15 papers
Population-Based Multi-Objective Training of Discriminators for Semi-Supervised GANs
Francisco Sedeño, Francisco Chicano, Jamal Toutouh
Semi-supervised generative adversarial networks (SSL-GANs) can exploit large unlabeled datasets while retaining a classifier in the discriminator, but their training is often unsta…
Robust Multi-Objective Optimization for Bicycle Rebalancing in Shared Mobility Systems
Diego Daniel Pedroza-Perez, Gabriel Luque, Sergio Nesmachnow +1
Dock-based bike-sharing systems exhibit spatial imbalances between bicycle supply and user demand, often addressed through overnight truck-based rebalancing. This work studies stat…
Adversarial attacks to image classification systems using evolutionary algorithms
Sergio Nesmachnow, Jamal Toutouh
Image classification currently faces significant security challenges due to adversarial attacks, which consist of intentional alterations designed to deceive classification models…
Guiding Evolutionary AutoEncoder Training with Activation-Based Pruning Operators
Steven Jorgensen, Erik Hemberg, Jamal Toutouh +1
This study explores a novel approach to neural network pruning using evolutionary computation, focusing on simultaneously pruning the encoder and decoder of an autoencoder. We intr…
Generate more than one child in your co-evolutionary semi-supervised learning GAN
Francisco Sedeño, Jamal Toutouh, Francisco Chicano
Generative Adversarial Networks (GANs) are very useful methods to address semi-supervised learning (SSL) datasets, thanks to their ability to generate samples similar to real data.…
Performance Analysis of Optimized VANET Protocols in Real World Tests
Jamal Toutouh, Enrique Alba
Vehicular ad hoc networks (VANETs) provide the communications required to deploy Intelligent Transportation Systems (ITS). In the current state of the art in this field there is a…