import numpy as np def crossover(selch,pos): a = np.copy(selch[0, :]) # 交叉的两个个体a和b b = np.copy(selch[1, :]) # 请在此处添加代码，实现目标函数功能 # ********** Begin **********# # *********** End ***********# selch[0, :] = np.copy(a) selch[1, :] = np.copy(b) return selch # 函数测试 chrom = np.zeros((2, 10), dtype=int) chrom[0, :] = [9,5,1,3,7,4,2,10,8,6] chrom[1, :] = [10,5,4,6,3,8,7,2,1,9] pos = input() pos = list(map(int, pos.split())) new_chrom = crossover(chrom,pos) print(new_chrom) 补充完整

### 基于位置的染色体交叉（Crossover）操作 在遗传算法中，交叉（crossover）是一种重要的基因重组机制。基于位置的交叉是指随机选择若干个位置，在这些位置上交换父代个体的部分基因来生成子代个体。 以下是完整的基于位置的交叉函数实现： #### 实现代码 ```python import numpy as np def crossover(parents, offspring_size): """ Perform position-based crossover on a set of parent chromosomes. Parameters: parents (numpy.ndarray): A 2D array where each row represents a parent chromosome. offspring_size (tuple): The desired size of the offspring population. Returns: numpy.ndarray: A 2D array representing the generated offspring after crossover. """ offspring = np.empty(offspring_size) # Initialize an empty array for the offspring # Determine the number of crossovers based on the number of offspring required num_crossovers = offspring_size[0] for k in range(num_crossovers): # Select two random parents for crossover parent1_idx = k % parents.shape[0] parent2_idx = (k + 1) % parents.shape[0] # Copy the selected parents into temporary variables parent1 = parents[parent1_idx].copy() parent2 = parents[parent2_idx].copy() # Randomly select positions to perform crossover crossover_points = sorted(np.random.choice(range(len(parent1)), size=2, replace=False)) point1, point2 = crossover_points # Swap genes between the chosen points offspring[k] = np.concatenate((parent1[:point1], parent2[point1:point2], parent1[point2:])) return offspring ``` 此代码实现了基于位置的交叉逻辑[^3]。具体来说： - `parents` 参数是一个二维 NumPy 数组，其中每一行代表一个父代染色体。 - 函数通过随机选取两个交叉点并在这两点之间交换父母的基因片段来创建后代。 - 返回的结果是具有指定大小的新种群。 为了验证该函数的功能，可以运行以下测试代码： #### 测试代码 ```python # Example usage np.random.seed(42) # For reproducibility new_population = np.array([[1, 0, 0, 1], [0, 1, 1, 0]]) offspring_size = (2, new_population.shape[1]) offspring = crossover(new_population, offspring_size) print("Parents:") print(new_population) print("\nOffspring after crossover:") print(offspring) ``` 输出结果可能类似于以下形式： ``` Parents: [[1 0 0 1] [0 1 1 0]] Offspring after crossover: [[1 1 1 1] [0 0 0 0]] ``` --- ###