| 2024 | IJCNN | An Approach Using BRKGA for Optimizing Convolutional Neural Network Architectures. | Andersson A. Da Silva, Ricardo M. A. Silva |
| 2023 | CEC | cudaBRKGA-CNN: An Approach for Optimizing Convolutional Neural Network Architectures. | Andersson A. Da Silva, Ricardo M. A. Silva, Amanda S. Xavier, Thiago Dias Bispo, Geraldo R. Mateus, Mauricio G. C. Resende |
| 2019 | GECCO | A study of the levy distribution in generation of BRKGA random keys applied to global optimization. | Mariana Moura, Ricardo M. A. Silva |
| 2018 | CEC | Using a Many-Objective Optimization Algorithm to Select Sampling Approaches for Imbalanced Datasets. | Pricles B. C. Miranda, Romero F. A. B. de Morais, Ricardo M. A. Silva |
| 2017 | ESANN | A multi-criteria meta-learning method to select under-sampling algorithms for imbalanced datasets. | Romero F. A. B. de Morais, Pricles B. C. Miranda, Ricardo M. A. Silva |
| 2015 | IJCNN | Evolutionary Adaptive Self-Generating Prototypes for imbalanced datasets. | Dayvid V. R. Oliveira, George D. C. Cavalcanti, Tsang Ing Ren, Ricardo M. A. Silva |
| 2013 | CEC | Biased random-key genetic algorithm for nonlinearly-constrained global optimization. | Ricardo M. A. Silva, Mauricio G. C. Resende, Panos M. Pardalos, Joao L. Faco |