Sampling and Learning the Mallows and Generalized Mallows Models under the Cayley Distance. Methodology and Computing in Applied Probability (DOI: 10.1007/s11009-016-9506-7). Irurozki, E., Calvo, B. and Lozano, J. A. 2016. PDF
A Review of Distances for the Mallows and Generalized Mallows Estimation of Distribution Algorithms. Journal of Computational Optimization and Applications, 62(2), 545--564. Ceberio, J., Irurozki, E., Mendiburu, A. and Lozano, J. A. 2014.
A Distance-based Ranking Model Estimation of Distribution Algorithm for the Flowshop Scheduling Problem. IEEE Trans. on Evolutionary Computation, 18(2), 286--300. Ceberio, J., Irurozki, E., Mendiburu, A. and Lozano, J. A. 2014.
A Review on Estimation of Distribution Algorithms in Permutation-based Combinatorial Optimization Problems. Progress in Artificial Intelligence, 1(1), 103--117. Ceberio, J., Irurozki, E., Mendiburu, A. and Lozano, J. A. 2012.
A Preprocessing Procedure for Haplotype Inference by Pure Parsimony. IEEE/ACM Trans. on Computational Biology and Bioinformatics, 8(5), 1183--1195 . Irurozki, E., Calvo, B. and Lozano, J. A. 2011.
Minería de Datos. Un Paseo por la Geometría: curso 2008-2009, 14-157. Lozano, J. A. and Irurozki E. 2009.
Mallows and Generalized Mallows Model for Matchings. 2nd round revision Bernoulli Irurozki, E., Calvo, B. and Lozano, J. A. 2016. PDF
perm mateda: A matlab toolbox of estimation of distribution algorithms for permutation-based combinatorial optimization problems Submitted to Transactions on Mathematical Software (TOMS). Irurozki, E., Ceberio, J., Santamaria, J., Santana, R. and Mendiburu, A. 2016.
Mallows Model under the Ulam Distance: a Feasible Combinatorial Approach. In Neural Information Processing System (NIPS), Analysis on Rank Data workshop. Irurozki, E., Ceberio, J., Calvo, B. and Lozano, J. A. 2014. PDF
Extending Distance-based Ranking Models in Estimation of Distribution Algorithms. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation. Ceberio, J., Irurozki, E., Mendiburu, A. and Lozano, J. A. 2014.
Learning Probability Distributions over Permutations by means of Fourier Coefficients In Canadian Conf. on Artificial Intelligence, 6657, 186–191. Irurozki, E., Calvo, B. and Lozano, J. A. 2011.
A New Preprocessing Procedure for the Haplotype Inference Problem. In IEEE Congress on Evolutionary Computation, 1320–1327. Irurozki, E., Calvo, B. and Lozano, J. A. 2009.
PerMallows (CRAN and GitHub) includes functions to work with the Mallows and Generalized Mallows Models. The considered distances are Kendall's-tau, Cayley, Hamming and Ulam and it includes functions for making inference, sampling and learning such distributions, some of which are novel in the literature. As a by-product, PerMallows also includes operations for permutations, paying special attention to those related with the Kendall's-tau, Cayley, Ulam and Hamming distances. It is also possible to generate random permutations at a given distance, or with a given number of inversions, or cycles, or fixed points or even with a given length on LIS (longest increasing subsequence).
random_perm_inversion generates a random permutation at a Kendall's-tau distance from the identity (i.e., with a given number of inversions).
PyRoad partitions the roads of a road network into homogeneous segments. It generates a data base of the segments and an interactive map. The road data can be read from OpenStreetMap.