this is my pareto data, it consist of index numbering(it is not the front rank, just numbering) from 0 to 4(here my non dominated fronts are 5, but in other iteration it may vary) all the fronts are first front represent in attribute 'front', the chromosome of the corresponding front represent in the attribute 'chromosome' and crowd distance for each front chromosomes are represent in the attribute 'crowding_distance'.

Pareto Data:  {0: {'front': 1, 'chromosome': [6.297445324392338e-07, 8.554381392108586e-07, 5.997857494489643e-07, 7.779302231822395e-07, 9.610502086538751e-07, 4.106865964555608e-06, 9.888120345127656e-06, 1.8582935126825054e-05, 6.37563091012306e-05, 0.0001, 1.76605627732622e-05, 1.8232428150915757e-12, 1.011942126951676], 'crowding_distance': inf}, 
1: {'front': 1, 'chromosome': [5.928960950232728e-07, 5.246899010673617e-07, 2.3597983021331862e-07, 7.148382105908126e-07, 8.339013977174255e-07, 6.620291266445139e-06, 9.675350920195607e-06, 2.16e-06, 4.866275845768111e-05, 5.821626989510773e-06, 2.756180102128729e-05, 2.0036835949651954e-12, 1.2629316229645553], 'crowding_distance': 0.1632322064300471}, 
3: {'front': 1, 'chromosome': [3.079193741979233e-07, 5.493853563682161e-07, 5.962330051784952e-07, 6.836010899457363e-07, 3.6240808664115155e-07, 6.6606965969884e-06, 1.9937691158981114e-05, 1.7191728784754382e-05, 8.971850846373753e-05, 4.015723977320496e-05, 2.8692695462897902e-05, 2.2706303553800746e-12, 1.0269731687428354], 'crowding_distance': 0.27551689332394413}, 
4: {'front': 1, 'chromosome': [3.545511345343661e-07, 6.593533836342896e-07, 5.900895025175424e-07, 1e-06, 8.236672513566215e-07, 1e-06, 4.169815507706654e-06, 2.0582310847353626e-05, 3.822390169392882e-05, 4.692770400431635e-05, 2.428748383699893e-05, 7.56845078924463e-13, 1.1233087361103162], 'crowding_distance': 0.36413048613924726}

i am having hypervolume values for each fronts with index 0 to 4(here total front is 5, but in other iteration it may vary)
Hypervolume Values: {0: 0.0050570185867492845, 1: 0.00653640894186963, 2: 0.002625808236184579, 3: 0.00032955243037485966, 4: 0.0008320008204058407}

and I am having the elitist_points
elitist_points [0, 1, 3]

in my previous code(method 1) i selected the indices from the population but in the existing code, i didnot get all the population in the pareto data, i am only focusing the indices of the pareto data, from which i am going to perform binary tournamet selection later. so i need to choose the indices pairs from the pareto data (similar implementation like how it choosen the pair indices from population) in the mentod 2. please modify the method 2, accoriding to the method 1 implementation.

Method 1:(previous code)
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)

Method 2:(modified existing code)
def binary_tournament_selection_with_crossover_and_mutation(population, mutation_rate, crossover_rate, pareto_data, crowding_distance_threshold, elitist_points, hypervolume_values):
    selected_chromosomes = []
    
    # Filter indices based on non-dominated sort (those with a rank of 1, typical for Pareto fronts)
    valid_indices = [index for index, data in pareto_data.items() if data[‘front’] == 1]

    # Use valid indices for tournaments
    all_pairs = list(itertools.combinations(valid_indices, 2))
    random.shuffle(all_pairs)

    while len(selected_chromosomes) < len(population):  # ensure we fill up the new population
        if not all_pairs:
            all_pairs = list(itertools.combinations(valid_indices, 2))
            random.shuffle(all_pairs)
        index1, index2 = all_pairs.pop(0)