This programming assignment is in python, using the following modules, random, numpy, and matplotlib. I'm having trouble with the selection function as well as the crossover methods. I would appreciate any help and would write a good review as well. Thank you in advance!

The following GA parameters should be used for the initial run of the implementation: Population size: 100 Crossover rate: 70% Mutation rate: 0.1% Maximum number of iterations: 1,000 Selection method: Tournament (K=2) Genetic Algorithm Description: 1. [Start] Generate random population of n chromosomes. 2. [Fitness] Evaluate the fitness f(x) of each chromosome x in the population. 3. [New population] Create a new population by repeating the following steps until the new population is complete: 1. [Selection Select two parent chromosomes from the population according to their fitness (the better, the greater the chance to be selected). 2. (Crossover] With a crossover probability cross over the parents form new offspring children). If no crossover is performed, both offspring are an exact copy of their parents. 3. [Mutation] For each chromosome and with a mutation probability mutate new offspring at one position in chromosome. If no mutation is performed, offspring is an exact copy of the parent. 4. [Replace] New population replaces old population. 5. [Test] If the maximum number of iterations is satisfied, stop, and return the best solution in current population. 6. [Loop] Go to Step 2. to F=1-(-) CN where c is the cost of the overlay network, and Cy is the total cost of a fully connected network. Afterwards, a selection method needs to be implemented. You are asked to implement the tournament colection method Tournament colection