ahmedfgad/GeneticAlgorithmPython

Health v2 · maintenance only

Adoption not part of the score

2220 stars · 500 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

PyGAD is an open-source Python 3 library for implementing the genetic algorithm to optimize single- and multi-objective problems. It can also train machine learning models, with built-in support for Keras and PyTorch neural networks.

Use cases

• solve optimization problems with a genetic algorithm in python
• train keras neural networks using a genetic algorithm
• optimize pytorch model weights with evolutionary search
• run multi-objective optimization in python
• find optimal parameters with a metaheuristic instead of gradient descent
• feature selection using genetic algorithms

When to choose

• you need a simple, well-documented genetic algorithm library in Python
• you want to train or fine-tune Keras/PyTorch models without gradients
• your objective function is non-differentiable or black-box
• you need both single- and multi-objective evolutionary optimization

When to avoid

• you need industrial-strength large-scale evolutionary computation with massive parallelism
• your problem is smooth and differentiable, where standard gradient-based optimizers are faster
• you need other metaheuristics like particle swarm or simulated annealing out of the box

Facets

library · maturity active

machine-learning deep-learning machine-learning artificial-intelligence deep-learning python genetic-algorithm evolutionary-algorithms optimization numpy keras pytorch metaheuristics multi-objective-optimization

Member repositories

For agents

markdown · JSON · MCP: product_card(name="ahmedfgad/GeneticAlgorithmPython")