no code implementations • 26 Jul 2023 • Isaac Triguero, Daniel Molina, Javier Poyatos, Javier Del Ser, Francisco Herrera
Most applications of Artificial Intelligence (AI) are designed for a confined and specific task.
no code implementations • 20 Feb 2023 • Javier Poyatos, Daniel Molina, Aitor Martínez, Javier Del Ser, Francisco Herrera
MO-EvoPruneDeepTL uses Transfer Learning to adapt the last layers of Deep Neural Networks, by replacing them with sparse layers evolved by a genetic algorithm, which guides the evolution based in the performance, complexity and robustness of the network, being the robustness a great quality indicator for the evolved models.
1 code implementation • 8 Feb 2022 • Javier Poyatos, Daniel Molina, Aritz. D. Martinez, Javier Del Ser, Francisco Herrera
Depending on its solution encoding strategy, our proposed model can either perform optimized pruning or feature selection over the densely connected part of the neural network.
no code implementations • 9 Aug 2020 • Aritz D. Martinez, Javier Del Ser, Esther Villar-Rodriguez, Eneko Osaba, Javier Poyatos, Siham Tabik, Daniel Molina, Francisco Herrera
In summary, three axes - optimization and taxonomy, critical analysis, and challenges - which outline a complete vision of a merger of two technologies drawing up an exciting future for this area of fusion research.
no code implementations • 19 Feb 2020 • Daniel Molina, Javier Poyatos, Javier Del Ser, Salvador García, Amir Hussain, Francisco Herrera
From our analysis, we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior.