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

    Args:
        individual Instance or Solution: First individual to apply crossover. Returned object
        other 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(individual) != len(other):
        msg = f"Individual of different length in uniform_crossover. len(ind) = {len(individual)} != len(other) = {len(other)}"
        raise ValueError(msg)

    offspring = individual.clone()
    cross_point = np.random.default_rng().integers(low=0, high=len(individual))
    offspring[cross_point:] = other[cross_point:]
    return offspring

def uniform_crossover(
    individual: IndType, other: IndType, cxpb: np.float64 = np.float64(0.5)
) -> IndType:
    """Uniform Crossover Operator for Instances and Solutions

    Args:
        individual (IndType): First individual to apply crossover. Returned object.
        other (IndType): Second individual to apply crossover
        cxpb (float64, optional): Crossover probability. Defaults to 0.5.

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

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

    probs = np.random.default_rng().random(size=len(individual))
    genotype = np.empty_like(individual)
    genotype = np.where(probs <= cxpb, individual, other)

    return individual.clone_with(variables=genotype)