Browsing by Author "Irurozki, E."
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An adaptive neuroevolutionbased hyperheuristic
Arza, E.; Ceberio, J.; Pérez, A.; Irurozki, E. (2020)According to the NoFreeLunch theorem, an algorithm that performs efficiently on any type of problem does not exist. In this sense, algorithms that exploit problemspecific knowledge usually outperform more generic ... 
Alternative Representations for Codifying Solutions in PermutationBased Problems
Malagon, M.; Irurozki, E.; Ceberio, J. (20200701)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 permutationbased ... 
Approaching the Quadratic Assignment Problem with Kernels of Mallows Models under the Hamming Distance
Arza, E.; Ceberio, J.; Pérez, A.; Irurozki, E. (201907)The Quadratic Assignment Problem (QAP) is a specially challenging permutationbased nphard 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. (202207)The IOHanalyzer is a webbased 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. (202007)The Quadratic Assignment Problem (QAP) is a wellknown permutationbased combinatorial optimization problem with real applications in industrial and logistics environments. Motivated by the challenge that this NPhard ... 
Mallows and generalized Mallows model for matchings
Irurozki, E.; Calvo, B.; Lozano, J.A. (20190225)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 ... 
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 ... 
perm mateda: A matlab toolbox of estimation of distribution algorithms for permutationbased combinatorial optimization problems
Irurozki, E.; Ceberio, J.; Santamaria, J.; Santana, R.; Mendiburu, A. (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 ... 
Rank aggregation for nonstationary data streams
Irurozki, E.; Pérez, A.; Lobo, J.L.; Del Ser, J. (2022)The problem of learning over nonstationary ranking streams arises naturally, particularly in recommender systems. The rankings represent the preferences of a population, and the nonstationarity means that the distribution ... 
Statistical assessment of experimental results: a graphical approach for comparing algorithms
Arza, E.; Ceberio, J.; Irurozki, E.; Pérez, A. (20210825)Nondeterministic measurements are common in realworld 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 ...