diff --git a/genetic_algorithm.py b/genetic_algorithm.py
new file mode 100644
index 000000000000..884bdc9880f2
--- /dev/null
+++ b/genetic_algorithm.py
@@ -0,0 +1,64 @@
+import random
+
+# Define the target string (the goal of the genetic algorithm)
+target_string = "Hello, World!"
+
+# Function to generate a random string of the same length as the target
+def generate_random_string(length):
+    return ''.join(random.choice("ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz ,.!?") for _ in range(length))
+
+# Function to calculate the fitness of an individual (string)
+def calculate_fitness(individual):
+    return sum(1 for i, j in zip(individual, target_string) if i == j)
+
+# Function to perform selection (tournament selection)
+def selection(population):
+    tournament_size = 3
+    selected = [random.choice(population) for _ in range(tournament_size)]
+    return max(selected, key=calculate_fitness)
+
+# Function to perform crossover (single-point crossover)
+def crossover(parent1, parent2):
+    point = random.randint(1, len(parent1) - 1)
+    child = parent1[:point] + parent2[point:]
+    return child
+
+# Function to perform mutation (random character mutation)
+def mutation(individual, mutation_rate):
+    return ''.join(
+        random.choice("ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz ,.!?")
+        if random.random() < mutation_rate else char
+        for char in individual
+    )
+
+# Main genetic algorithm loop
+population_size = 100
+mutation_rate = 0.01
+
+population = [generate_random_string(len(target_string)) for _ in range(population_size)]
+
+generation = 1
+while True:
+    population = sorted(population, key=calculate_fitness, reverse=True)
+    best_individual = population[0]
+
+    if best_individual == target_string:
+        break
+
+    new_population = [best_individual]
+
+    while len(new_population) < population_size:
+        parent1 = selection(population)
+        parent2 = selection(population)
+        child = crossover(parent1, parent2)
+        child = mutation(child, mutation_rate)
+        new_population.append(child)
+
+    population = new_population
+    generation += 1
+
+    if generation % 10 == 0:
+        print(f"Generation {generation}: {best_individual} (Fitness: {calculate_fitness(best_individual)})")
+
+print(f"Generation {generation}: {best_individual} (Fitness: {calculate_fitness(best_individual)})")
+print("Genetic algorithm completed!")