127 citations · 176 across the 16 of their papers we have counts for
7 papers · 1 filter
Enabling Population-Based Architectures for Neural Combinatorial Optimization
Andoni Irazusta Garmendia, Josu Ceberio, Alexander Mendiburu
Neural Combinatorial Optimization (NCO) has mostly focused on learning policies, typically neural networks, that operate on a single candidate solution at a time, either by constru…
MARCO: A Memory-Augmented Reinforcement Framework for Combinatorial Optimization
Andoni I. Garmendia, Quentin Cappart, Josu Ceberio +1
Neural Combinatorial Optimization (NCO) is an emerging domain where deep learning techniques are employed to address combinatorial optimization problems as a standalone solver. Des…
Neuroevolutionary algorithms driven by neuron coverage metrics for semi-supervised classification
Roberto Santana, Ivan Hidalgo-Cenalmor, Unai Garciarena +2
In some machine learning applications the availability of labeled instances for supervised classification is limited while unlabeled instances are abundant. Semi-supervised learnin…
Redefining Neural Architecture Search of Heterogeneous Multi-Network Models by Characterizing Variation Operators and Model Components
Unai Garciarena, Roberto Santana, Alexander Mendiburu
With neural architecture search methods gaining ground on manually designed deep neural networks -even more rapidly as model sophistication escalates-, the research trend shifts to…
On the Exploitation of Neuroevolutionary Information: Analyzing the Past for a More Efficient Future
Unai Garciarena, Nuno Lourenço, Penousal Machado +2
Neuroevolutionary algorithms, automatic searches of neural network structures by means of evolutionary techniques, are computationally costly procedures. In spite of this, due to t…
Computing factorized approximations of Pareto-fronts using mNM-landscapes and Boltzmann distributions
Roberto Santana, Alexander Mendiburu, Jose A. Lozano
NM-landscapes have been recently introduced as a class of tunable rugged models. They are a subset of the general interaction models where all the interactions are of order less or…
MOEA/D-GM: Using probabilistic graphical models in MOEA/D for solving combinatorial optimization problems
Murilo Zangari de Souza, Roberto Santana, Aurora Trinidad Ramirez Pozo +1
Evolutionary algorithms based on modeling the statistical dependencies (interactions) between the variables have been proposed to solve a wide range of complex problems. These algo…