See here for description of this code.

```
import random as rn
import numpy as np
import matplotlib.pyplot as plt
# For use in Jupyter notebooks only:
# Create reference solutions
# --------------------------
def create_reference_solutions(chromosome_length, solutions):
"""
Function to create reference chromosomes that will mimic an ideal solution
"""
references = np.zeros((solutions, chromosome_length))
number_of_ones = int(chromosome_length / 2)
for solution in range(solutions):
# 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)
references[solution,:] = reference
return references
# Evaluate solutions
# ------------------
def calculate_fitness(reference, population):
"""
Calculate how many binary digits in each solution are the same as our
reference solution.
"""
# 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 score_population(population, references):
"""
Loop through all reference solutoins and request score/fitness of
populaiton against that reference solution.
"""
scores = np.zeros((population.shape[0], references.shape[0]))
for i, reference in enumerate(references):
scores[:,i] = calculate_fitness(reference, population)
return scores
# Calculate crowding and select a population based on crowding scores
# -------------------------------------------------------------------
def calculate_crowding(scores):
"""
Crowding is based on a vector for each individual
All scores are normalised between low and high. For any one score, all
solutions are sorted in order low to high. Crowding for chromsome x
for that score is the difference between the next highest and next
lowest score. Total crowding value sums all crowding for all scores
"""
population_size = len(scores[:, 0])
number_of_scores = len(scores[0, :])
# create crowding matrix of population (row) and score (column)
crowding_matrix = np.zeros((population_size, number_of_scores))
# normalise scores (ptp is max-min)
normed_scores = (scores - scores.min(0)) / scores.ptp(0)
# calculate crowding distance for each score in turn
for col in range(number_of_scores):
crowding = np.zeros(population_size)
# end points have maximum crowding
crowding[0] = 1
crowding[population_size - 1] = 1
# Sort each score (to calculate crowding between adjacent scores)
sorted_scores = np.sort(normed_scores[:, col])
sorted_scores_index = np.argsort(
normed_scores[:, col])
# Calculate crowding distance for each individual
crowding[1:population_size - 1] = \
(sorted_scores[2:population_size] -
sorted_scores[0:population_size - 2])
# resort to orginal order (two steps)
re_sort_order = np.argsort(sorted_scores_index)
sorted_crowding = crowding[re_sort_order]
# Record crowding distances
crowding_matrix[:, col] = sorted_crowding
# Sum croding distances of each score
crowding_distances = np.sum(crowding_matrix, axis=1)
return crowding_distances
def reduce_by_crowding(scores, number_to_select):
"""
This function selects a number of solutions based on tournament of
crowding distances. Two members of the population are picked at
random. The one with the higher croding dostance is always picked
"""
population_ids = np.arange(scores.shape[0])
crowding_distances = calculate_crowding(scores)
picked_population_ids = np.zeros((number_to_select))
picked_scores = np.zeros((number_to_select, len(scores[0, :])))
for i in range(number_to_select):
population_size = population_ids.shape[0]
fighter1ID = rn.randint(0, population_size - 1)
fighter2ID = rn.randint(0, population_size - 1)
# If fighter # 1 is better
if crowding_distances[fighter1ID] >= crowding_distances[
fighter2ID]:
# add solution to picked solutions array
picked_population_ids[i] = population_ids[
fighter1ID]
# Add score to picked scores array
picked_scores[i, :] = scores[fighter1ID, :]
# remove selected solution from available solutions
population_ids = np.delete(population_ids, (fighter1ID),
axis=0)
scores = np.delete(scores, (fighter1ID), axis=0)
crowding_distances = np.delete(crowding_distances, (fighter1ID),
axis=0)
else:
picked_population_ids[i] = population_ids[fighter2ID]
picked_scores[i, :] = scores[fighter2ID, :]
population_ids = np.delete(population_ids, (fighter2ID), axis=0)
scores = np.delete(scores, (fighter2ID), axis=0)
crowding_distances = np.delete(
crowding_distances, (fighter2ID), axis=0)
# Convert to integer
picked_population_ids = np.asarray(picked_population_ids, dtype=int)
return (picked_population_ids)
# Pareto selecion
# ---------------
def identify_pareto(scores, population_ids):
"""
Identifies a single Pareto front, and returns the population IDs of
the selected solutions.
"""
population_size = scores.shape[0]
# Create a starting list of items on the Pareto front
# All items start off as being labelled as on the Parteo front
pareto_front = np.ones(population_size, dtype=bool)
# Loop through each item. This will then be compared with all other items
for i in range(population_size):
# Loop through all other items
for j in range(population_size):
# Check if our 'i' pint is dominated by out 'j' point
if all(scores[j] >= scores[i]) and any(scores[j] > scores[i]):
# j dominates i. Label 'i' point as not on Pareto front
pareto_front[i] = 0
# Stop further comparisons with 'i' (no more comparisons needed)
break
# Return ids of scenarios on pareto front
return population_ids[pareto_front]
def build_pareto_population(
population, scores, minimum_population_size, maximum_population_size):
"""
As necessary repeats Pareto front selection to build a population within
defined size limits. Will reduce a Pareto front by applying crowding
selection as necessary.
"""
unselected_population_ids = np.arange(population.shape[0])
all_population_ids = np.arange(population.shape[0])
pareto_front = []
while len(pareto_front) < minimum_population_size:
temp_pareto_front = identify_pareto(
scores[unselected_population_ids, :], unselected_population_ids)
# Check size of total parteo front.
# If larger than maximum size reduce new pareto front by crowding
combined_pareto_size = len(pareto_front) + len(temp_pareto_front)
if combined_pareto_size > maximum_population_size:
number_to_select = combined_pareto_size - maximum_population_size
selected_individuals = (reduce_by_crowding(
scores[temp_pareto_front], number_to_select))
temp_pareto_front = temp_pareto_front[selected_individuals]
# Add latest pareto front to full Pareto front
pareto_front = np.hstack((pareto_front, temp_pareto_front))
# Update unslected population ID by using sets to find IDs in all
# ids that are not in the selected front
unselected_set = set(all_population_ids) - set(pareto_front)
unselected_population_ids = np.array(list(unselected_set))
population = population[pareto_front.astype(int)]
return population
# Population functions
# --------------------
def create_population(individuals, chromosome_length):
"""
Create random population with given number of individuals and chroosome
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 = rn.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 breed_by_crossover(parent_1, parent_2):
"""
Combine two parent chromsomes by crossover to produce two children.
"""
# Get length of chromosome
chromosome_length = len(parent_1)
# Pick crossover point, avoding ends of chromsome
crossover_point = rn.randint(1,chromosome_length-1)
# Create children. np.hstack joins two arrays
child_1 = np.hstack((parent_1[0:crossover_point],
parent_2[crossover_point:]))
child_2 = np.hstack((parent_2[0:crossover_point],
parent_1[crossover_point:]))
# Return children
return child_1, child_2
def randomly_mutate_population(population, mutation_probability):
"""
Randomly mutate population with a given individual gene mutation
probability. Individual gene may switch between 0/1.
"""
# Apply random mutation
random_mutation_array = np.random.random(size=(population.shape))
random_mutation_boolean = \
random_mutation_array <= mutation_probability
population[random_mutation_boolean] = \
np.logical_not(population[random_mutation_boolean])
# Return mutation population
return population
def breed_population(population):
"""
Create child population by repetedly calling breeding function (two parents
producing two children), applying genetic mutation to the child population,
combining parent and child population, and removing duplice chromosomes.
"""
# Create an empty list for new population
new_population = []
population_size = population.shape[0]
# Create new popualtion generating two children at a time
for i in range(int(population_size/2)):
parent_1 = population[rn.randint(0, population_size-1)]
parent_2 = population[rn.randint(0, population_size-1)]
child_1, child_2 = breed_by_crossover(parent_1, parent_2)
new_population.append(child_1)
new_population.append(child_2)
# Add the child population to the parent population
# In this method we allow parents and children to compete to be kept
population = np.vstack((population, np.array(new_population)))
population = np.unique(population, axis=0)
return population
# *************************************
# ******** MAIN ALGORITHM CODE ********
# *************************************
# Set general parameters
chromosome_length = 50
starting_population_size = 5000
maximum_generation = 250
minimum_population_size = 500
maximum_population_size = 1000
# Create two reference solutions
# (this is used just to illustrate GAs)
references = create_reference_solutions(chromosome_length, 2)
# Create starting population
population = create_population(
starting_population_size, chromosome_length)
#population = np.unique(population, axis=0)
# Now we'll go through the generations of genetic algorithm
for generation in range(maximum_generation):
if generation %10 ==0:
print ('Generation (out of %i): %i '%(maximum_generation, generation))
# Brred
population = breed_population(population)
# Score population
scores = score_population(population, references)
# Build pareto front
population = build_pareto_population(
population, scores, minimum_population_size, maximum_population_size)
# Get final pareto front
scores = score_population(population, references)
population_ids = np.arange(population.shape[0]).astype(int)
pareto_front = identify_pareto(scores, population_ids)
population = population[pareto_front, :]
scores = scores[pareto_front]
# Plot Pareto front (for two scores only)
x = scores[:, 0]/chromosome_length*100
y = scores[:, 1]/chromosome_length*100
plt.xlabel('Objective A - % maximum obtainable')
plt.ylabel('Objective B - % maximum obtainable')
plt.scatter(x,y)
plt.savefig('pareto.png')
plt.show()
```