Choose a random sample from a list with bias in Python

To give a little background, I am coding a genetic algorithm to solve the Traveling Salesman Problem (TSP). In my population, I have an ordered list of paths from shortest to longest (fittest to least fit), and their respective distances, like this:

population = [
[[a, b, c, d], [10.12]],
[[b, c, a, d], [11.33]],
[[d, a, c, b], [11.5]],
[[b, a, d, c], [12.07]]
...]

After the population is ordered by their fitness, I need to kill half of them randomly, but in such a fashion that the fitter the member, the better their chance of surviving.

1. I've tried using random.choices() and passing a list with chances of probability (bias) into the weights parameter, and my desired size of half the original population as k like this:

   # returns something like [0.99, 0.75, 0.65, 0.22...]
   bias_weights = [x / len(population) for x in range(len(population))]
   
   random.choices(population, weights=bias_weights, k=len(population) / 2)
   
   

   The problem with the code above is that it produces duplicates in my list, and it's very messy to get rid of them and keep the population size at 50%.

2. I've also tried using the np.random.choices() from the numpy library, but it requires the list that I am passing to be 1D, and the list of weights and biases to add up to 1.

Is there any other way to do this?