pygad 2.3.0

PyGAD: A Python library for implementing the genetic algorithm.

PyGAD

PyGAD is an open-source Python 3 library for implementing the genetic algorithm and optimizing machine learning algorithms.

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.

Besides building the genetic algorithm, it builds and optimizes machine learning algorithms. Currently, PyGAD supports building and training (using genetic algorithm) artificial neural networks for classification problems.

The library is under active development and more features in the genetic algorithm will be added like working with binary problems. This is in addition to supporting more machine learning algorithms.

Installation

To install PyGAD, simply use pip to download and install the library from PyPI (Python Package Index). The library lives a PyPI at this page https://pypi.org/project/pygad.

For Windows, issue the following command:

pip install pygad

For Linux and Mac, replace pip by use pip3 because the library only supports Python 3.

pip3 install pygad

PyGAD is developed in Python 3.7.3 and depends on NumPy for creating and manipulating arrays and Matplotlib for creating figures. The exact NumPy version used in developing PyGAD is 1.16.4. For Matplotlib, the version is 3.1.0.

Quick Start

To get started with PyGAD, simply import it.

import pygad

Using PyGAD, a wide range of problems can be optimized. A quick and simple problem to be optimized using the PyGAD is finding the best set of weights that satisfy 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

The first step is to prepare the inputs and the outputs of this equation.

function_inputs = [4,-2,3.5,5,-11,-4.7]
desired_output = 44

A very important step is to implement the fitness function that will be used for calculating the fitness value for each solution. Here is one.

def fitness_func(solution, solution_idx):
    output = numpy.sum(solution*function_inputs)
    fitness = 1.0 / numpy.abs(output - desired_output)
    return fitness

Next is to prepare the parameters of PyGAD. Here is an example for a set of parameters.

fitness_function = fitness_func

num_generations = 50
num_parents_mating = 4

sol_per_pop = 8
num_genes = len(function_inputs)

init_range_low = -2
init_range_high = 5

parent_selection_type = "sss"
keep_parents = 1

crossover_type = "single_point"

mutation_type = "random"
mutation_percent_genes = 10

After the parameters are prepared, an instance of the pygad.GA class is created.

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,
                       init_range_low=init_range_low,
                       init_range_high=init_range_high,
                       parent_selection_type=parent_selection_type,
                       keep_parents=keep_parents,
                       crossover_type=crossover_type,
                       mutation_type=mutation_type,
                       mutation_percent_genes=mutation_percent_genes)

After creating the instance, the run() method is called to start the optimization.

ga_instance.run()

After the run() method completes, information about the best solution found by PyGAD can be accessed.

solution, solution_fitness, solution_idx = ga_instance.best_solution()
print("Parameters of the best solution : {solution}".format(solution=solution))
print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))

prediction = numpy.sum(numpy.array(function_inputs)*solution)
print("Predicted output based on the best solution : {prediction}".format(prediction=prediction))

Parameters of the best solution : [3.92692328 -0.11554946 2.39873381 3.29579039 -0.74091476 1.05468517]
Fitness value of the best solution = 157.37320042925006
Predicted output based on the best solution : 44.00635432206546

There is more to do using PyGAD. Read its documentation to explore the features of PyGAD.

PyGAD's Modules

PyGAD has the following modules:

1. The main module has the same name as the library which is pygad that builds the genetic algorithm.
2. The second module is nn which builds artificial neural networks.
3. The third module is gann for optimizing neural networks using the genetic algorithm.
4. The cnn module builds convolutional neural networks.
5. The gacnn module optimizes convolutional neural networks using the genetic algorithm.

The PyGAD's documentation discusses each of these modules.

PyGAD Documentation

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 [e-mail].

If you built a project that uses PyGAD, then please drop an e-mail to [e-mail] with the following information so that your project is included in the documentation.

• Project title
• Brief description
• Preferably, a link that directs the readers to your project

Please check the Contact Us section for more contact details.

For More Information

There are different resources that can be used to get started with the genetic algorithm and building it in Python.

Tutorial: Implementing Genetic Algorithm in Python

To start with coding the genetic algorithm, you can check the tutorial titled Genetic Algorithm Implementation in Python available at these links:

• LinkedIn
• Towards Data Science
• KDnuggets

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.

Tutorial: Introduction to 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:

• LinkedIn
• Towards Data Science
• KDnuggets

Tutorial: Build Neural Networks in Python

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:

• LinkedIn
• Towards Data Science
• KDnuggets

Tutorial: Optimize Neural Networks with Genetic Algorithm

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:

• LinkedIn
• Towards Data Science
• KDnuggets

Tutorial: Building CNN in Python

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:

• LinkedIn
• Towards Data Science
• KDnuggets
• Chinese Translation

This tutorial) is prepared based on a previous version of the project but it still a good resource to start with coding CNNs.

Tutorial: Derivation of CNN from FCNN

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:

• LinkedIn
• Towards Data Science
• KDnuggets

Book: Practical Computer Vision Applications Using Deep Learning with CNNs

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:

• Amazon
• Springer
• Apress
• O'Reilly
• Google Books

Contact Us

• E-mail: [e-mail]
• LinkedIn
• Amazon Author Page
• Heartbeat
• Paperspace
• KDnuggets
• TowardsDataScience
• GitHub