Initial version Mateda3.0

InitEnvironments.m

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+function[] = InitEnvironments()
+% [] = InitEnvironments()
+%
+% InitEnvironments:           Initialize the environment of mateda
+%                             update the paths below according the
+%                             location of the programs in your computer.             
+%
+% Last version 12/21/2020. Roberto Santana (roberto.santana@ehu.es)       
+
+
+%path_mateda =  '~/Dropbox/Colaborations/Mateda3';
+path_mateda =  '~/Work/git/Mateda3';
+
+P = genpath(path_mateda);
+addpath(P);
+cd(path_mateda);
+
+
+
+% Last version 12/21/2020. Roberto Santana (roberto.santana@ehu.eus) 

InitEnvironments.m~

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+function[] = InitEnvironments()
+% [] = InitEnvironments()
+%
+% InitEnvironments:           Initialize the environment of mateda
+%                   After installing the BNT, BNT_SLP learning matlab, and
+%                   mateda toolboxs, update the paths below according the
+%                   location of the programs in your computer.             
+%
+% Last version 8/26/2008. Roberto Santana (roberto.santana@ehu.es)       
+
+
+
+%https://es.mathworks.com/matlabcentral/fileexchange/13562-structure-learning-package-for-bayes-net-toolbox
+%https://www.cs.ubc.ca/~schmidtm/Software/UGM.html
+%https://miat.inrae.fr/GMtoolbox/documentation.html
+% https://github.com/probml/pmtk3
+
+path_mateda =  '~/Dropbox/Colaborations/Mateda3';
+path_FullBNT = '~/Dropbox/Colaborations/Mateda3/bnt';
+path_BNT_SLP = '~/Dropbox/Colaborations/Mateda3/BNT_SLP';
+
+%path_mateda =  'C:\WorkDirectory\Mateda2.2';
+%path_FullBNT = 'C:\WorkDirectory\FullBNT-1.0.4';
+%path_BNT_SLP = 'C:\WorkDirectory\FullBNT-1.0.4\BNT_StructureLearning_v1[1].4c\BNT_SLP';
+
+
+
+%   cd(path_FullBNT);
+%   addpath(genpathKPM(pwd));
+%   cd(path_BNT_SLP);
+%   add_SLP;   
+
+
+P = genpath(path_mateda);
+addpath(P);
+cd(path_mateda);
+
+
+
+% Last version 12/04/2020. Roberto Santana (roberto.santana@ehu.eus) 

README_perm_mateda.txt

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+
+Main Features of perm_mateda
+-----------------------------
+
+- Implementation of Mallows and Generalized Mallows models.
+- Implementation of Cayley, Kendall, and Ulam distances between permutations.
+- Implementation of a number of optimization problems: Traveling Salesman Problem (TSP), Permutation Flowshop Scheduling Problem (PFSP), Linear Ordering Problem (LOP), and Quadratic Assignment Problem (QAP).
+- As a control, implementation of previous edge-histogram-based (EHM) and node-histogram-based (NHM) approaches to permutation problems [Tsutsui:2006] have been included.
+
+
+Organization of the permutation directory
+-----------------------------------------
+Distances: Implementation of Cayley, Kendall, and Ulam distances.         
+Consensus: Comprises the implementation of different methods for computing the consensus permutation given a set of permutations.            
+Mallows: Contains the implementation of the learning and sampling methods based on Mallows probabilistic models that used different distances.       
+Histogram_Models: Contains the implementation of the EHM and NHM models. These are histogram-based models included for the sake of comparison with previous approaches.
+Problems: Implementation of TSP, PFSP, LOP, and QAP problems. It also contains test instances for these problems. 
+Scripts_Perm_Mateda: Contains a number of examples of using the Mallows EDAs in using different parameters and for different problems. It also contains post-processing steps for extracting and visualizing the results of the algorithms.   
+Operations: Contains a number of auxiliary functions, including two dependencies used for the generation of Ferrer Shapes. Programs colex.m and partition.m
+
+
+A quick introduction to perm_mateda with a set of examples.
+----------------------------------------------------------
+
+- A number of examples are included in the directory Scripts_Perm_Mateda
+
+Example 1:  Application of Mallows EDA using the Ulam distance to the PFSP problem
+Example 2:  Application of Generalized Mallows EDA using the Cayley distance to the LOP problem
+Example 3:  Application of Generalized Mallows EDA using the Kendall distance to the LOP problem
+Example 4:  Application of Mallows EDA using the Cayley distance to the QAP problem
+Example 5:  Application of Mallows EDA using the Kendall distance to the TSP problem
+
+
+
+Some useful references:
+----------------------------------------------------------
+
+
+E. Irurozki, J. Ceberio, B. Calvo, J.A. Lozano. Mallows model under the Ulam distance: a feasible combinatorial approach. Neural Information Processing Systems 2014, Workshop on Analysis of Rank Data, Montreal, Canada 8-13 December 2014.
+J. Ceberio,  A. Mendiburu, J.A Lozano: Introducing the Mallows Model on Estimation of Distribution Algorithms. In Proceedings of International Conference on Neural Information Processing (ICONIP), 2011
+J. Ceberio, E. Irurozki, A. Mendiburu, J.A. Lozano. A Review of Distances for the Mallows and Generalized Mallows Estimation of Distribution Algorithms. Journal of Computational Optimization and Applications. Vol. 62, No. 2, Pp. 545-564.
+J. Ceberio, R. Santana, A. Mendiburu, J.A. Lozano. Mixtures of Generalized Mallows models for solving the Quadratic Assignment Problem. 2015 IEEE Congress on Evolutionary Computation (CEC-2015),pp.2050-2057, Sendai, Japan, 25-28 May 2015.
+S. Tsutsui. Node histogram vs. edge histogram: A comparison of probabilistic model-building genetic algorithms in permutation domains. In: Evolutionary Computation, 2006. CEC 2006. IEEE Congress on. IEEE, 2006. p. 1939-1946.
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