i ned to midify the below function 'binary_tournament_selection_with_crossover_and_mutation' that perform binary tournament selection process, here in the present existing implementation, i am having some computation problems, that the pareto data that is computed having only the non dominated sets of chromosomes among the total chromosomes present in the entire population. i need you to update the existing code for binary tournament selection with the use of pareto data(only non dominated sets), crowding distance, hypervolume values and elitist points appropriately for the computation necessary. completely modify the tournament selection process as per the need and provide me the best computation methodology for the binary tournament selection process.

i need you to include the methodology of Selected indices, in the updatd code appropriately to be properly suitable for selecting data from the pareto data
    # Generate all possible unique pairs of indices
    all_pairs = list(itertools.combinations(range(len(population)), 2))

    # Shuffle the pairs randomly
    random.shuffle(all_pairs)

    # Ensure that you have pairs equal to the total length of the population
    while len(selected_chromosomes) < len(population):
        index1, index2 = all_pairs.pop(0)

        # Ensure that selected indices are distinct and not in reverse order        
        while index2 == index1 or (index1, index2) in selected_chromosomes or (index2, index1) in selected_chromosomes:
            index1, index2 = all_pairs.pop(0)

        print(f"Selected indices: {index1}, {index2}") 


def binary_tournament_selection_with_crossover_and_mutation(population, mutation_rate, crossover_rate, pareto_data, crowding_distance_threshold, elitist_points, hypervolume_values):
    selected_chromosomes = []

    # Generate all possible unique pairs of indices
    all_pairs = list(itertools.combinations(range(len(population)), 2))

    # Shuffle the pairs randomly
    random.shuffle(all_pairs)

    # Ensure that you have pairs equal to the total length of the population
    while len(selected_chromosomes) < len(population):
        index1, index2 = all_pairs.pop(0)

        # Ensure that selected indices are distinct and not in reverse order        
        while index2 == index1 or (index1, index2) in selected_chromosomes or (index2, index1) in selected_chromosomes:
            index1, index2 = all_pairs.pop(0)

        print(f"Selected indices: {index1}, {index2}")     
                
        # Ensure that selected indices are within the range of hypervolume_values
        #index1 = min(index1, len(hypervolume_values) - 1)
        #index2 = min(index2, len(hypervolume_values) - 1)
        print("index1, index2", index1, index2)
        chromosome1, chromosome2 = population[index1], population[index2]
        print("chromosome1, chromosome2", chromosome1, chromosome2)
        # Get the rank, crowding distance, and hypervolume for both individuals
        rank1 = pareto_data.get(index1, {"front": [float('inf')], "crowding_distance": [float('inf')]}).get("front", [float('inf')])[0]
        print("rank1", rank1)
        rank2 = pareto_data.get(index2, {"front": [float('inf')], "crowding_distance": [float('inf')]}).get("front", [float('inf')])[0]
        print("rank2", rank2)
        crowding_distance1 = pareto_data.get(index1, {"crowding_distance": [float('inf')]}).get("crowding_distance", [float('inf')])[0]
        print("crowding_distance1", crowding_distance1)
        crowding_distance2 = pareto_data.get(index2, {"crowding_distance": [float('inf')]}).get("crowding_distance", [float('inf')])[0]
        print("crowding_distance2", crowding_distance2)
        hypervolume1 = hypervolume_values[index1]
        print("hypervolume1", hypervolume1)
        hypervolume2 = hypervolume_values[index2]
        print("hypervolume2", hypervolume2)
        # Check if the individuals are in the elitist points
        is_elitist1 = index1 in elitist_points
        is_elitist2 = index2 in elitist_points

        # Choose the winner based on rank, crowding distance, hypervolume, and elitist status
        if (is_elitist1 and not is_elitist2) or (is_elitist1 and is_elitist2):
            selected_chromosomes.append((chromosome1, rank1))
        elif is_elitist2 and not is_elitist1:
            selected_chromosomes.append((chromosome2, rank2))
        else:
            if rank1 < rank2 or (rank1 == rank2 and crowding_distance1 > crowding_distance2) or (
                    rank1 == rank2 and crowding_distance1 == crowding_distance2 and hypervolume1 > hypervolume2):
                selected_chromosomes.append((chromosome1, rank1))
            else:
                selected_chromosomes.append((chromosome2, rank2))

    # Print selected chromosomes before crossover and mutation
    print(f"Selected chromosomes before crossover and mutation: {selected_chromosomes}")