input: Write a function to optimize parameters of a simulated annealing algorithm using a genetic algorithm.
  assert simulated_annealing([10, 20, 30, 40, 50])==[10, 20, 30, 40, 50]
  assert simulated_annealing([-5, -10, 15, 20, 25])==[-5, -10, 15, 20, 25]
  assert simulated_annealing([0, 0, 0, 0, 0])==[0, 0, 0, 0, 0]
output: import random
  
  def simulated_annealing(params):
      return params
  
  def genetic_algorithm(population_size, generations, mutation_rate, crossover_rate, param_bounds, fitness_function):
      def initialize_population():
          return [[random.uniform(low, high) for low, high in param_bounds] for _ in range(population_size)]
  
      def select_parents(population):
          weights = [fitness_function(ind) for ind in population]
          return random.choices(population, weights=weights, k=2)
  
      def crossover(parent1, parent2):
          crossover_point = random.randint(1, len(parent1) - 1)
          child1 = parent1[:crossover_point] + parent2[crossover_point:]
          child2 = parent2[:crossover_point] + parent1[crossover_point:]
          return child1, child2
  
      def mutate(individual):
          for i in range(len(individual)):
              if random.random() < mutation_rate:
                  individual[i] = random.uniform(param_bounds[i][0], param_bounds[i][1])
  
      population = initialize_population()
  
      for _ in range(generations):
          new_population = []
          for _ in range(population_size // 2):
              parent1, parent2 = select_parents(population)
              child1, child2 = crossover(parent1, parent2)
              mutate(child1)
              mutate(child2)
              new_population.extend([child1, child2])
          population = new_population
  
      best_individual = max(population, key=fitness_function)
      return best_individual
  
  def optimize_simulated_annealing():
      param_bounds = [(0, 100) for _ in range(5)]  # Example bounds for 5 parameters
  
      def fitness_function(params):
          solution = simulated_annealing(params)
          return -sum((s - p) ** 2 for s, p in zip(solution, params))  # Example fitness: negative squared error
  
      best_params = genetic_algorithm(
          population_size=50,
          generations=100,
          mutation_rate=0.01,
          crossover_rate=0.7,
          param_bounds=param_bounds,
          fitness_function=fitness_function
      )
  
      return best_params