Как решить проблему TSP с помощью пакета pyGAD?

Используя пакет PyGAD, как сгенерировать элемент популяций с элементом, не повторяющимся между 1 и 12? В случайной совокупности он всегда имеет двойное значение. У меня нет идеала, как этого избежать. Или мне следует манипулировать функцией обратного вызова при создании новой популяции?

import pandas as pd
import numpy as np
import pygad
xs = [8, 50, 18, 35, 90, 40, 84, 74, 34, 40, 60, 74]
ys = [3, 62, 0, 25, 89, 71, 7, 29, 45, 65, 69, 47]
cities = ['Z', 'P', 'A', 'K', 'O', 'Y', 'N', 'X', 'G', 'Q', 'S', 'J']
locations = list(zip(xs, ys, cities))

def fitness_func(solution, solution_idx):
    # Calculating the fitness value of each solution in the current population.
    # The fitness function calulates the sum of products between each input and its corresponding weight.
    # output = numpy.sum(solution*function_inputs)
    # fitness = 1.0 / numpy.abs(output - desired_output)
    
    total_length = 0
    itemidx=0
    
    for loc in solution:
        if itemidx>0 :
            cityidx1 = loc-1
            cityidx2 =solution[itemidx-1]-1   
            total_length +=((xs[cityidx1] - xs[cityidx2]) ** 2 + (ys[cityidx1] - ys[cityidx2]) ** 2) ** (1 / 2)  
            # print(xs[cityidx1],ys[cityidx1], xs[cityidx1], ys[cityidx2],total_length )
        elif  itemidx==solution.size :
            cityidx1 = loc-1
            cityidx2 =solution[itemidx-1]-1 
            total_length +=((xs[cityidx1] - xs[cityidx2]) ** 2 + (ys[cityidx1] - ys[cityidx2]) ** 2) ** (1 / 2)  
            if ((xs[cityidx1] - xs[cityidx2]) ** 2 + (ys[cityidx1] - ys[cityidx2]) ** 2) ** (1 / 2)  <0:
                print('ERROR',((xs[cityidx1] - xs[cityidx2]) ** 2 + (ys[cityidx1] - ys[cityidx2]) ** 2) ** (1 / 2)  )
            # print(xs[cityidx1],ys[cityidx1], xs[cityidx1], ys[cityidx2],total_length )
            
            cityidx2 =solution[0] 
            total_length +=((xs[itemidx] - xs[0]) ** 2 + (ys[itemidx] - ys[0]) ** 2) ** (1 / 2) 
            # print(total_length)
        itemidx += 1
    #print("fitness_func",total_length,solution,solution_idx)    
    return total_length*-1 #fitness

fitness_function = fitness_func
num_generations = 50 # Number of generations.
num_parents_mating = 7 # Number of solutions to be selected as parents in the mating pool.
sol_per_pop = 50 # Number of solutions in the population.
num_genes = 12  

init_range_low = 1
init_range_high = 12

parent_selection_type = "rank" # Type of parent selection.
keep_parents = 7 # Number of parents to keep in the next population. -1 means keep all parents and 0 means keep nothing.

crossover_type = "single_point" # Type of the crossover operator.

# Parameters of the mutation operation.
mutation_type = "swap" # Type of the mutation operator.
mutation_percent_genes = 10 

last_fitness = 0
population_list=[]
gene_space = [i for i in range(1, 13)]
for i in range(sol_per_pop):
    nxm_random_num=list(np.random.permutation(gene_space)) 
    population_list.append(nxm_random_num) # add to the population_list
ga_instance = pygad.GA(num_generations=num_generations,
                       num_parents_mating=num_parents_mating, 
                       fitness_func=fitness_function,
                       sol_per_pop=sol_per_pop, 
                       initial_population=population_list,
                       num_genes=num_genes,

                       gene_space = gene_space, #  
                    #    init_range_low=init_range_low,
                    #    init_range_high=init_range_high,

                       parent_selection_type=parent_selection_type,
                       keep_parents=keep_parents,
                       crossover_type=crossover_type,
                       mutation_type=mutation_type,
                       mutation_percent_genes=mutation_percent_genes
                       )
ga_instance.run()
solution, solution_fitness, solution_idx = ga_instance.best_solution()
print("best_solution: {solution}".format(solution =solution)) 

#best_solution: [ 3  4 12 10  6  9  2 10 12 10  6  9] 
#**Is any way to get new gerneration that elements not duplication**

Любая помощь будет высоко ценится!