pygad

Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).

Building Convolutional Neural Networks From Scratch using NumPy

Building and training artificial neural networks (regression or classification) using the genetic algorithm.

Implementation of Artificial Neural Networks using NumPy

Train PyTorch Models using the Genetic Algorithm with PyGAD

Python Control System : Create control loops and let the AI set the PID parameters

Training Keras Models by the Genetic Algorithm using PyGAD

This repo contains the code for neural network weight optimisation using 4 evolutionary algorithms.

Python-based optimisation tool for configuring lazy-wave dynamic power cables using PyGAD and static simulations in OrcaFlex.

A metaheuristic genetic algorithm route inspection problem solver for research purposes

Use genetic algorithm to train neuro network to play flappy bird game

A project in which nonogram puzzles are solved using genetic algorithms and swarm intelligence. The project compares the performance and quality of different solutions for different sizes of nonograms. The program was written in python using the pygad and pyswarms packages.

Automatically redraw any image into a painting by means of artificial evolution using the genetic algorithm with PyGAD.

Train a very small mnist model with genetic algorithm.

This genetic matching algorithm uses the PyGAD library to group users of StudyJio into groups of five.

Solving n queens puzzle with genetic algorithm.

ImageContrastEnhancement is a demonstration of the capabilities of convolutional genetic algorithms in image processing, specifically in the area of contrast enhancement.

Snake GA - Genetic algorithm that solves the Snake game. GA was implemented by PyGAD library available for Python, neural networks was created in Keras and game was created in Pygame.

Mathematical model, brute-force algorithm, and evolutionary algorithm for solving the internet shopping optimization problem

Two-stage genetic-algorithm timetabling — one GA builds a conflict-free master schedule under hard and soft constraints, a second GA derives each student's personal timetable from it.