Genetic Algorithm

This is a pure Python implementation for a genetic algorithm. It has precreated classes and functions for using this algorithm.

The genetic module is compossed of a Individual and GeneticAlgorithm class.

from genetic_algorithm.genetic import GeneticAlgorithm, Individual
from genetic_algorithm.chooser import DefaultChooser
from genetic_algorithm.rules import single_crossover, SimpleMutator

def get_pop() -> list[Individual]:
    ...

def fitness(indivdual: Individual) -> float: # or int
    ...

ga = GeneticAlgorithm(get_pop(), DefaultChooser(), single_crossover, SimpleMutator(), tracker=..., n_keep=..., n_mutated_keeped=...)
ga.train(iterations=10, fitness)
# or use ga.evololution(fitness, do_sort) where fitness is a list of tuples of a number representing a score and an Individual
# if the list isn't sorted already, pass True for do_sort 

When creating your population, you should give the Individuals a list of values, a keras.Model, a keras.Sequential or a NN object, as well as a id.
The NN object can be created with the simple_nn modul. You will need tenserflow for for keras.Model and keras.Sequential.