By changing values in one array it automatically changes values in another array. How to avoid that happening?

I have created a genetic algorithm to create children from parents.
At the start of the algorithm a random workload(arrays of sub-arrays) is created.
Workload L=2, population size N=30, InputsNumber=3 and mutation rate m=0.05. Then I do some score calculations for the population to pick the 2 workloads(the parents) with highest score. Now the new population are the parents only. After that form the 2 parents I create children with crossover function and mutation function and I add the children to the population with the parents. Now I do the same thing for 10 times and every time I pick the 2 best to be the parents from the population. Now the problem is that when I change the children values in mutation function, all of a sudden the parents change their values to children values. How to avoid that? The parents are correct BEFORE I call the mutation() function and AFTER the mutation function they change. I can’t understand why this is happening. Please help me find why this is happening!

HERE IS AN EXAMPLE OUTPUT: https://ibb.co/m5T0hSq

Parent or child array example: [[0 0 0],[0 0 0]]
Parents or children array example: [ [[0 0 0],[0 0 0]], [[0 0 0],[0 0 0]] ]

def generateRandomWorkload(inputsNumber, L, N):
   global population
   individualWorkload = []
   for n in range(N):
      for i in range(L):
         individual = [0 for _ in range(len(inputsNumber))]
         individualWorkload.append(individual)

      population.append(individualWorkload)
      individualWorkload = []


def crossover(L):
   global parents, children

   children = []

   for i in range(2):
      C = random.randint(0, 1)
      R = random.randint(0, L)
      if C == 0:
         child = parents[0][0:R] + parents[1][R:L]
         children.append(child)
      elif C == 1:
         child = parents[1][0:R] + parents[0][R:L]
         children.append(child)

return children


def mutation(mutation_rate):
   global children

   for i in range(len(children)):
      for j in range(len(children[i])):
         for k in range(len(children[i][j])):
            r = random.uniform(0, 1)
            if r <= mutation_rate:
               children[i][j][k] = 1 - children[i][j][k]

return children


def geneticAlgorithm(inputsNumber, L, N):
   global parents, children, population


   generateRandomWorkload(inputsNumber, L, N)
   print("SEED POPULATION: ", population, "n n")

   for generation in range(10):
       print(bcolors.OKGREEN + "MEASUREMENTS OF ", generation+1, " GENERATION" + bcolors.ENDC)
       for individualWorkload in population:
           ### HERE I CALCULATE SOME SCORES (scoreI) ###

       # Parents
       print("PARENTS SELECTION... n")
       scoreI.sort(key=lambda x: x[1])
       parents = [scoreI[-1][0], scoreI[-2][0]]
       population = [parents[0], parents[1]]
       print("SELECTED PARENTS: n", parents, "n")
       print("PARENTS IN POPULATION:", population)

       # Crossover
       print("BEGIN CROSSOVER... n")
       print("PARENTS: ", parents)
       children = crossover(L)
       print("CROSSOVER CHILDREN:n", children, "n")

       # Mutation
       print("BEGIN MUTATION...n")
       print("PARENTS: ", parents)
       children = mutation(0.05)
       print("MUTATION CHILDREN:n", children, "n")

       # New population
       population.append(children[0])
       population.append(children[1])

       print("PARENTS: ", parents)
       print("NEW POPULATION: n", population, "n")