Added an algorithm in how to use genetic algorithm for numbers

DIRECTORY.md

@@ -517,6 +517,7 @@
   * [Fuzzy Set Operations](fuzzy_logic/fuzzy_set_operations.py)
 
 ## [Genetic Algorithm](genetic_algorithm)
+  * [Basic Number](genetic_algorithm/basic_number.py)
   * [Basic String](genetic_algorithm/basic_string.py)
 
 ## [Geodesy](geodesy)
@@ -876,6 +877,7 @@
   * [Test Factorial](maths/test_factorial.py)
   * [Test Prime Check](maths/test_prime_check.py)
   * [Three Sum](maths/three_sum.py)
+  * [Tonelli Shanks](maths/tonelli_shanks.py)
   * [Trailing Zeroes](maths/trailing_zeroes.py)
   * [Trapezoidal Rule](maths/trapezoidal_rule.py)
   * [Triplet Sum](maths/triplet_sum.py)

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() -> tuple[float, float]:
+    """
+    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()