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A flexible genetic algorithm library written in Python3.

Project description


A flexible genetic algorithm library written purly in Python3.


For python3, simply run:

$ pip3 install OptivolutionPy

Or clone this repository and run python3 install from within the project directory. e.g.:

$ git clone
$ cd OptivolutionPy
$ python3 install

Advanced Example

Smart Ants using OptivolutionPy & Processing3. check this repo for more details.


Simple example

Solving the one-dimensional knapsack problem:

#!/usr/bin/env python3

import random

from optivolution.population import Population
from optivolution.chromosome import Chromosome

class OneDimensinalKnapsack(Chromosome):
    """ Inidividual knapsack object. """
    maximum_weight = 15
    knapsack_data = [{'name': 'box1', 'value': 4, 'weight': 12},
                     {'name': 'box2', 'value': 2, 'weight': 1},
                     {'name': 'box3', 'value': 10, 'weight': 4},
                     {'name': 'box4', 'value': 1, 'weight': 1},
                     {'name': 'box5', 'value': 2, 'weight': 2}]

    def __init__(self, genes_length=len(knapsack_data), genes=[]):
        super().__init__(genes_length, genes)

    def fitness(self):        
        """ Defining the fitness function. """
        # Use the knapsack value as the fitness value
        total_value = 0
        total_weight = 0

        for i in range(self.genes_length):
            if (self.genes[i] == True):
                total_value += self.knapsack_data[i]['value']
                total_weight += self.knapsack_data[i]['weight']

        if total_weight > self.maximum_weight:
            total_value = 0

        return total_value

    def random_gene(self):
        """ Defining the random gene. """
        return random.choice((0, 1))

class KnapscakPopulation(Population):
    tournament_sample_percentage = 10

    def random_individual(self):
        """ Defining the random individual in the population. """
        return OneDimensinalKnapsack()

def main():
    population = KnapscakPopulation(population_size=20)

    print(f"Generation {population.generation_number}")
    best = population.get_best_individual()

    # The optimal answer for this test case is
    # (15, [0, 1, 1, 1, 1])
    print((, best.genes))

if __name__ == "__main__":


(15, [0, 1, 1, 1, 1])


Mohamed Hisham – G-Mail | GitHub | LinkedIn

This project is licensed under the GNU GPLv3 License - check LICENSE for more details.

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Files for OptivolutionPy, version 1.0.1
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