natural-selection 0.2.21

Tools for running evolutionary algorithm experiments

Natural Selection

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by Zipfian Science                               

Python tools for creating and running Evolutionary Algorithm (EA) experiments by Zipfian Science.

• For documentation, see docs.
• Source on GitHub.
• For history, see changelog

Install

$ pip install natural-selection

And use

from natural_selection.genetic_algorithms import Gene, Chromosome, Individual, Island
from natural_selection.genetic_algorithms.utils.random_functions import random_int, random_gaussian

# Create a gene
g_1 = Gene(name="test_int", value=3, gene_max=10, gene_min=1, randomise_function=random_int)
g_2 = Gene(name="test_real", value=0.5, gene_max=1.0, gene_min=0.1, randomise_function=random_gaussian)

# Add a list of genes to a genome
gen = Chromosome([g_1, g_2])

# Next, create an individual to carry these genes and evaluate them
fitness_function = lambda island, individual, x, y: individual.chromosome[0].value * x + individual.chromosome[0].value * y
adam = Individual(fitness_function, name="Adam", chromosome=gen)

# Now we can create an island for running the evolutionary process
# Notice the fitness function parameters are given here.
params = dict()
params['x'] = 0.5
params['y'] = 0.2
isolated_island = Island(function_params=params)

# Using a single individual, we can create a new population
isolated_island.initialise(adam, population_size=5)

# And finally, we let the randomness of life do its thing: optimise
best_individual = isolated_island.evolve(n_generations=5)

# After running for a few generations, we have an individual with the highest fitness
fitness = best_individual.fitness
genes = best_individual.chromosome

for gene in genes:
  print(gene.name, gene.value)

Release

• Date: 2022-03-13
• Version: 0.2.21