# Painting the Starry Night with Genetic Algorithms

19 Mar 2021, Samuel Hinton

A gentle introduction to genetic algorithms by recreating painting classics?

This is what we’re going to make in this tutorial. It may not look like much, but then again, this is but the first step into genetic algorithms. If you’ve guessed that this is the Starry Night (or bothered to read the title, description or anything else), fantastic.

The idea behind Genetic Algorithms is simple - each algorithm can be evaluated to get its fitness, and algorithms are mutated, bred and culled, according to their performance. The broad steps of a genetic algorithm are as follows:

2. Determine the fitness of members of the population
3. Select the fitest members for survival
4. Breed those members (for example, using crossover, which we’ll cover next)
5. Mutate them for fun!
6. Continue from step 2

To get us started with genetic algorithms, we are going to forget for now about a population and breeding, instead we shall have a single organism and line of descent, mutating it away as it undergoes mitosis.

To begin, some necessary imports:

import os
import numpy as np
from numpy.random import choice, random, normal
from colour import Color
import pygame  # Pygame for nice fast drawing


For the first fun part, we want an organism. An organism is an entity with a chromosome, which is a collection of genes. These genes are what we mutate, and the genes express themselves in various ways. In our example, each gene expresses itself as a circle.

Each organism has the ability to mutate. That is, perform mitosis to generate a new organism that is a clone of itself, except with some genes changed in some way. In the implementation below, you can see that when we mutate, we give the option to add or remove a gene, or to change features of existing genes. Of course, we could do these both together, we just have to keep in mind that if we change too much at once, our new organism will probably be a bad fit and get culled immediately.

If you are familiar with MCMC optimisation methods like Metropolis-Hastings, this is us making sure our step size is small enough. If MCMC is something new, ignore this sentence!

class Organism:
def __init__(self, genes):
self.chromosome = np.clip(genes, 0, 1)
self.visual = None
self.fitness = None

""" Get a mutated organism given the mutation rates and scale input """
chromosome = np.copy(self.chromosome)
n_gene, n_feats = chromosome.shape

# Here we can add/remove a gene, or mutate an existing one
# Mutate features in our genes
num_mutations = 1 + int(mutation_rate * chromosome.size)
# As we mutate more, the size of mutations decreases
scale2 = scale / num_mutations
for i in range(num_mutations):
chromosome[choice(n_gene), choice(n_feats)] += normal() * scale2
else:
# Either add or remove a gene
if random() < 0.3:
chromosome = np.delete(chromosome, choice(n_gene), axis=0)
else:
# When we add, we'll do so by blending two existing genes
# and perturbing it. More likely to find a good gene this way.
a, b = choice(n_gene, 2, replace=False)
gene = np.atleast_2d(0.5 * (chromosome[a, :] + chromosome[b, :]))
gene += scale * normal(size=(1, gene.size))
gene[:, 2] *= 0.2
chromosome = np.append(chromosome, gene, axis=0)

return Organism(chromosome)



After this, we want something that keeps track of our organism, and mutates it, evaluates it, etc. We shall call this a Population, even though in our case we just looking at a single best_organism to start with.

This a fairly large code block, but it should be fairly straightforward in its logic. If you want the important part, the step function at the bottom is main component.

class Population:
def __init__(self, path):
""" Load in the reference image and create a surface we can draw on. """
pygame.init()
w, h, d = self.ref.shape
self.screen = pygame.Surface((w, h))
self.screen.fill((255, 255, 255))

self.best_organism = None

def draw(self, organism):
""" Draw an organism by expressing each gene in term """
w, h, d = self.ref.shape
screen = self.screen.copy()
for gene in organism.chromosome:
x, y, size, *hsl = gene
position = (int(x * w), int(y * h))
c = tuple(map(lambda x: int(255 * x),  Color(hsl=hsl).rgb))
pygame.draw.circle(screen, c, position, int((size * 0.3 + 0.01) * w))
return screen

def spawn(self, complexity=1):
""" Spawn a new individual with complexity genes to start with """
random_genes = random((complexity, 6))
self.best_organism = Organism(random_genes)
self.calc_fitness(self.best_organism)

def calc_fitness(self, organism):
""" Calculate the fitness of a gene by drawing it and comparing
it to the reference """
screen = self.draw(organism)
diff = pygame.surfarray.pixels3d(screen) - self.ref
organism.fitness = -np.mean(np.abs(diff)) - 1e-5 * organism.chromosome.size
organism.visual = screen

def step(self, time, outdir, rate=0.01, scale=0.1, add_chance=0.3):
""" Take a step in time by mutating the organism and seeing if its the
fittest out there! This will also save out a checkpoint file so we can resume
evolution, and save out images every 200 steps. """
self.calc_fitness(o)

if o.fitness > self.best_organism.fitness:
self.best_organism = o

# Save out the image if we want it
if time % 200 == 0:
path = outdir + f"{time // 200:04d}.png"
pygame.image.save(self.best_organism.visual, path)
np.save(outdir + "save.npy", self.best_organism.chromosome)



With this population, we can create a handy helper function which points to a reference image, sets an output directup up, and then either loads the checkpoint, or starts anew if its not found!

def evolve(rate, scale, add_chance, steps=1000000):
pop = Population("genetic1/starry_night.png")
outdir = f"genetic1/output/"
os.makedirs(outdir, exist_ok=True)
save = outdir + "save.npy"
if os.path.exists(save):
pop.calc_fitness(pop.best_organism)
start = int(sorted(os.listdir(outdir))[-2][:-4]) * 200
else:
pop.spawn(complexity=10)
start = 0
for i in range(start, steps):


With this all set up, we can now call evolve and specify the mutation rate, the scale of the mutations, and the chance that we add or remove a gene in any given mutation.

The above is our starting point, so lets see how far we can get using a slow rate of evolution:

# 1% chance of mutation, scale is a normal of std 0.2, and 1% chance to add or remove
evolve(0.01, 0.2, 0.01)


After this has run, we will have a lot of PNG files in the output directory. If we fire up good old ffmpeg to turn them into a video sequence, this is what we get:

Alright, this all looks interesting…

But…

It took thousands upon thousands of iterations to get us anything like what we want, and even at the end it could be markedly better.

Obviously, it is time to increase the sophistication of our algorithm. Onto part two! And for reference, here is our final frame:

Connect to stay in the loop for tutorials and posts.

### Samuel Hinton

Astrophysicist & Data Scientist

Here’s the full code for convenience:

from colour import Color
from numpy.random import choice, random, normal
import numpy as np
import os
import pygame  # Pygame for nice fast drawing

class Organism:
def __init__(self, genes):
self.chromosome = np.clip(genes, 0, 1)
self.visual = None
self.fitness = None

""" Get a mutated organism given the mutation rates and scale input """
chromosome = np.copy(self.chromosome)
n_gene, n_feats = chromosome.shape

# Here we can add/remove a gene, or mutate an existing one
# Mutate features in our genes
num_mutations = 1 + int(mutation_rate * chromosome.size)
# As we mutate more, the size of mutations decreases
scale2 = scale / num_mutations
for i in range(num_mutations):
chromosome[choice(n_gene), choice(n_feats)] += normal() * scale2
else:
# Either add or remove a gene
if random() < 0.3:
chromosome = np.delete(chromosome, choice(n_gene), axis=0)
else:
# When we add, we'll do so by blending two existing genes
# and perturbing it. More likely to find a good gene this way.
a, b = choice(n_gene, 2, replace=False)
gene = np.atleast_2d(0.5 * (chromosome[a, :] + chromosome[b, :]))
gene += scale * normal(size=(1, gene.size))
gene[:, 2] *= 0.2
chromosome = np.append(chromosome, gene, axis=0)

return Organism(chromosome)

class Population:
def __init__(self, path):
""" Load in the reference image and create a surface we can draw on. """
pygame.init()
w, h, d = self.ref.shape
self.screen = pygame.Surface((w, h))
self.screen.fill((255, 255, 255))

self.best_organism = None

def draw(self, organism):
""" Draw an organism by expressing each gene in term """
w, h, d = self.ref.shape
screen = self.screen.copy()
for gene in organism.chromosome:
x, y, size, *hsl = gene
position = (int(x * w), int(y * h))
c = tuple(map(lambda x: int(255 * x),  Color(hsl=hsl).rgb))
pygame.draw.circle(screen, c, position, int((size * 0.3 + 0.01) * w))
return screen

def spawn(self, complexity=1):
""" Spawn a new individual with complexity genes to start with """
random_genes = random((complexity, 6))
self.best_organism = Organism(random_genes)
self.calc_fitness(self.best_organism)

def calc_fitness(self, organism):
""" Calculate the fitness of a gene by drawing it and comparing
it to the reference """
screen = self.draw(organism)
diff = pygame.surfarray.pixels3d(screen) - self.ref
organism.fitness = -np.mean(np.abs(diff)) - 1e-5 * organism.chromosome.size
organism.visual = screen

def step(self, time, outdir, rate=0.01, scale=0.1, add_chance=0.3):
""" Take a step in time by mutating the organism and seeing if its the
fittest out there! This will also save out a checkpoint file so we can resume
evolution, and save out images every 200 steps. """
self.calc_fitness(o)

if o.fitness > self.best_organism.fitness:
self.best_organism = o

# Save out the image if we want it
if time % 200 == 0:
path = outdir + f"{time // 200:04d}.png"
pygame.image.save(self.best_organism.visual, path)
np.save(outdir + "save.npy", self.best_organism.chromosome)

pop = Population("genetic1/starry_night.png")
outdir = f"genetic1/output/"
os.makedirs(outdir, exist_ok=True)
save = outdir + "save.npy"
if os.path.exists(save):
pop.calc_fitness(pop.best_organism)
start = int(sorted(os.listdir(outdir))[-2][:-4]) * 200
else:
pop.spawn(complexity=10)
start = 0
for i in range(start, steps):

# 1% chance of mutation, scale is a normal of std 0.2, and 1% chance to add or remove
evolve(0.01, 0.2, 0.01)