# Projects and Research The open-source projects that make up PyGAD, projects built with it, and research papers that use it. ## PyGAD Projects at GitHub The PyGAD library is available at PyPI at this page https://pypi.org/project/pygad. PyGAD is built out of a number of open-source GitHub projects. A brief note about these projects is given in the next subsections. ### [GeneticAlgorithmPython](https://github.com/ahmedfgad/GeneticAlgorithmPython) GitHub Link: https://github.com/ahmedfgad/GeneticAlgorithmPython [**GeneticAlgorithmPython**](https://github.com/ahmedfgad/GeneticAlgorithmPython) is the first project which is an open-source Python 3 project for implementing the genetic algorithm based on NumPy. ### [NumPyANN](https://github.com/ahmedfgad/NumPyANN) GitHub Link: https://github.com/ahmedfgad/NumPyANN [**NumPyANN**](https://github.com/ahmedfgad/NumPyANN) builds artificial neural networks in **Python 3** using **NumPy** from scratch. The purpose of this project is to only implement the **forward pass** of a neural network without using a training algorithm. Currently, it only supports classification and later regression will be also supported. Moreover, only one class is supported per sample. ### [NeuralGenetic](https://github.com/ahmedfgad/NeuralGenetic) GitHub Link: https://github.com/ahmedfgad/NeuralGenetic [NeuralGenetic](https://github.com/ahmedfgad/NeuralGenetic) trains neural networks using the genetic algorithm based on the previous 2 projects [GeneticAlgorithmPython](https://github.com/ahmedfgad/GeneticAlgorithmPython) and [NumPyANN](https://github.com/ahmedfgad/NumPyANN). ### [NumPyCNN](https://github.com/ahmedfgad/NumPyCNN) GitHub Link: https://github.com/ahmedfgad/NumPyCNN [NumPyCNN](https://github.com/ahmedfgad/NumPyCNN) builds convolutional neural networks using NumPy. The purpose of this project is to only implement the **forward pass** of a convolutional neural network without using a training algorithm. ### [CNNGenetic](https://github.com/ahmedfgad/CNNGenetic) GitHub Link: https://github.com/ahmedfgad/CNNGenetic [CNNGenetic](https://github.com/ahmedfgad/CNNGenetic) trains convolutional neural networks using the genetic algorithm. It uses the [GeneticAlgorithmPython](https://github.com/ahmedfgad/GeneticAlgorithmPython) project for building the genetic algorithm. ### [KerasGA](https://github.com/ahmedfgad/KerasGA) GitHub Link: https://github.com/ahmedfgad/KerasGA [KerasGA](https://github.com/ahmedfgad/KerasGA) trains [Keras](https://keras.io) models using the genetic algorithm. It uses the [GeneticAlgorithmPython](https://github.com/ahmedfgad/GeneticAlgorithmPython) project for building the genetic algorithm. ### [TorchGA](https://github.com/ahmedfgad/TorchGA) GitHub Link: https://github.com/ahmedfgad/TorchGA [TorchGA](https://github.com/ahmedfgad/TorchGA) trains [PyTorch](https://pytorch.org) models using the genetic algorithm. It uses the [GeneticAlgorithmPython](https://github.com/ahmedfgad/GeneticAlgorithmPython) project for building the genetic algorithm. [pygad.torchga](https://github.com/ahmedfgad/TorchGA): https://github.com/ahmedfgad/TorchGA ## Projects Built using PyGAD If you created a project that uses PyGAD, then we can support you by mentioning this project here in PyGAD's documentation. To do that, please send a message at ahmed.f.gad@gmail.com or check the [**Contact Us**](https://pygad.readthedocs.io/en/latest/help_support.html#contact-us) section for more contact details. Within your message, please send the following details: - Project title - Brief description - Preferably, a link that directs the readers to your project ## Research Papers using PyGAD A number of research papers used PyGAD and here are some of them: * Alberto Meola, Manuel Winkler, Sören Weinrich, Metaheuristic optimization of data preparation and machine learning hyperparameters for prediction of dynamic methane production, Bioresource Technology, Volume 372, 2023, 128604, ISSN 0960-8524. * Jaros, Marta, and Jiri Jaros. "Performance-Cost Optimization of Moldable Scientific Workflows." * Thorat, Divya. "Enhanced genetic algorithm to reduce makespan of multiple jobs in map-reduce application on serverless platform". Diss. Dublin, National College of Ireland, 2020. * Koch, Chris, and Edgar Dobriban. "AttenGen: Generating Live Attenuated Vaccine Candidates using Machine Learning." (2021). * Bhardwaj, Bhavya, et al. "Windfarm optimization using Nelder-Mead and Particle Swarm optimization." *2021 7th International Conference on Electrical Energy Systems (ICEES)*. IEEE, 2021. * Bernardo, Reginald Christian S. and J. Said. “Towards a model-independent reconstruction approach for late-time Hubble data.” (2021). * Duong, Tri Dung, Qian Li, and Guandong Xu. "Prototype-based Counterfactual Explanation for Causal Classification." *arXiv preprint arXiv:2105.00703* (2021). * Farrag, Tamer Ahmed, and Ehab E. Elattar. "Optimized Deep Stacked Long Short-Term Memory Network for Long-Term Load Forecasting." *IEEE Access* 9 (2021): 68511-68522. * Antunes, E. D. O., Caetano, M. F., Marotta, M. A., Araujo, A., Bondan, L., Meneguette, R. I., & Rocha Filho, G. P. (2021, August). Soluções Otimizadas para o Problema de Localização de Máxima Cobertura em Redes Militarizadas 4G/LTE. In *Anais do XXVI Workshop de Gerência e Operação de Redes e Serviços* (pp. 152-165). SBC. * M. Yani, F. Ardilla, A. A. Saputra and N. Kubota, "Gradient-Free Deep Q-Networks Reinforcement learning: Benchmark and Evaluation," *2021 IEEE Symposium Series on Computational Intelligence (SSCI)*, 2021, pp. 1-5, doi: 10.1109/SSCI50451.2021.9659941. * Yani, Mohamad, and Naoyuki Kubota. "Deep Convolutional Networks with Genetic Algorithm for Reinforcement Learning Problem." * Mahendra, Muhammad Ihza, and Isman Kurniawan. "Optimizing Convolutional Neural Network by Using Genetic Algorithm for COVID-19 Detection in Chest X-Ray Image." *2021 International Conference on Data Science and Its Applications (ICoDSA)*. IEEE, 2021. * Glibota, Vjeko. *Umjeravanje mikroskopskog prometnog modela primjenom genetskog algoritma*. Diss. University of Zagreb. Faculty of Transport and Traffic Sciences. Division of Intelligent Transport Systems and Logistics. Department of Intelligent Transport Systems, 2021. * Zhu, Mingda. *Genetic Algorithm-based Parameter Identification for Ship Manoeuvring Model under Wind Disturbance*. MS thesis. NTNU, 2021. * Abdalrahman, Ahmed, and Weihua Zhuang. "Dynamic pricing for differentiated pev charging services using deep reinforcement learning." *IEEE Transactions on Intelligent Transportation Systems* (2020).