Refactor functions in function_optimization.py

Author: KelvinPuyam

genetic_algorithm/function_optimization.py

@@ -5,7 +5,7 @@
 """
 
 import random
-from typing import Callable, Tuple, List
+from collections.abc import Callable
 
 # Define the parameters for the genetic algorithm
 N_POPULATION = 100
@@ -14,7 +14,7 @@
 NUM_GENERATIONS = 50
 
 
-def evaluate(func: Callable, params: List[float]) -> float:
+def evaluate(func: Callable, params: list[float]) -> float:
     """
     Evaluate the fitness of an individual based on the provided function.
 
@@ -27,14 +27,14 @@ def evaluate(func: Callable, params: List[float]) -> float:
     return func(*params)
 
 
-def crossover(parent_1: List[float], parent_2: List[float]) -> List[float]:
+def crossover(parent_1: list[float], parent_2: list[float]) -> list[float]:
     """Perform crossover between two parents."""
     crossover_point = random.randint(1, len(parent_1) - 1)
     child = parent_1[:crossover_point] + parent_2[crossover_point:]
     return child
 
 
-def mutate(individual: List[float], mutation_rate: float) -> List[float]:
+def mutate(individual: list[float], mutation_rate: float) -> list[float]:
     """Mutate an individual with a certain probability."""
     mutated_individual = []
     for gene in individual:
@@ -48,8 +48,8 @@ def mutate(individual: List[float], mutation_rate: float) -> List[float]:
 
 
 def select(
-    population: List[Tuple[List[float], float]], num_selected: int
-) -> List[List[float]]:
+    population: list[tuple[list[float], float]], num_selected: int
+) -> list[list[float]]:
     """Select individuals based on their fitness scores."""
     sorted_population = sorted(population, key=lambda x: x[1])
     selected_parents = [
@@ -60,10 +60,8 @@ def select(
 
 def optimize(
     func: Callable,
-    num_params: int,
-    param_ranges: List[Tuple[float, float]],
-    optimization_goal: str,
-) -> Tuple[List[float], float]:
+    param_ranges: list[tuple[float, float]],
+) -> tuple[list[float], float]:
     """Optimize the given function using a genetic algorithm."""
     # Initialize the population
     population = [
@@ -72,7 +70,7 @@ def optimize(
     ]
 
     # Main optimization loop
-    for generation in range(NUM_GENERATIONS):
+    for _generation in range(NUM_GENERATIONS):
         # Evaluate the fitness of each individual in the population
         population_fitness = [
             (individual, evaluate(func, individual)) for individual in population
@@ -83,7 +81,7 @@ def optimize(
 
         # Generate offspring through crossover and mutation
         offspring = []
-        for i in range(N_POPULATION):
+        for _i in range(N_POPULATION):
             parent_1 = random.choice(selected_parents)
             parent_2 = random.choice(selected_parents)
             child = crossover(parent_1, parent_2)
@@ -109,8 +107,6 @@ def quadratic_function(x, y):
         return x**2 + y**2
 
     param_ranges = [(-10, 10), (-10, 10)]
-    best_params, best_fitness = optimize(
-        quadratic_function, 2, param_ranges, "minimize"
-    )
+    best_params, best_fitness = optimize(quadratic_function, param_ranges)
     print("Best parameters:", best_params)
     print("Best fitness:", best_fitness)