godatadriven/evol

import random
from evol import Population, Evolution

random.seed(42)

def random_start():
    """
    This function generates a random (x,y) coordinate
    """
    return (random.random() - 0.5) * 20, (random.random() - 0.5) * 20

def func_to_optimise(xy):
    """
    This is the function we want to optimise (maximize)
    """
    x, y = xy
    return -(1-x)**2 - 2*(2-x**2)**2

def pick_random_parents(pop):
    """
    This is how we are going to select parents from the population
    """
    mom = random.choice(pop)
    dad = random.choice(pop)
    return mom, dad

def make_child(mom, dad):
    """
    This function describes how two candidates combine into a new candidate
    Note that the output is a tuple, just like the output of `random_start`
    We leave it to the developer to ensure that chromosomes are of the same type
    """
    child_x = (mom[0] + dad[0])/2
    child_y = (mom[1] + dad[1])/2
    return child_x, child_y

def add_noise(chromosome, sigma):
    """
    This is a function that will add some noise to the chromosome.
    """
    new_x = chromosome[0] + (random.random()-0.5) * sigma
    new_y = chromosome[1] + (random.random()-0.5) * sigma
    return new_x, new_y

# We start by defining a population with candidates.
pop = Population(chromosomes=[random_start() for _ in range(200)],
                 eval_function=func_to_optimise, maximize=True)

# We define a sequence of steps to change these candidates
evo1 = (Evolution()
       .survive(fraction=0.5)
       .breed(parent_picker=pick_random_parents, combiner=make_child)
       .mutate(func=add_noise, sigma=1))

# We define another sequence of steps to change these candidates
evo2 = (Evolution()
       .survive(n=1)
       .breed(parent_picker=pick_random_parents, combiner=make_child)
       .mutate(func=add_noise, sigma=0.2))

# We are combining two evolutions into a third one. You don't have to
# but this approach demonstrates the flexibility of the library.
evo3 = (Evolution()
       .repeat(evo1, n=50)
       .repeat(evo2, n=10)
       .evaluate())

# In this step we are telling evol to apply the evolutions
# to the population of candidates.
pop = pop.evolve(evo3, n=5)
print(f"the best score found: {max([i.fitness for i in pop])}")