evolving-simple-organisms/evolve.py at master · koval77/evolving-simple-organisms · GitHub

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import operator

from numpy import floor

from numpy.random import randint
from numpy.random import random
from numpy.random import choice
from numpy.random import uniform

from organism import Organism


def evolve(settings, organisms_old, gen):

    elitism_num = int(floor(settings['elitism'] * settings['pop_size']))
    new_orgs = settings['pop_size'] - elitism_num

    # --- GET STATS FROM CURRENT GENERATION ----------------+
    # stats = defaultdict(int)
    stats = {'BEST': -1000, 'WORST': 1000, 'SUM': 0, 'COUNT': 0}
    for org in organisms_old:
        if org.fitness > stats['BEST']:
            stats['BEST'] = org.fitness

        if org.fitness < stats['WORST']:
            stats['WORST'] = org.fitness

        stats['SUM'] += org.fitness
        stats['COUNT'] += 1

    stats['AVG'] = stats['SUM'] / stats['COUNT']

    # --- ELITISM (KEEP BEST PERFORMING ORGANISMS) ---------+
    orgs_sorted = sorted(organisms_old, key=operator.attrgetter('fitness'), reverse=True)
    organisms_new = []
    for i in range(0, elitism_num):
        organisms_new.append(Organism(settings, wih=orgs_sorted[i].wih, who=orgs_sorted[i].who, name=orgs_sorted[i].name))

    # --- GENERATE NEW ORGANISMS ---------------------------+
    for w in range(0, new_orgs):

        # SELECTION (TRUNCATION SELECTION)
        canidates = range(0, elitism_num)
        # canidates = range(0, settings['pop_size']-1)
        random_index = choice(a=canidates, size=2, replace=False)
        org_1 = orgs_sorted[random_index[0]]
        org_2 = orgs_sorted[random_index[1]]

        # CROSSOVER
        crossover_weight = random()
        wih_new = (crossover_weight * org_1.wih) + ((1 - crossover_weight) * org_2.wih)
        who_new = (crossover_weight * org_1.who) + ((1 - crossover_weight) * org_2.who)

        # MUTATION
        mutate = random()
        if mutate <= settings['mutate']:

            # PICK WHICH WEIGHT MATRIX TO MUTATE
            mat_pick = randint(0, 2)

            # MUTATE: WIH WEIGHTS
            if mat_pick == 0:
                index_row = randint(0, settings['hnodes'])
                index_col = randint(0, settings['inodes'])
                wih_new[index_row][index_col] = wih_new[index_row][index_col] * uniform(0.9, 1.1)

            # MUTATE: WHO WEIGHTS
            if mat_pick == 1:
                index_row = randint(0, settings['onodes'])
                index_col = randint(0, settings['hnodes'])
                who_new[index_row][index_col] = who_new[index_row][index_col] * uniform(0.9, 1.1)

        organisms_new.append(Organism(settings, wih=wih_new, who=who_new, name='gen['+str(gen)+']-org['+str(w)+']'))

    return organisms_new, stats