Python/genetic_algorithm/basic_string.py at master · deepshekhardas/Python · GitHub

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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}"

)

Python/genetic_algorithm/basic_string.py at master · deepshekhardas/Python · GitHub

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