def elitist_replacement(
    current_population: Sequence[IndType],
    offspring: Sequence[IndType],
    hof: int = 1,
) -> list[IndType]:
    """Returns a new population constructed using the Elitist approach.
    HoF number of individuals from the current + offspring populations are
    kept in the new population. The remaining individuals are selected from
    the offspring population.

    Args:
        current_population  Sequence[IndType],: Current population in the algorithm
        offspring  Sequence[IndType],: Offspring population
        hof (int, optional): _description_. Defaults to 1.

    Raises:
        ValueError: Raises if the sizes of the population are different

    Returns:
          list[IndType]:
    """
    if len(current_population) != len(offspring):
        msg = f"The size of the current population ({len(current_population)}) != size of the offspring ({len(offspring)}) in elitist_replacement"
        raise ValueError(msg)

    combined_population = sorted(
        itertools.chain(current_population, offspring),
        key=attrgetter("fitness"),
        reverse=True,
    )
    top = combined_population[:hof]
    return list(top + offspring[1:])

def first_improve_replacement(
    current_population: Sequence[IndType],
    offspring: Sequence[IndType],
) -> list[IndType]:
    """Returns a new population produced by a greedy operator.
    Each individual in the current population is compared with its analogous in the offspring population
    and the best survives

    Args:
        current_population ( Sequence[IndType],): Current population in the algorithm
        offspring ( Sequence[IndType],): Offspring population

    Raises:
        ValueError: Raises if the sizes of the population are different

    Returns:
         list[IndType]: New population
    """
    if len(current_population) != len(offspring):
        msg = f"The size of the current population ({len(current_population)}) != size of the offspring ({len(offspring)}) in first_improve_replacement"
        raise ValueError(msg)

    return [a if a > b else b for a, b in zip(current_population, offspring)]

def generational_replacement(
    current_population: Sequence[IndType],
    offspring: Sequence[IndType],
) -> list[IndType]:
    """Returns the offspring population as the new current population

    Args:
        current_population ( Sequence[IndType],): Current population in the algorithm
        offspring ( Sequence[IndType],): Offspring population

    Raises:
        ValueError: Raises if the sizes of the population are different

    Returns:
         Sequence[IndType]: New population
    """
    if len(current_population) != len(offspring):
        msg = f"The size of the current population ({len(current_population)}) != size of the offspring ({len(offspring)}) in generational replacement"
        raise ValueError(msg)

    return offspring[:]