Genetic Algorithms for python

This library is a wrapper for genetic algorithms to leverage in optimisation problems. It hopes to make writing infinitely customizable genetic algorithms easy and quick while having all the standard features expected.

Installation

This module can be installed via pip:

pip install genetic-algorithms

Roadmap

• add mutation potency & frequency, extract population flow to user defined sequence.
• add multi population models
• add common crossover and mutation generic methods

Examples

This is a basic example for maximising values in a list, starting with 10 members running 100 generation. Then it will log to the screen and create a csv file with each generations information in short format.

import genetic_algorithms as ga
import random

class MyMember(ga.MemberBase):
    def _construct_from_params(self, construction_parameters=None):
        # Starting point is a bunch of 5 numbers [0-9]
        self.vars = [random.randrange(10) for _ in range(5)]

    def mutate(self):
        # Mutation involves adding a number between -2 and 2 to a random variable
        i = random.randrange(-2,3)
        j = random.randrange(len(self.vars))
        self.vars[j] += i

    def crossover(self, pairing):
        # Crossing over takes the first half from member one, and the second half from member 2
        new_params = self.vars[:len(self.vars) // 2] + pairing.vars[len(pairing.vars) // 2:]
        return MyMember(new_params)

    def score_self(self):
        return sum(self.vars)

def m_fit_func(member: MyMember):
    # This scores the member on its properties, but can involve any external functions as needed.
    # Remember to test this does what you want.
    return member.score_self()

pop = ga.Population(size=10, member_type=MyMember, member_parameters_generator=None,
                   fitness_function=m_fit_func, population_seed=0)
pop.run(100, print_logging=True, csv_path="example1.csv")

This next one is a little more complected; we want to identify a door of a particular size.

import genetic_algorithms as ga
import random
import numpy as np


class Door(ga.MemberBase):
    def _construct_from_params(self, construction_parameters=None):
        # Set the parameters as passed by the generator
        self.height = construction_parameters['height']
        self.width = construction_parameters['width']

    def mutate(self):
        # Mutate by adding 1 or -1 to our door height and width.
        # this is an example of a bad mutation function because it wouldn't hit an integer after the mutation
        i = random.randrange(2)
        j = random.randrange(2)
        self.height += pow(-1, i)
        self.width += pow(-1, j)

    def crossover(self, pairing):
        # Again a bad example of a crossover, the mean we will converge quickly but very hard to get a precice score.
        new_params = {'height': np.mean([self.height, pairing.height]),
                      'width': np.mean([self.width, pairing.width])}
        return Door(construction_parameters=new_params)

    def __repr__(self):
        return 'height: ' + str(self.height) + " | width: " + str(self.width)


def fit_through_door(member: Door):
    door_height = 10
    door_width = 3
    height_diff = abs(door_height - member.height)
    width_diff = abs(door_width - member.width)
    return height_diff + width_diff

def param_generator():
    max_h = 10
    max_w = 20
    yield {'height': random.randrange(max_h),
           'width': random.randrange(max_w)}

random.seed(1)
pop = ga.Population(100, Door, fit_through_door, member_parameters_generator=param_generator)
# run 500 generations before checking we have some parameters changed
pop.run(generations=500, print_logging=True, maximise_fitness_func=False)
print("Best door:", pop.get_top())
# returns what almost exactly what we want:
# Best door: (height: 9.999995902180672 | width: 3.999865485355258, 244033.23286180547)

Finally, we will boost it to a very complicated example, we want to generate a copy of a picture of a face from a randomly generated face.