diff --git a/DIRECTORY.md b/DIRECTORY.md
index 34967082b359..b1adc23f6e61 100644
--- a/DIRECTORY.md
+++ b/DIRECTORY.md
@@ -717,6 +717,7 @@
* [Archimedes Principle](physics/archimedes_principle.py)
* [Casimir Effect](physics/casimir_effect.py)
* [Centripetal Force](physics/centripetal_force.py)
+ * [Grahams Law](physics/grahams_law.py)
* [Horizontal Projectile Motion](physics/horizontal_projectile_motion.py)
* [Hubble Parameter](physics/hubble_parameter.py)
* [Ideal Gas Law](physics/ideal_gas_law.py)
diff --git a/genetic_algorithm/basic_string.py b/genetic_algorithm/basic_string.py
index 45b8be651f6e..388e7219f54b 100644
--- a/genetic_algorithm/basic_string.py
+++ b/genetic_algorithm/basic_string.py
@@ -21,6 +21,54 @@
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 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, genes: list[str]) -> str:
+ """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)
+
+
+# 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"""
+ 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.
@@ -70,17 +118,6 @@ def basic(target: str, genes: list[str], debug: bool = True) -> tuple[int, int,
total_population += len(population)
# 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
- counting each char in the right position
- >>> evaluate("Helxo Worlx", Hello World)
- ["Helxo Worlx", 9]
- """
- score = len(
- [g for position, g in enumerate(item) if g == main_target[position]]
- )
- return (item, float(score))
# Adding a bit of concurrency can make everything faster,
#
@@ -94,7 +131,7 @@ def evaluate(item: str, main_target: str = target) -> tuple[str, float]:
#
# 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]
+ 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)
@@ -121,41 +158,9 @@ def evaluate(item: str, main_target: str = target) -> tuple[str, float]:
(item, score / len(target)) for item, score in population_score
]
- # 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 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[ # noqa: B023
- 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))
- pop.append(mutate(child_2))
- return pop
-
- def crossover(parent_1: str, parent_2: str) -> tuple[str, str]:
- """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."""
- 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
for i in range(N_SELECTED):
- population.extend(select(population_score[int(i)]))
+ 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