Now showing items 1-6 of 6
On the fair comparison of optimization algorithms in different machines
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
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 ...
An adaptive neuroevolution-based hyperheuristic
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
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
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
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 ...