PyGAD: A Python Library for Building the Genetic Algorithm and Training Machine Learning Algoithms (Keras & PyTorch).
PyGAD is an open-source easy-to-use Python 3 library for building the genetic algorithm and optimizing machine learning algorithms. It supports Keras and PyTorch. PyGAD supports optimizing both single-objective and multi-objective problems.
Check documentation of the PyGAD.
PyGAD supports different types of crossover, mutation, and parent selection. PyGAD allows different types of problems to be optimized using the genetic algorithm by customizing the fitness function.
The library is under active development and more features are added regularly. If you want a feature to be supported, please check the Contact Us section to send a request.
To install PyGAD, simply use pip to download and install the library from PyPI (Python Package Index). The library is at PyPI at this page https://pypi.org/project/pygad.
Install PyGAD with the following command:
pip install pygad
To get started with PyGAD, please read the documentation at Read The Docs https://pygad.readthedocs.io.
The source code of the PyGAD' modules is found in the following GitHub projects:
The documentation of PyGAD is available at Read The Docs https://pygad.readthedocs.io.
The documentation of the PyGAD library is available at Read The Docs at this link: https://pygad.readthedocs.io. It discusses the modules supported by PyGAD, all its classes, methods, attribute, and functions. For each module, a number of examples are given.
If there is an issue using PyGAD, feel free to post at issue in this GitHub repository https://github.com/ahmedfgad/GeneticAlgorithmPython or by sending an e-mail to ahmed.f.gad@gmail.com.
If you built a project that uses PyGAD, then please drop an e-mail to ahmed.f.gad@gmail.com with the following information so that your project is included in the documentation.
Please check the Contact Us section for more contact details.
The next figure lists the different stages in the lifecycle of an instance of the pygad.GA class. Note that PyGAD stops when either all generations are completed or when the function passed to the on_generation parameter returns the string stop.
The next code implements all the callback functions to trace the execution of the genetic algorithm. Each callback function prints its name.
import pygad
import numpy
function_inputs = [4,-2,3.5,5,-11,-4.7]
desired_output = 44
def fitness_func(ga_instance, solution, solution_idx):
output = numpy.sum(solution*function_inputs)
fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001)
return fitness
fitness_function = fitness_func
def on_start(ga_instance):
print("on_start()")
def on_fitness(ga_instance, population_fitness):
print("on_fitness()")
def on_parents(ga_instance, selected_parents):
print("on_parents()")
def on_crossover(ga_instance, offspring_crossover):
print("on_crossover()")
def on_mutation(ga_instance, offspring_mutation):
print("on_mutation()")
def on_generation(ga_instance):
print("on_generation()")
def on_stop(ga_instance, last_population_fitness):
print("on_stop()")
ga_instance = pygad.GA(num_generations=3,
num_parents_mating=5,
fitness_func=fitness_function,
sol_per_pop=10,
num_genes=len(function_inputs),
on_start=on_start,
on_fitness=on_fitness,
on_parents=on_parents,
on_crossover=on_crossover,
on_mutation=on_mutation,
on_generation=on_generation,
on_stop=on_stop)
ga_instance.run()
Based on the used 3 generations as assigned to the num_generations argument, here is the output.
on_start()
on_fitness()
on_parents()
on_crossover()
on_mutation()
on_generation()
on_fitness()
on_parents()
on_crossover()
on_mutation()
on_generation()
on_fitness()
on_parents()
on_crossover()
on_mutation()
on_generation()
on_stop()
Check the PyGAD's documentation for information about the implementation of this example. It solves a single-objective problem.
import pygad
import numpy
"""
Given the following function:
y = f(w1:w6) = w1x1 + w2x2 + w3x3 + w4x4 + w5x5 + 6wx6
where (x1,x2,x3,x4,x5,x6)=(4,-2,3.5,5,-11,-4.7) and y=44
What are the best values for the 6 weights (w1 to w6)? We are going to use the genetic algorithm to optimize this function.
"""
function_inputs = [4,-2,3.5,5,-11,-4.7] # Function inputs.
desired_output = 44 # Function output.
def fitness_func(ga_instance, solution, solution_idx):
# Calculating the fitness value of each solution in the current population.
# The fitness function calulates the sum of products between each input and its corresponding weight.
output = numpy.sum(solution*function_inputs)
fitness = 1.0 / numpy.abs(output - desired_output)
return fitness
fitness_function = fitness_func
num_generations = 100 # Number of generations.
num_parents_mating = 7 # Number of solutions to be selected as parents in the mating pool.
# To prepare the initial population, there are 2 ways:
# 1) Prepare it yourself and pass it to the initial_population parameter. This way is useful when the user wants to start the genetic algorithm with a custom initial population.
# 2) Assign valid integer values to the sol_per_pop and num_genes parameters. If the initial_population parameter exists, then the sol_per_pop and num_genes parameters are useless.
sol_per_pop = 50 # Number of solutions in the population.
num_genes = len(function_inputs)
last_fitness = 0
def callback_generation(ga_instance):
global last_fitness
print(f"Generation = {ga_instance.generations_completed}")
print(f"Fitness = {ga_instance.best_solution()[1]}")
print(f"Change = {ga_instance.best_solution()[1] - last_fitness}")
last_fitness = ga_instance.best_solution()[1]
# Creating an instance of the GA class inside the ga module. Some parameters are initialized within the constructor.
ga_instance = pygad.GA(num_generations=num_generations,
num_parents_mating=num_parents_mating,
fitness_func=fitness_function,
sol_per_pop=sol_per_pop,
num_genes=num_genes,
on_generation=callback_generation)
# Running the GA to optimize the parameters of the function.
ga_instance.run()
# After the generations complete, some plots are showed that summarize the how the outputs/fitenss values evolve over generations.
ga_instance.plot_fitness()
# Returning the details of the best solution.
solution, solution_fitness, solution_idx = ga_instance.best_solution()
print(f"Parameters of the best solution : {solution}")
print(f"Fitness value of the best solution = {solution_fitness}")
print(f"Index of the best solution : {solution_idx}")
prediction = numpy.sum(numpy.array(function_inputs)*solution)
print(f"Predicted output based on the best solution : {prediction}")
if ga_instance.best_solution_generation != -1:
print(f"Best fitness value reached after {ga_instance.best_solution_generation} generations.")
# Saving the GA instance.
filename = 'genetic' # The filename to which the instance is saved. The name is without extension.
ga_instance.save(filename=filename)
# Loading the saved GA instance.
loaded_ga_instance = pygad.load(filename=filename)
loaded_ga_instance.plot_fitness()
There are different resources that can be used to get started with the genetic algorithm and building it in Python.
To start with coding the genetic algorithm, you can check the tutorial titled Genetic Algorithm Implementation in Python available at these links:
This tutorial is prepared based on a previous version of the project but it still a good resource to start with coding the genetic algorithm.
Get started with the genetic algorithm by reading the tutorial titled Introduction to Optimization with Genetic Algorithm which is available at these links:
Read about building neural networks in Python through the tutorial titled Artificial Neural Network Implementation using NumPy and Classification of the Fruits360 Image Dataset available at these links:
Read about training neural networks using the genetic algorithm through the tutorial titled Artificial Neural Networks Optimization using Genetic Algorithm with Python available at these links:
To start with coding the genetic algorithm, you can check the tutorial titled Building Convolutional Neural Network using NumPy from Scratch available at these links:
This tutorial) is prepared based on a previous version of the project but it still a good resource to start with coding CNNs.
Get started with the genetic algorithm by reading the tutorial titled Derivation of Convolutional Neural Network from Fully Connected Network Step-By-Step which is available at these links:
You can also check my book cited as Ahmed Fawzy Gad 'Practical Computer Vision Applications Using Deep Learning with CNNs'. Dec. 2018, Apress, 978-1-4842-4167-7 which discusses neural networks, convolutional neural networks, deep learning, genetic algorithm, and more.
Find the book at these links:
If you used PyGAD, please consider adding a citation to the following paper about PyGAD:
@article{gad2023pygad,
title={Pygad: An intuitive genetic algorithm python library},
author={Gad, Ahmed Fawzy},
journal={Multimedia Tools and Applications},
pages={1--14},
year={2023},
publisher={Springer}
}
FAQs
PyGAD: A Python Library for Building the Genetic Algorithm and Training Machine Learning Algoithms (Keras & PyTorch).
We found that pygad demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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