In Python, write three parent selection mechanisms,multi-pointer selection (MPS),tournament selection(sampling without replacement),and random uniform selection.

********BELOW IS THE MAIN.py file.**********

import random import numpy

import initialization import evaluation import recombination import mutation import parentSelection import survivorSelection

def main(): random.seed() numpy.random.seed()

string_length = 8 popsize = 20 mating_pool_size = int(popsize*0.5) # has to be even tournament_size = 3 mut_rate = 0.2 xover_rate = 0.9 gen_limit = 50

# initialize population gen = 0 # initialize the generation counter population = initialization.permutation(popsize, string_length) fitness = [] for i in range (0, popsize): fitness.append(evaluation.fitness_8queen(population[i])) print("generation", gen, ": best fitness", max(fitness), "average fitness", sum(fitness)/len(fitness))

# evolution begins while gen < gen_limit: # pick parents parents_index = parentSelection.MPS(fitness, mating_pool_size) # parents_index = parentSelection.tournament(fitness, mating_pool_size, tournament_size) # parents_index = parentSelection.random_uniform(popsize, mating_pool_size)

# in order to randomly pair up parents random.shuffle(parents_index) # reproduction offspring =[] offspring_fitness = [] i= 0 # initialize the counter for parents in the mating pool # offspring are generated using selected parents in the mating pool while len(offspring) < mating_pool_size: # recombination if random.random() < xover_rate: off1,off2 = recombination.permutation_cut_and_crossfill(population[parents_index[i]], population[parents_index[i+1]]) else: off1 = population[parents_index[i]].copy() off2 = population[parents_index[i+1]].copy()

# mutation if random.random() < mut_rate: off1 = mutation.permutation_swap(off1) if random.random() < mut_rate: off2 = mutation.permutation_swap(off2) offspring.append(off1) offspring_fitness.append(evaluation.fitness_8queen(off1)) offspring.append(off2) offspring_fitness.append(evaluation.fitness_8queen(off2)) i = i+2 # update the counter

# form the population of next generation population, fitness = survivorSelection.mu_plus_lambda(population, fitness, offspring, offspring_fitness) # population, fitness = survivorSelection.replacement(population, fitness, offspring, offspring_fitness) # population, fitness = survivorSelection.random_uniform(population, fitness, offspring, offspring_fitness) gen = gen + 1 # update the generation counter print("generation", gen, ": best fitness", max(fitness), "average fitness", sum(fitness)/len(fitness)) # evolution ends # print the final best solution(s) k = 0 for i in range (0, popsize): if fitness[i] == max(fitness): print("best solution", k, population[i], fitness[i]) k = k+1

# end of main

main()

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********BELOW IS Partially filled parentSelection.py************

#multi-pointer selection (MPS)

def MPS(fitness, mating_pool_size): selected_to_mate = [] # list of the indices of picked parents

#student code begin

#student code end

return selected_to_mate

#tournament selection without replacement def tournament(fitness, mating_pool_size, tournament_size): selected_to_mate = [] # list of the indices of picked parents

#student code begin

#student code end return selected_to_mate

#randomly uniformly pick parents def random_uniform (population_size, mating_pool_size):

selected_to_mate = [] # list of the indices of picked parents

#student code begin

#student code end return selected_to_mate