GrafoRVFL: A gradient-free optimization framework for boosting random vector functional link network

Random Vector Functional Link (RVFL) networks have garnered attention as a rapid and efficient neural network model due to their simplified architecture and reduced training complexity. Nevertheless, the hyperparameter tuning of this network remains a substantial obstacle in the pursuit of enhanced performance across many applications. In this study, we present GrafoRVFL, an open-source framework that employs gradient-free algorithms to optimize RVFL networks’ hyperparameters. GrafoRVFL is a system that is adaptable and helps to enhance the performance of RVFL models. It is constructed on top of Numpy, Mealpy, and Scikit-Learn. We evaluate the proposed framework by comparing 14 hybrid gradient-free trained RVFL models on a variety of regression and classification datasets. The best-performing models achieve classification accuracies of 96%, 92%, and 85% on the breast cancer, waveform, and magic telescope datasets, respectively. For regression, R-scores of 0.70, 0.89, and 0.80 are observed on the diabetes, Boston housing, and California housing datasets. Additionally, we compare three hybrid RVFL models with GridSearchCV and RandomizedSearchCV on the digits dataset. The results show that our hybrid models yield better performance while requiring significantly less computational time. This suggests that our proposed framework can serve as a critical resource for researchers and practitioners who are seeking practical and resilient approaches to real-world issues. The source code of the library is accessible to the public on the GitHub repository: https://github.com/thieu1995/GrafoRVFL.