def one_point_crossover(ind: IndType, other: IndType) -> IndType:
    """One point crossover

    Args:
        ind Instance or Solution: First individual to apply crossover. Returned object
        ind_2 Instance or Solution: Second individual to apply crossover

    Raises:
        ValueError: When the len(ind_1) != len(ind_2)

    Returns:
        Instance or Solution: New individual
    """
    if len(ind) != len(other):
        msg = f"Individual of different length in uniform_crossover. len(ind) = {len(ind)} != len(other) = {len(other)}"
        raise ValueError(msg)
    offspring = ind.clone()
    cross_point = np.random.default_rng().integers(low=0, high=len(ind))
    offspring[cross_point:] = other[cross_point:]
    return offspring

def uniform_crossover(ind: IndType, other: IndType, cxpb: float = 0.5) -> IndType:
    """Uniform Crossover Operator for Instances and Solutions

    Args:
        ind Instance or Solution: First individual to apply crossover. Returned object.
        ind_2 Instance or Solution: Second individual to apply crossover
        cxpb (float, optional): _description_. Defaults to 0.5.

    Raises:
        ValueError: When the len(ind_1) != len(ind_2)

    Returns:
        Instance or Solution: New individual
    """
    if len(ind) != len(other):
        msg = f"Individual of different length in uniform_crossover. len(ind) = {len(ind)} != len(other) = {len(other)}"
        raise ValueError(msg)

    probs = np.random.default_rng().random(size=len(ind))
    chromosome = [i if pb <= cxpb else j for pb, i, j in zip(probs, ind, other)]
    offspring = ind.clone()
    offspring[:] = chromosome
    return offspring