Search
Now showing items 1-10 of 10
Implementing the Cumulative Difference Plot in the IOHanalyzer
Arza, E.
; Ceberio, J.; Irurozki, E.; Pérez, A. (2022-07)
; 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 ...
Rank aggregation for non-stationary data streams
Irurozki, E.; Pérez, A.; Lobo, J.L.; Del Ser, J. (2022)
; Lobo, J.L.; Del Ser, J. (2022)
The problem of learning over non-stationary ranking streams arises naturally, particularly in recommender systems. The rankings represent the preferences of a population, and the non-stationarity means that the distribution ...
Statistical assessment of experimental results: a graphical approach for comparing algorithms
Arza, E.; Ceberio, J.; Irurozki, E.; Pérez, A. (2021-08-25)
; 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 ...
On the fair comparison of optimization algorithms in different machines
Arza, E.; Pérez, A.; Ceberio, J.; Irurozki, E. (2021)
; 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 ...
Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment Problem
Arza, E.; Pérez, A.; Irurozki, E.; Ceberio, J. (2020-07)
; 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 ...
Alternative Representations for Codifying Solutions in Permutation-Based Problems
(2020-07-01)
Since their introduction, Estimation of Distribution Algorithms (EDAs) have proved to be very competitive algorithms to solve many optimization problems. However, despite recent developments, in the case of permutation-based ...
An adaptive neuroevolution-based hyperheuristic
Arza, E.; Ceberio, J.; Pérez, A.; Irurozki, E. (2020)
; 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)
; 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 ...
Mallows and generalized Mallows model for matchings
Irurozki, E.; Calvo, B.; Lozano, J.A. (2019-02-25)
(2019-02-25)
The Mallows and Generalized Mallows Models are two of the most popular probability models for distribu- tions on permutations. In this paper, we consider both models under the Hamming distance. This models can be seen as ...
perm mateda: A matlab toolbox of estimation of distribution algorithms for permutation-based combinatorial optimization problems
(2018)
Permutation problems are combinatorial optimization problems whose solutions are naturally codified as permutations. Due to their complexity, motivated principally by the factorial cardinality of the search space of ...