geneticpython 0.0.2

A simple and friendly Python framework for genetic-based algorithms

geneticpython

A simple and friendly Python framework for genetic-based algorithms (strongly supports tree-encoding)

• Supported algorithms: Genetic Algorithm (GAEngine), NSGA-ii (NSGAIIEngine).
• An example on ZDT1 problem:

Installation

This package requires python 3.6 or later.

pip install geneticpython

Getting started

We can quickly design a genetic algorithm in the following steps:

1. define a template individual with specific encoding

from geneticpython.models import BinaryIndividual
indv_temp = BinaryIndividual(length=100)

2. define population based on this population can uniformly initialize a population or you can define your own by passing init_population argument function

from geneticpython import Population
population = Population(indv_temp, pop_size=100)

3. define some core operators in genetic algorithm

from geneticpython.core.operators import RouletteWheelSelection, UniformCrossover, \
                                        FlipBitMutation, RouletteWheelReplacement
selection = RouletteWheelSelection()
crossover = UniformCrossover(pc=0.8, pe=0.5)
mutation = FlipBitMutation(pm=0.1)
# this function decides which individuals will be survived
replacement = RouletteWheelReplacement()

4. create an engine and register the defined population and operators

from geneticpython import GAEngine
engine = GAEngine(population, selection=selection,
                  selection_size=100,
                  crossover=crossover,
                  mutation=mutation,
                  replacement=replacement)

5. register fitness function which gets an individual and returns its fitness value

@engine.maximize_objective
def fitness(indv):
    return fitness_of_indv

6. run engine

engine.create_seed(seed)
history = engine.run(generations=1000)

7. get results and plot history

ans = engine.get_best_indv()
print(ans)
plot_single_objective_history({'geneticpython': history})

You can find more examples here

TODO

• Create extensive documentation and docs and comments in source-code
• Implement other algorithms: PSO, DE, MOED/A, MOPSO, MODE,...
• Implement other operators: PMX crossover, ...

Issues and Contribution

This project is in development, if you find any issues, please create an issue here.

If you are interested in contributing this project, feel free to create pull request here. We appreciate any contributions from you.

Acknowledgements

Special thanks to https://github.com/PytLab/gaft for getting me started a great API design.

This repository includes adaptions of the following repositories as baselines:

• https://github.com/msu-coinlab/pymoo
• https://github.com/tensorflow/tensorflow