pygad

What it is and what it does

PyGAD is a genetic algorithm library that lets you define a fitness function and run an evolutionary optimization loop to find good solutions to single- or multi-objective problems. It handles population initialization, parent selection, crossover, and mutation automatically, with callback hooks at each stage so you can monitor or intervene in the algorithm's execution. The core library depends only on numpy and cloudpickle, keeping the base install lightweight; optional extras add visualization (matplotlib) and deep learning integration (Keras/PyTorch).

You write a fitness function that scores candidate solutions, then instantiate a GA object with your parameters (population size, number of generations, crossover/mutation strategy) and call run(). The library is actively maintained, supports current Python versions, and is documented with examples covering both simple optimization problems and neural network training scenarios.

Use it for

• Optimize weights or hyperparameters for a machine learning model when gradient-based methods are impractical or you want to explore a non-convex search space.
• Train neural network weights using Keras or PyTorch by wrapping the model in a fitness function that evaluates test accuracy.
• Solve combinatorial or discrete optimization problems (e.g., scheduling, routing) where you define fitness as a penalty-based score.
• Multi-objective optimization where you balance competing goals (e.g., model accuracy vs. inference speed) in a single fitness metric.
• Educational exploration of genetic algorithms and evolutionary computation with built-in lifecycle callbacks to trace algorithm behavior.

Worth the install?

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Install

pygad on PyPI

Before you install

Low install friction with only two runtime dependencies (numpy and cloudpickle). Active maintenance with recent releases; last commit 2026-07-09. Optional extras available for visualization and deep learning features.

License in practice

Licensed under BSD 3-Clause, which permits commercial and private use with attribution and liability disclaimers. License treatment is marked unclear in the fact sheet, so verify the exact terms apply to your use case.

Quickstart

pip install pygad

import pygad
import numpy

def fitness_func(ga_instance, solution, solution_idx):
    output = numpy.sum(solution * [4, -2, 3.5, 5, -11, -4.7])
    return 1.0 / (numpy.abs(output - 44) + 0.000001)

ga = pygad.GA(num_generations=100, num_parents_mating=7,
              fitness_func=fitness_func, sol_per_pop=10, num_genes=6)
ga.run()

Package facts

Evidence: pygad-3.7.0-py3-none-any.whl

Tags

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