T
+34 946 567 842
F
+34 946 567 843
E
earza@bcamath.org
Information of interest
- Orcid: 0000-0002-8044-0334
-
An adaptive neuroevolution-based hyperheuristic
Arza, E.
; Ceberio, J.; Pérez, A.; Irurozki, E. (2020)
According to the No-Free-Lunch theorem, an algorithm that performs efficiently on any type of problem does not exist. In this sense, algorithms that exploit problem-specific knowledge usually outperform more generic ...
-
Approaching the Quadratic Assignment Problem with Kernels of Mallows Models under the Hamming Distance
Arza, E.; Ceberio, J.; Pérez, A.; Irurozki, E. (2019-07)
The Quadratic Assignment Problem (QAP) is a specially challenging permutation-based np-hard combinatorial optimization problem, since instances of size $n>40$ are seldom solved using exact methods. In this sense, many ...
-
Implementing the Cumulative Difference Plot in the IOHanalyzer
Arza, E.; Ceberio, J.; Irurozki, E.; Pérez, A. (2022-07)
The IOHanalyzer is a web-based framework that enables an easy visualization and comparison of the quality of stochastic optimization algorithms. IOHanalyzer offers several graphical and statistical tools analyze the results ...
-
Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment Problem
Arza, E.; Pérez, A.; Irurozki, E.; Ceberio, J. (2020-07)
The Quadratic Assignment Problem (QAP) is a well-known permutation-based combinatorial optimization problem with real applications in industrial and logistics environments. Motivated by the challenge that this NP-hard ...
-
On the fair comparison of optimization algorithms in different machines
Arza, E.; Pérez, A.; Ceberio, J.; Irurozki, E. (2021)
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 stopping criterion, all algorithms need to ...
-
Statistical assessment of experimental results: a graphical approach for comparing algorithms
Arza, E.; Ceberio, J.; Irurozki, E.; Pérez, A. (2021-08-25)
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 chaotic environment are just two examples ...