机制的优化算法。其核心思想是通过模拟生物进化过程，不断迭代生成新的解，直到找到最优解或满足特定条件。以下是遗传算法的基本运行函数：

# Run the genetic algorithm for max_generations iterations
# and return the best individual found
def run(self) -> C:
    best: C = max(self._population, key=self._fitness_key)
    for generation in range(self._max_generations):
        # early exit if we beat threshold
        if best.fitness() >= self._threshold: 
            return best
        print(f"Generation {generation} Best {best.fitness()} Avg {mean(map(self._fitness_key, self._population))}")
        self._reproduce_and_replace()
        self._mutate()
        highest: C = max(self._population, key=self._fitness_key)
        if highest.fitness() >