evolutionary_example.py

import random
from evol import Population, Evolution

CHROMO_LENGTH = 30
CHROMO_PARTITION = 10
TARGET_VALUE = 52

POPULATION_SIZE = 100

def random_chromosome(size):
    return ''.join(random.choices(['0', '1'], k=size))

def decode_chromosome(chromosome, size):

    def bit2int(binary):
        return int(binary, 2)

    sequence = [chromosome[i:i+size] for i in range(0,len(chromosome), size)]

    return tuple(map(bit2int,sequence))

def function(x,y,w):
    return (x**2 + 2*y + w)

def eval_chromosome(chromosome):

    xyz = decode_chromosome(chromosome, CHROMO_PARTITION)

    value = function(*xyz)

    return abs(TARGET_VALUE - value)

def crossing_chromosome(chromo1, chromo2):

    idxs = random.choices(range(0, len(chromo1)), k=2)
    idx1, idx2 = min(idxs), max(idxs)

    new_one = chromo1[:idx1] + chromo2[idx1:idx2] + chromo1[idx2:]
    # newTwo = chromo2[:idx1] + chromo1[idx1:idx2] + chromo2[idx2:]

    # if (new_one == chromo1):
    #     print(f"Crossing >> {new_one}")
    # elif (new_one == chromo2):
    #     print(f"Crossing :::::::::::: {new_one}")

    return new_one

def mutation(chromosome, probability=0.8):

    flip_bit = lambda bit : '1' if bit == '0' else '0'

    genes = list(chromosome)

    # print(probability)

    for i in range(0,len(chromosome)):
        if random.random() < probability:
            genes[i] = flip_bit(genes[i])

    return ''.join(genes)

def select_by_wheel(population : Population):

    weights = population._individual_weights

    selected = random.choices(population, weights=weights, k=len(population))

    return selected

def normalize_by_windowing(population):
    value = max([chromosome.score for chromosome in population]) + 1

    for chromosome in population:
        chromosome.fitness = abs(value - chromosome.score)

    return population

def pick_random(population):
    return tuple(random.choices(population, k=2))

def custom_logger(population):

    best = min(p.fitness for p in population)
    worst = max(p.fitness for p in population)

    length = len(population)
    gen = population.generation

    print(f"Best: {best} | Worst: {worst} | Length {length} | Generation: {gen}")

    return population

if __name__ == "__main__":
    pop = (Population(chromosomes=[random_chromosome(CHROMO_LENGTH) for _ in range(POPULATION_SIZE)],
                     eval_function=eval_chromosome,
                     maximize=False).evaluate())

    best = min(p.fitness for p in pop)
    print("Best fitness before evolutionary ", best)

    evo = (Evolution()
           .survive(n=30)
           .breed(parent_picker=pick_random, combiner=crossing_chromosome)
           .mutate(mutation, probability=0.5)
           .evaluate()
           )

    result = pop.evolve(evo, n=2000)

    best = min(p.fitness for p in result.evaluate())
    print("Best fitness after ::", best)