14 citations · 54 across the 8 of their papers we have counts for
8 papers
On the Fair Comparison of Optimization Algorithms in Different Machines
Etor Arza, Josu Ceberio, Ekhiñe Irurozki +1
An experimental comparison of two or more optimization algorithms requires the same computational resources to be assigned to each algorithm. When a maximum runtime is set as the s…
Doubly Stochastic Matrix Models for Estimation of Distribution Algorithms
Valentino Santucci, Josu Ceberio
Problems with solutions represented by permutations are very prominent in combinatorial optimization. Thus, in recent decades, a number of evolutionary algorithms have been propose…
Neural Improvement Heuristics for Graph Combinatorial Optimization Problems
Andoni I. Garmendia, Josu Ceberio, Alexander Mendiburu
Recent advances in graph neural network architectures and increased computation power have revolutionized the field of combinatorial optimization (CO). Among the proposed models fo…
Neural Combinatorial Optimization: a New Player in the Field
Andoni I. Garmendia, Josu Ceberio, Alexander Mendiburu
Neural Combinatorial Optimization attempts to learn good heuristics for solving a set of problems using Neural Network models and Reinforcement Learning. Recently, its good perform…
Comparing Two Samples Through Stochastic Dominance: A Graphical Approach
Etor Arza, Josu Ceberio, Ekhiñe Irurozki +1
Non-deterministic measurements are common in real-world scenarios: the performance of a stochastic optimization algorithm or the total reward of a reinforcement learning agent in a…
Constrained Combinatorial Optimization with Reinforcement Learning
Ruben Solozabal, Josu Ceberio, Martin Takáč
This paper presents a framework to tackle constrained combinatorial optimization problems using deep Reinforcement Learning (RL). To this end, we extend the Neural Combinatorial Op…
Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment Problem
Etor Arza, Aritz Perez, Ekhine Irurozki +1
The Quadratic Assignment Problem (QAP) is a well-known permutation-based combinatorial optimization problem with real applications in industrial and logistics environments. Motivat…
Evolving Neural Networks in Reinforcement Learning by means of UMDAc
Mikel Malagon, Josu Ceberio
Neural networks are gaining popularity in the reinforcement learning field due to the vast number of successfully solved complex benchmark problems. In fact, artificial intelligenc…