Search Results for author:
Found 17 papers, 4 papers with code
Evolutionary latent space search for driving human portrait generation
This article presents an evolutionary approach for synthetic human portraits generation based on the latent space exploration of a generative adversarial network.
Citizen centric optimal electric vehicle charging stations locations in a full city: case of Malaga
This article presents the problem of locating electric vehicle (EV) charging stations in a city by defining the Electric Vehicle Charging Stations Locations (EV-CSL) problem.
Reliable and Fast Recurrent Neural Network Architecture Optimization
This article introduces Random Error Sampling-based Neuroevolution (RESN), a novel automatic method to optimize recurrent neural network architectures.
Fostering Diversity in Spatial Evolutionary Generative Adversarial Networks
Generative adversary networks (GANs) suffer from training pathologies such as instability and mode collapse, which mainly arise from a lack of diversity in their adversarial interactions.
Exact and heuristic approaches for multi-objective garbage accumulation points location in real scenarios
Municipal solid waste management is a major challenge for nowadays urban societies, because it accounts for a large proportion of public budget and, when mishandled, it can lead to environmental and social problems.
Signal Propagation in a Gradient-Based and Evolutionary Learning System
Generative adversarial networks (GANs) exhibit training pathologies that can lead to convergence-related degenerative behaviors, whereas spatially-distributed, coevolutionary algorithms (CEAs) for GAN training, e. g. Lipizzaner, are empirically robust to them.
Conditional Generative Adversarial Networks to Model Urban Outdoor Air Pollution
This is a relevant problem because the design of most cities prioritizes the use of motorized vehicles, which has degraded air quality in recent years, having a negative effect on urban health.
Analyzing the Components of Distributed Coevolutionary GAN Training
We investigate the impact on the performance of two algorithm components that influence the diversity during coevolution: the performance-based selection/replacement inside each sub-population and the communication through migration of solutions (networks) among overlapping neighborhoods.
Parallel/distributed implementation of cellular training for generative adversarial neural networks
Generative adversarial networks (GANs) are widely used to learn generative models.
Adversarial Genetic Programming for Cyber Security: A Rising Application Domain Where GP Matters
We delineate Adversarial Genetic Programming for Cyber Security, a research topic that, by means of genetic programming (GP), replicates and studies the behavior of cyber adversaries and the dynamics of their engagements.
Data Dieting in GAN Training
We investigate training Generative Adversarial Networks, GANs, with less data.
Re-purposing Heterogeneous Generative Ensembles with Evolutionary Computation
In machine learning, ensembles of predictors demonstrate better results than a single predictor for many tasks.
Random Error Sampling-based Recurrent Neural Network Architecture Optimization
Our findings show that we can achieve state-of-the-art error performance and that we reduce by half the time needed to perform the optimization.
Soft computing methods for multiobjective location of garbage accumulation points in smart cities
Experimental evaluation performed on real scenarios on the cities of Montevideo (Uruguay) and Bahia Blanca (Argentina) demonstrates the effectiveness of the proposed approaches.
Spatial Evolutionary Generative Adversarial Networks
We contribute a superior evolutionary GANs training method, Mustangs, that eliminates the single loss function used across Lipizzaner's grid.
DLOPT: Deep Learning Optimization Library
Deep learning hyper-parameter optimization is a tough task.
Low-Cost Recurrent Neural Network Expected Performance Evaluation
In this study, we propose a low computational cost model to evaluate the expected performance of a given architecture based on the distribution of the error of random samples of the weights.
