Now showing items 1-10 of 10

• #### An adaptive neuroevolution-based hyperheuristic ﻿

(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 ...
• #### 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 ...
• #### Approaching the Quadratic Assignment Problem with Kernels of Mallows Models under the Hamming Distance ﻿

(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 ﻿

(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 ﻿

(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 ...
• #### Mallows and generalized Mallows model for matchings ﻿

(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 ...
• #### On the fair comparison of optimization algorithms in different machines ﻿

(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 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 ...
• #### Rank aggregation for non-stationary data streams ﻿

(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 ﻿

(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 ...