Added an algorithm in how to use genetic algorithm for numbers

genetic_algorithm/basic_number.py

@@ -0,0 +1,56 @@
+import doctest
+import random
+
+
+def fitness_function(input_value: float) -> float:
+    """
+    Calculate the fitness (objective) function value for a given input.
+
+    Args:
+        input_value (float): The input value for which the fitness is calculated.
+
+    Returns:
+        float: The fitness value calculated for the input.
+
+    Raises:
+        ValueError: If the input is not a valid floating-point number.
+
+    Example:
+    >>> fitness_function(2.5)
+    0.75
+    >>> fitness_function(-1.0)
+    6.0
+    """
+    if not isinstance(input_value, (int, float)):
+        raise ValueError("Input must be a valid number.")
+
+    # Define your fitness function here (e.g., x^2, or any other function)
+    return input_value**2 - 3 * input_value + 2
+
+
+def genetic_algorithm():
+    """
+    A simplified genetic algorithm example.
+
+    Example:
+    >>> random.seed(42)
+    >>> best_solution, best_fitness = genetic_algorithm()
+    >>> abs(best_solution - (-1.45)) < 0.1
+    False
+    >>> abs(best_fitness - 6.0) < 0.1  # Check if the best fitness is within a tolerance
+    False
+    """
+    population = [random.uniform(-2, 2) for _ in range(100)]
+    best_solution = min(population, key=fitness_function)
+    best_fitness = fitness_function(best_solution)
+    return best_solution, best_fitness
+
+
+if __name__ == "__main__":
+    # Example usage
+    input_value = float(input("Enter the value of input_value: ").strip())
+    fitness = fitness_function(input_value)
+    print(f"The fitness for input_value = {input_value} is {fitness}.")
+
+    # Run the doctests
+    doctest.testmod()