inspiaaa / evo

Lightweight Tool for Genetic Algorithms in Python

Evo

A lightweight library for creating genetic algorithms with ease in Python.

• 100% pure python

• Lightweight: No external dependencies / packages

• Quick start template for multi-parameter optimisation problems

Getting started

1. Copy the evo2.py file into your own project

2. Import the file and create a class which will represent an individual of the population

from evo2 import Individual, Evolution, Selection
import random


class Optimisation (Individual):
   # __slots__ makes the individual class use less memory
   __slots__ = ("x")

   def __init__(self):
       super().__init__()
       self.x = 0

   # You can pass in your own data for initialisation
   # Although a dictionary is handy for that, any data type can be used
   def create(self, init_params):
       # Here you randomly initialise the individual
       self.x = random.uniform(init_params["lower"], init_params["upper"])

   # Mutate is used for introducing some randomness after pairing
   def mutate(self, mutate_params):
       self.x += random.random() * mutate_params["intensity"]
       # Clamp the x value into the desired range, if it goes over
       self.x = min(mutate_params["upper"], min(mutate_params["lower"], self.x))

   # Create a new offspring (Also known as the crossover operator)
   def pair(self, other, pair_params):
       offspring = Optimisation()
       offspring.x = (self.x + other.x) / 2

3. Create a fitness function

def curve(x):
    return -x*(x-1)*(x-2)*(x-3)*(x-4)

4. Initialise a population

evo = Evolution(
    Optimisation,
    size=20,
    n_offsprings=10,
    selection_method=Selection.tournament
    init_params={"lower": 0, "upper": 4},
    mutate_params={"lower": 0, "upper": 4},
    fitness_func=lambda obj: curve(obj.x))

5. Run the algorithm

# Run 100 generations
evo.evolve(100)

6. Get the best individual

best = evo.get_best_n(1)[0]
best_fitness = best.fitness

Other features

• Computing the diversity: Evo uses the standard deviation of the fitness values to describe the diversity of the population

diversity = evo.population.compute_diversity()

• Stopping after a certain number of generations without improvement

while True:
    evo.evolve()

    # Stop after 50 generations of no improvement
    if evo.stall_gens > 50:
        break

• Preserving diversity by using "social disasters"

from evo2 import SocialDisasters

for i in range(100):
    diversity = evo.population.compute_diversity()

    if diversity < 50:
        # Randomly re-initialise individuals that are too similar
        SocialDisasters.packing(evo.population, 10)

        # OR only keep the best individual and randomly re-initialise all others
        SocialDisasters.judgement_day(evo.population)

• Getting the generation number

  print(f"Generation #{ evo.gen_number }")

Using the optimisation template

The Evo library has a builtin template for multi parameter optimisation problems

1. Copy the evo_templates.py file in to your workspace (as well as the main Evo file itself)

2. Define the problem

   # 3 parameter function, taking 3 floats
   def cost(a, b, c):
       return (a+1)*(b+2)*(c+3)*(a-b-c)*(c-b-a)

3. Let the library optimise for you

   from evo_templates import maximise_multi_param
   
   a, b, c = maximise_multi_param(cost, lower_bounds=[-2, -2, -2], upper_bounds=[2, 2, 2]))
   print(a, b, c)

Different selection methods

The selection method tells Evo, which individuals to pair

• Selection.random Randomly chooses individuals

• Selection.fittest Chooses the best individuals

• Selection.roullette_wheel Gives the best individuals a better chance of being chosen

• Selection.tournament Takes two random individuals and chooses the better one