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"""
Simple multithreaded algorithm to show how the 4 phases of a genetic algorithm works
(Evaluation, Selection, Crossover and Mutation)
https://en.wikipedia.org/wiki/Genetic_algorithm
Author: D4rkia
"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
N_POPULATION = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
N_SELECTED = 50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
MUTATION_PROBABILITY = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))
def evaluate(item: str, main_target: str) -> tuple[str, float]:
"""
Evaluate how similar the item is with the target by just
counting each char in the right position
>>> evaluate("Helxo Worlx", "Hello World")
('Helxo Worlx', 9.0)
"""
score = len([g for position, g in enumerate(item) if g == main_target[position]])
return (item, float(score))
def crossover(parent_1: str, parent_2: str) -> tuple[str, str]:
"""
Slice and combine two strings at a random point.
>>> random.seed(42)
>>> crossover("123456", "abcdef")
('12345f', 'abcde6')
"""
random_slice = random.randint(0, len(parent_1) - 1)
child_1 = parent_1[:random_slice] + parent_2[random_slice:]
child_2 = parent_2[:random_slice] + parent_1[random_slice:]
return (child_1, child_2)
def mutate(child: str, genes: list[str]) -> str:
"""
Mutate a random gene of a child with another one from the list.
>>> random.seed(123)
>>> mutate("123456", list("ABCDEF"))
'12345A'
"""
child_list = list(child)
if random.uniform(0, 1) < MUTATION_PROBABILITY:
child_list[random.randint(0, len(child)) - 1] = random.choice(genes)
return "".join(child_list)
# Select, crossover and mutate a new population.
def select(
parent_1: tuple[str, float],
population_score: list[tuple[str, float]],
genes: list[str],
) -> list[str]:
"""
Select the second parent and generate new population
>>> random.seed(42)
>>> parent_1 = ("123456", 8.0)
>>> population_score = [("abcdef", 4.0), ("ghijkl", 5.0), ("mnopqr", 7.0)]
>>> genes = list("ABCDEF")
>>> child_n = int(min(parent_1[1] + 1, 10))
>>> population = []
>>> for _ in range(child_n):
... parent_2 = population_score[random.randrange(len(population_score))][0]
... child_1, child_2 = crossover(parent_1[0], parent_2)
... population.extend((mutate(child_1, genes), mutate(child_2, genes)))
>>> len(population) == (int(parent_1[1]) + 1) * 2
True
"""
pop = []
# Generate more children proportionally to the fitness score.
child_n = int(parent_1[1] * 100) + 1
child_n = 10 if child_n >= 10 else child_n
for _ in range(child_n):
parent_2 = population_score[random.randint(0, N_SELECTED)][0]
child_1, child_2 = crossover(parent_1[0], parent_2)
# Append new string to the population list.
pop.append(mutate(child_1, genes))
pop.append(mutate(child_2, genes))
return pop
def basic(target: str, genes: list[str], debug: bool = True) -> tuple[int, int, str]:
"""
Verify that the target contains no genes besides the ones inside genes variable.
>>> from string import ascii_lowercase
>>> basic("doctest", ascii_lowercase, debug=False)[2]
'doctest'
>>> genes = list(ascii_lowercase)
>>> genes.remove("e")
>>> basic("test", genes)
Traceback (most recent call last):
...
ValueError: ['e'] is not in genes list, evolution cannot converge
>>> genes.remove("s")
>>> basic("test", genes)
Traceback (most recent call last):
...
ValueError: ['e', 's'] is not in genes list, evolution cannot converge
>>> genes.remove("t")
>>> basic("test", genes)
Traceback (most recent call last):
...
ValueError: ['e', 's', 't'] is not in genes list, evolution cannot converge
"""
# Verify if N_POPULATION is bigger than N_SELECTED
if N_POPULATION < N_SELECTED:
msg = f"{N_POPULATION} must be bigger than {N_SELECTED}"
raise ValueError(msg)
# Verify that the target contains no genes besides the ones inside genes variable.
not_in_genes_list = sorted({c for c in target if c not in genes})
if not_in_genes_list:
msg = f"{not_in_genes_list} is not in genes list, evolution cannot converge"
raise ValueError(msg)
# Generate random starting population.
population = []
for _ in range(N_POPULATION):
population.append("".join([random.choice(genes) for i in range(len(target))]))
# Just some logs to know what the algorithms is doing.
generation, total_population = 0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(population)
# Random population created. Now it's time to evaluate.
# (Option 1) Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item, target) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# (Option 2) We just need to call evaluate for every item inside the population.
population_score = [evaluate(item, target) for item in population]
# Check if there is a matching evolution.
population_score = sorted(population_score, key=lambda x: x[1], reverse=True)
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 10 == 0:
print(
f"\nGeneration: {generation}"
f"\nTotal Population:{total_population}"
f"\nBest score: {population_score[0][1]}"
f"\nBest string: {population_score[0][0]}"
)
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
population_best = population[: int(N_POPULATION / 3)]
population.clear()
population.extend(population_best)
# Normalize population score to be between 0 and 1.
population_score = [
(item, score / len(target)) for item, score in population_score
]
# This is selection
for i in range(N_SELECTED):
population.extend(select(population_score[int(i)], population_score, genes))
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(population) > N_POPULATION:
break
if __name__ == "__main__":
target_str = (
"This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
)
genes_list = list(
" ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
"nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
)
generation, population, target = basic(target_str, genes_list)
print(
f"\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"
)
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