https://github.com/thieu1995/graforvfl
GrafoRVFL: A Gradient-Free Optimization Framework for Boosting Random Vector Functional Link Network
https://github.com/thieu1995/graforvfl
artificial-intelligence evolutionary-computing genetic-algorithm global-search gradient-free-based-rvfl machine-learning mealpy metaheuristic-algorithm metaheuristics nature-inspired-algorithms neural-network optimization-algorithms particle-swarm-optimization random-vector-functional-link-neural-network rvfl-networks swarm-based-intelligence whale-optimization-algorithm
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GrafoRVFL: A Gradient-Free Optimization Framework for Boosting Random Vector Functional Link Network
- Host: GitHub
- URL: https://github.com/thieu1995/graforvfl
- Owner: thieu1995
- License: gpl-3.0
- Created: 2023-08-08T11:23:30.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-11-10T17:27:08.000Z (11 months ago)
- Last Synced: 2024-11-10T17:28:44.561Z (11 months ago)
- Topics: artificial-intelligence, evolutionary-computing, genetic-algorithm, global-search, gradient-free-based-rvfl, machine-learning, mealpy, metaheuristic-algorithm, metaheuristics, nature-inspired-algorithms, neural-network, optimization-algorithms, particle-swarm-optimization, random-vector-functional-link-neural-network, rvfl-networks, swarm-based-intelligence, whale-optimization-algorithm
- Language: Python
- Homepage: https://graforvfl.readthedocs.org
- Size: 126 KB
- Stars: 5
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: ChangeLog.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
Awesome Lists containing this project
README
# GrafoRVFL (GRAdient Free Optimized Random Vector Functional Link)
---
[](https://github.com/thieu1995/GrafoRVFL/releases)
[](https://pypi.python.org/pypi/graforvfl)
[](https://badge.fury.io/py/graforvfl)
[](https://pepy.tech/project/graforvfl)
[](https://github.com/thieu1995/graforvfl/actions/workflows/publish-package.yml)
[](https://graforvfl.readthedocs.io/en/latest/?badge=latest)
[](https://t.me/+fRVCJGuGJg1mNDg1)
[](https://doi.org/10.5281/zenodo.10258280)
[](https://www.gnu.org/licenses/gpl-3.0)
## 📑 Overview
**GrafoRVFL** is an open-source Python library designed to optimize Random Vector Functional Link (RVFL) networks using
various **gradient-free metaheuristic algorithms** such as GA, PSO, WOA, TLO, DE, etc. It is fully implemented in
**NumPy** and seamlessly integrates with the **Scikit-Learn** interface, making it easy to plug into standard
ML workflows. GrafoRVFL enables hyperparameter tuning for RVFL networks without relying on gradient-based methods.
## ✨ Features
- ✅ Free software under **GNU GPL v3**
- 📘 Full documentation: [https://graforvfl.readthedocs.io](https://graforvfl.readthedocs.io)
- 🧠 Estimators:
- `RvflRegressor`
- `RvflClassifier`
- `GfoRvflCV`
- `GfoRvflTuner`
- `GfoRvflComparator`
- 🐍 Python compatibility: `>= 3.8`
- 🧩 Dependencies:
- `numpy`, `scipy`, `scikit-learn`, `pandas`, `mealpy`, `permetrics`, `matplotlib`
## 📖 Citation Request
Please include these citations if you plan to use this library:
```bibtex
@software{nguyen_van_thieu_2023_10258280,
author = {Nguyen Van Thieu},
title = {GrafoRVFL: A Gradient-Free Optimization Framework for Boosting Random Vector Functional Link Network},
month = June,
year = 2025,
publisher = {Zenodo},
doi = {10.5281/zenodo.10258280},
url = {https://github.com/thieu1995/GrafoRVFL}
}
@article{van2023mealpy,
title={MEALPY: An open-source library for latest meta-heuristic algorithms in Python},
author={Van Thieu, Nguyen and Mirjalili, Seyedali},
journal={Journal of Systems Architecture},
year={2023},
publisher={Elsevier},
doi={10.1016/j.sysarc.2023.102871}
}
@inproceedings{nguyen2019building,
title={Building resource auto-scaler with functional-link neural network and adaptive bacterial foraging optimization},
author={Nguyen, Thieu and Nguyen, Binh Minh and Nguyen, Giang},
booktitle={International Conference on Theory and Applications of Models of Computation},
pages={501--517},
year={2019},
organization={Springer}
}
@inproceedings{nguyen2018resource,
title={A resource usage prediction system using functional-link and genetic algorithm neural network for multivariate cloud metrics},
author={Nguyen, Thieu and Tran, Nhuan and Nguyen, Binh Minh and Nguyen, Giang},
booktitle={2018 IEEE 11th conference on service-oriented computing and applications (SOCA)},
pages={49--56},
year={2018},
organization={IEEE},
doi={10.1109/SOCA.2018.00014}
}
```
* Learn more about Random Vector Functional Link from [this paper](https://doi.org/10.1016/j.ins.2015.09.025)
* Learn more about on how to use Gradient Free Optimization to fine-tune the hyper-parameter of RVFL networks from
[this paper](https://doi.org/10.1016/j.neucom.2018.07.080)
## 🔧 Installation
Install the latest version from PyPI:
```bash
$ pip install graforvfl
```
Verify installation:
```bash
$ python
>>> import graforvfl
>>> graforvfl.__version__
```
## 🧪 Example Usage
Below is a simple example code of how to use Gradient Free Optimization to tune hyper-parameter of RVFL network.
```python
from sklearn.datasets import load_breast_cancer
from graforvfl import Data, GfoRvflCV, StringVar, IntegerVar, FloatVar
## Load data object
X, y = load_breast_cancer(return_X_y=True)
data = Data(X, y)
## Split train and test
data.split_train_test(test_size=0.2, random_state=2, inplace=True)
print(data.X_train.shape, data.X_test.shape)
## Scaling dataset
data.X_train, scaler_X = data.scale(data.X_train, scaling_methods=("standard", "minmax"))
data.X_test = scaler_X.transform(data.X_test)
data.y_train, scaler_y = data.encode_label(data.y_train)
data.y_test = scaler_y.transform(data.y_test)
# Design the boundary (parameters)
my_bounds = [
IntegerVar(lb=3, ub=50, name="size_hidden"),
StringVar(valid_sets=("none", "relu", "leaky_relu", "celu", "prelu", "gelu", "elu",
"selu", "rrelu", "tanh", "hard_tanh", "sigmoid", "hard_sigmoid",
"log_sigmoid", "silu", "swish", "hard_swish", "soft_plus", "mish",
"soft_sign", "tanh_shrink", "soft_shrink", "hard_shrink",
"softmin", "softmax", "log_softmax"), name="act_name"),
StringVar(valid_sets=("orthogonal", "he_uniform", "he_normal", "glorot_uniform",
"glorot_normal", "lecun_uniform", "lecun_normal", "random_uniform",
"random_normal"), name="weight_initializer"),
FloatVar(lb=0, ub=10., name="reg_alpha"),
]
model = GfoRvflCV(problem_type="classification", bounds=my_bounds,
optim="OriginalWOA", optim_params={"name": "WOA", "epoch": 10, "pop_size": 20},
scoring="AS", cv=3, seed=42, verbose=True)
model.fit(data.X_train, data.y_train)
print(model.best_params)
print(model.best_estimator)
print(model.best_estimator.scores(data.X_test, data.y_test, list_metrics=("PS", "RS", "NPV", "F1S", "F2S")))
```
👉 The more complicated cases in the folder: [examples](/examples). You can also read the [documentation](https://graforvfl.readthedocs.io/)
for more detailed installation instructions, explanations, and examples.
## 📎 Official channels
* 🔗 [Official source code repository](https://github.com/thieu1995/GrafoRVFL)
* 📘 [Official document](https://graforvfl.readthedocs.io/)
* 📦 [Download releases](https://pypi.org/project/graforvfl/)
* 🐞 [Issue tracker](https://github.com/thieu1995/GrafoRVFL/issues)
* 📝 [Notable changes log](/ChangeLog.md)
* 💬 [Official discussion group](https://t.me/+fRVCJGuGJg1mNDg1)
---
Developed by: [Thieu](mailto:nguyenthieu2102@gmail.com?Subject=GrafoRVFL_QUESTIONS) @ 2025