diff --git a/genetic_algorithm/function_optimization.py b/genetic_algorithm/function_optimization.py
new file mode 100644
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+++ b/genetic_algorithm/function_optimization.py
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+"""
+Genetic algorithm for function optimization.
+
+Author: kpuyam
+"""
+
+import random
+from collections.abc import Callable
+from typing import Sequence
+
+# Define the parameters for the genetic algorithm
+N_POPULATION = 100
+N_SELECTED = 20
+MUTATION_PROBABILITY = 0.1
+NUM_GENERATIONS = 50
+
+
+def evaluate(func: Callable, params: list[float]) -> float:
+    """
+    Evaluate the fitness of an individual based on the provided function.
+
+    Example:
+    >>> evaluate(lambda x, y: x ** 2 + y ** 2, [3, 4])
+    25
+    >>> evaluate(lambda x, y: x + y, [3, 4])
+    7
+    """
+    return func(*params)
+
+
+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]:
+    """Mutate an individual with a certain probability."""
+    mutated_individual = []
+    for gene in individual:
+        if random.random() < mutation_rate:
+            # Add some noise to the gene's value
+            mutated_gene = gene + random.uniform(-0.1, 0.1)
+            mutated_individual.append(mutated_gene)
+        else:
+            mutated_individual.append(gene)
+    return mutated_individual
+
+
+def select(
+    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 = [
+        individual[0] for individual in sorted_population[:num_selected]
+    ]
+    return selected_parents
+
+
+def optimize(
+    func: Callable,
+    param_ranges: Sequence[tuple[float, float]],
+) -> tuple[Sequence[float], float]:
+    """Optimize the given function using a genetic algorithm."""
+    # Initialize the population
+    population = [
+        [random.uniform(param_range[0], param_range[1]) for param_range in param_ranges]
+        for _ in range(N_POPULATION)
+    ]
+
+    # Main optimization loop
+    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
+        ]
+
+        # Select parents for crossover
+        selected_parents = select(population_fitness, N_SELECTED)
+
+        # Generate offspring through crossover and mutation
+        offspring = []
+        for _i in range(N_POPULATION):
+            parent_1 = random.choice(selected_parents)
+            parent_2 = random.choice(selected_parents)
+            child = crossover(parent_1, parent_2)
+            child = mutate(child, MUTATION_PROBABILITY)
+            offspring.append(child)
+
+        # Replace the old population with the offspring
+        population = offspring
+
+    # Find the best individual in the final population
+    best_individual, best_fitness = max(population_fitness, key=lambda x: x[1])
+
+    return best_individual, best_fitness
+
+
+if __name__ == "__main__":
+    # Set random seed for reproducibility
+    random.seed(123)
+
+    # Example usage:
+    def quadratic_function(x, y):
+        """Example function to optimize."""
+        return x**2 + y**2
+
+    param_ranges = [(-10, 10), (-10, 10)]
+    best_params, best_fitness = optimize(quadratic_function, param_ranges)
+    print("Best parameters:", best_params)
+    print("Best fitness:", best_fitness)