def batch_uniform_one_mutation(
    population: np.ndarray, lb: np.ndarray, ub: np.ndarray
) -> np.ndarray:
    """Performs uniform one mutation in batches

    Args:
        population (np.ndarray): Batch of individuals to mutate
        lb (np.ndarray): Lower bound for each dimension
        up (np.ndarray): Upper bound for each dimension

    Raises:
        ValueError: If the dimension of the individuals do not match the bounds

    Returns:
        np.ndarray: mutated population
    """
    dimension = len(population[0])
    n_individuals = len(population)
    if len(lb) != len(ub) or dimension != len(lb):
        msg = f"The size of individuals ({dimension}) and bounds {len(lb)} is different in uniform_one_mutation"
        raise ValueError(msg)

    rng = np.random.default_rng(84793258734753)
    mutation_points = rng.integers(low=0, high=dimension, size=n_individuals)
    new_values = rng.uniform(
        low=lb[mutation_points], high=ub[mutation_points], size=n_individuals
    )

    population[np.arange(n_individuals), mutation_points] = new_values

    return population