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Basic string grammar fix (#7534)

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* Grammar edit

* Flake8 consistency fix

* Apply suggestions from code review

Co-authored-by: Christian Clauss <cclauss@me.com>
This commit is contained in:
Abhishek Chakraborty 2022-10-23 03:42:02 -07:00 committed by GitHub
parent ed127032b3
commit f32f78a9e0
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1 changed files with 27 additions and 27 deletions
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54
genetic_algorithm/basic_string.py
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@ -9,15 +9,15 @@ from __future__ import annotations
import random
# Maximum size of the population. bigger could be faster but is more memory expensive
# Maximum size of the population. Bigger could be faster but is more memory expensive.
N_POPULATION = 200
# Number of elements selected in every generation for evolution the selection takes
# place from the best to the worst of that generation must be smaller than N_POPULATION
# 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
# guarantees that all genes will be used during evolution
# 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
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))
@ -56,20 +56,20 @@ def basic(target: str, genes: list[str], debug: bool = True) -> tuple[int, int,
f"{not_in_genes_list} is not in genes list, evolution cannot converge"
)
# Generate random starting population
# 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
# Just some logs to know what the algorithms is doing.
generation, total_population = 0, 0
# This loop will end when we will find a perfect match for our target
# 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
# Random population created. Now it's time to evaluate.
def evaluate(item: str, main_target: str = target) -> tuple[str, float]:
"""
Evaluate how similar the item is with the target by just
@ -92,17 +92,17 @@ def basic(target: str, genes: list[str], debug: bool = True) -> tuple[int, int,
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this will probably be slower
# we just need to call evaluate for every item inside population
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
population_score = [evaluate(item) for item in population]
# Check if there is a matching evolution
# 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
# 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}"
@ -111,21 +111,21 @@ def basic(target: str, genes: list[str], debug: bool = True) -> tuple[int, int,
f"\nBest string: {population_score[0][0]}"
)
# Flush the old population keeping some of the best evolutions
# Keeping this avoid regression of evolution
# 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 from 0 to 1
# Normalize population score to be between 0 and 1.
population_score = [
(item, score / len(target)) for item, score in population_score
]
# Select, Crossover and Mutate a new population
# Select, crossover and mutate a new population.
def select(parent_1: tuple[str, float]) -> list[str]:
"""Select the second parent and generate new population"""
pop = []
# Generate more child proportionally to the fitness score
# 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):
@ -134,32 +134,32 @@ def basic(target: str, genes: list[str], debug: bool = True) -> tuple[int, int,
][0]
child_1, child_2 = crossover(parent_1[0], parent_2)
# Append new string to the population list
# Append new string to the population list.
pop.append(mutate(child_1))
pop.append(mutate(child_2))
return pop
def crossover(parent_1: str, parent_2: str) -> tuple[str, str]:
"""Slice and combine two string in a random point"""
"""Slice and combine two string at a random point."""
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) -> str:
"""Mutate a random gene of a child with another one from the list"""
"""Mutate a random gene of a child with another one from the list."""
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)
# This is Selection
# This is selection
for i in range(N_SELECTED):
population.extend(select(population_score[int(i)]))
# 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 string in
# a lot fewer generations
# 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