The algorithm uses tournament selection to select which parents to use toreproduce the children. You are asked to use and implement a new selection method (moremethods are alsopossible if you want)...

#!/usr/bin/env python
# coding: utf-8
# In[36]:
import random
import numpy as np
get_ipython().run_line_magic('matplotlib', 'inline')
def create_reference_solution(chromosome_length):
number_of_ones = int(chromosome_length / 2)
# Build an array with an equal mix of zero and ones
reference = np.zeros(chromosome_length)
reference[0: number_of_ones] = 1
# Shuffle the array to mix the zeros and ones
np.random.shuffle(reference)

return reference
def create_starting_population(individuals, chromosome_length):
# Set up an initial array of all zeros
population = np.zeros((individuals, chromosome_length))
# Loop through each row (individual)
for i in range(individuals):
# Choose a random number of ones to create
ones = random.randint(0, chromosome_length)
# Change the required number of zeros to ones
population[i, 0:ones] = 1
# Sfuffle row
np.random.shuffle(population[i])

return population
def calculate_fitness(reference, population):
# Create an array of True/False compared to reference
identical_to_reference = population == reference
# Sum number of genes that are identical to the reference
fitness_scores = identical_to_reference.sum(axis=1)

return fitness_scores
def select_individual_by_tournament(population, scores):
# Get population size
population_size = len(scores)

# Pick individuals for tournament
fighter_1 = random.randint(0, population_size-1)
fighter_2 = random.randint(0, population_size-1)

#print ("Fighter 1", fighter_1)
#print ("Fighter 2", fighter_2)

# Get fitness score for each
fighter_1_fitness = scores[fighter_1]
fighter_2_fitness = scores[fighter_2]

# Identify undividual with highest fitness
# Fighter 1 will win if score are equal
if fighter_1_fitness >= fighter_2_fitness:
winner = fighter_1
else:
winner = fighter_2

#print ("Winner ", winner)
# Return the chromsome of the winner
return population[winner, :]
def select_individual_by_random_selection(population, scores):
# Get population size
population_size = len(scores)

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