no code implementations • 5 Mar 2023 • Roberto Santana, Ivan Hidalgo-Cenalmor, Unai Garciarena, Alexander Mendiburu, Jose Antonio Lozano
We assess the impact of these functions on semi-supervised problems with a varying amount of labeled instances.
no code implementations • 16 Jun 2021 • Unai Garciarena, Roberto Santana, Alexander Mendiburu
In this paper, we investigate the effect of different variation operators in a complex domain, that of multi-network heterogeneous neural models.
no code implementations • 26 May 2021 • Unai Garciarena, Nuno Lourenço, Penousal Machado, Roberto Santana, Alexander Mendiburu
Neuroevolutionary algorithms, automatic searches of neural network structures by means of evolutionary techniques, are computationally costly procedures.
no code implementations • 21 Mar 2019 • Unai Garciarena, Alexander Mendiburu, Roberto Santana
Multi-task learning, as it is understood nowadays, consists of using one single model to carry out several similar tasks.
no code implementations • 1 Jul 2018 • Unai Garciarena, Roberto Santana, Alexander Mendiburu
In the past, evolutionary algorithms (EAs) that use probabilistic modeling of the best solutions incorporated latent or hidden vari- ables to the models as a more accurate way to represent the search distributions.
no code implementations • 13 Jan 2018 • Unai Garciarena, Alexander Mendiburu, Roberto Santana
We evaluate the method to introduce imputation methods as part of TPOT.
no code implementations • 4 Jun 2017 • Unai Garciarena, Roberto Santana, Alexander Mendiburu
Missing data has a ubiquitous presence in real-life applications of machine learning techniques.