genetic_algorithm.basic_string
==============================

.. py:module:: genetic_algorithm.basic_string

.. autoapi-nested-parse::

   Simple multithreaded algorithm to show how the 4 phases of a genetic algorithm works
   (Evaluation, Selection, Crossover and Mutation)
   https://en.wikipedia.org/wiki/Genetic_algorithm
   Author: D4rkia



Attributes
----------

.. autoapisummary::

   genetic_algorithm.basic_string.MUTATION_PROBABILITY
   genetic_algorithm.basic_string.N_POPULATION
   genetic_algorithm.basic_string.N_SELECTED
   genetic_algorithm.basic_string.target_str


Functions
---------

.. autoapisummary::

   genetic_algorithm.basic_string.basic
   genetic_algorithm.basic_string.crossover
   genetic_algorithm.basic_string.evaluate
   genetic_algorithm.basic_string.mutate
   genetic_algorithm.basic_string.select


Module Contents
---------------

.. py:function:: basic(target: str, genes: list[str], debug: bool = True) -> tuple[int, int, str]

   Verify that the target contains no genes besides the ones inside genes variable.

   >>> from string import ascii_lowercase
   >>> basic("doctest", ascii_lowercase, debug=False)[2]
   'doctest'
   >>> genes = list(ascii_lowercase)
   >>> genes.remove("e")
   >>> basic("test", genes)
   Traceback (most recent call last):
       ...
   ValueError: ['e'] is not in genes list, evolution cannot converge
   >>> genes.remove("s")
   >>> basic("test", genes)
   Traceback (most recent call last):
       ...
   ValueError: ['e', 's'] is not in genes list, evolution cannot converge
   >>> genes.remove("t")
   >>> basic("test", genes)
   Traceback (most recent call last):
       ...
   ValueError: ['e', 's', 't'] is not in genes list, evolution cannot converge


.. py:function:: crossover(parent_1: str, parent_2: str) -> tuple[str, str]

   Slice and combine two strings at a random point.
   >>> random.seed(42)
   >>> crossover("123456", "abcdef")
   ('12345f', 'abcde6')


.. py:function:: evaluate(item: str, main_target: str) -> tuple[str, float]

   Evaluate how similar the item is with the target by just
   counting each char in the right position
   >>> evaluate("Helxo Worlx", "Hello World")
   ('Helxo Worlx', 9.0)


.. py:function:: mutate(child: str, genes: list[str]) -> str

   Mutate a random gene of a child with another one from the list.
   >>> random.seed(123)
   >>> mutate("123456", list("ABCDEF"))
   '12345A'


.. py:function:: select(parent_1: tuple[str, float], population_score: list[tuple[str, float]], genes: list[str]) -> list[str]

   Select the second parent and generate new population

   >>> random.seed(42)
   >>> parent_1 = ("123456", 8.0)
   >>> population_score = [("abcdef", 4.0), ("ghijkl", 5.0), ("mnopqr", 7.0)]
   >>> genes = list("ABCDEF")
   >>> child_n = int(min(parent_1[1] + 1, 10))
   >>> population = []
   >>> for _ in range(child_n):
   ...     parent_2 = population_score[random.randrange(len(population_score))][0]
   ...     child_1, child_2 = crossover(parent_1[0], parent_2)
   ...     population.extend((mutate(child_1, genes), mutate(child_2, genes)))
   >>> len(population) == (int(parent_1[1]) + 1) * 2
   True


.. py:data:: MUTATION_PROBABILITY
   :value: 0.4


.. py:data:: N_POPULATION
   :value: 200


.. py:data:: N_SELECTED
   :value: 50


.. py:data:: target_str
   :value: 'This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'