Santana R and Alba E (2001), "Generating test matrices to evaluate the performance of strategies to search typical testors". Research Report at: Institute of Cybernetics, Mathematics and Physics. Havana, Cuba, January, 2001. (ICIMAF 2000130)
[Abstract] [BibTeX] [URL]

Abstract: Testors and particularly typical testors, have been used in feature selection and supervised classification problems. Deterministic algorithms have usually been used to find typical testors. Recently, a new approach based on evolutionary algorithms has been developed. A common problem to test the behavior of both approaches is the necessity of knowing, in advance, the number of typical testors of a given basic matrix. For an arbitrary matrix, this number can not be known unless all typical testors have been found. Therefore, this paper introduces, for the first time, a strategy to generate basic matrices for which the number of typical testors is known without to find them. This method is illustrated with some examples. 
BibTeX:
@techreport{Santana_and_Alba:2001a,
author = {Roberto Santana and Eduardo Alba},
title = {Generating test matrices to evaluate the performance of strategies to search typical testors},
school = {Institute of Cybernetics, Mathematics and Physics},
year = {2001},
number = {ICIMAF 2000130},
url = {https://revistas.usfq.edu.ec/index.php/avances/article/view/23}
}

Santana R, Ochoa A and Soto MR (2001), "The mixture of trees factorized distribution algorithm". Research Report at: Institute of Cybernetics, Mathematics and Physics. Havana, Cuba, January, 2001. (ICIMAF 2000129)
[Abstract] [BibTeX] [URL]

Abstract: This paper introduces the Mixtures of Trees Factorized Distribution Algorithm (MTFDA). It is based on a mixture of trees distribution and the Estimation Maximization learning algorithm. The probabilistic model and the learning procedure of the MTFDA differ to previous proposals of probabilistic modeling in the context of Evolutionary Computation. Preliminary results show that the MTFDA overperforms Factorized Distribution Algorithms that use up to second order statistics. It is also competitive, and some times superior to Bayesian Factorized Distribution Algorithms. The paper illustrates how the MTFDA can incorporate information about particular features of the search space by conveniently selecting the mixture of trees parameters. 
BibTeX:
@techreport{Santana_et_al:2001,
author = {R. Santana and A. Ochoa and M. R. Soto},
title = {The mixture of trees factorized distribution algorithm},
school = {Institute of Cybernetics, Mathematics and Physics},
year = {2001},
number = {ICIMAF 2000129},
url = {https://dl.acm.org/doi/pdf/10.5555/2955239.2955322}
}

Santana R, Ochoa A and Soto MR (2001), "The mixture of trees factorized distribution algorithm", In Proceedings of the Genetic and Evolutionary Computation Conference GECCO2001. San Francisco, CA , pp. 543550. Morgan Kaufmann Publishers.
[Abstract] [BibTeX] [URL]

Abstract: This paper introduces a Factorized Distribution Algorithm based on a mixture of trees distribution. The probabilistic model and the learning algorithm used differs to previous uses of probabilistic modeling in the context of Evolutionary Computation. Preliminary results show the algorithm is competitive, and some times superior to other Factorized Distribution Algorithms. We also illustrate how particular features of the search space can be employed during the search by conveniently selecting the mixture of trees parameters. 
BibTeX:
@inproceedings{Santana_et_al:2001b,
author = {R. Santana and A. Ochoa and M. R. Soto},
editor = {L. Spector and E. Goodman and A. Wu and W.B. Langdon and H.M. Voigt and M. Gen and S. Sen and M. Dorigo and S. Pezeshk and M. Garzon and E. Burke},
title = {The mixture of trees factorized distribution algorithm},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference GECCO2001},
publisher = {Morgan Kaufmann Publishers},
year = {2001},
pages = {543550},
url = {https://dl.acm.org/doi/pdf/10.5555/2955239.2955322}
}

Santana R, Ochoa A and Soto MR (2001), "Factorized Distribution Algorithms for functions with unitation constraints", In Evolutionary Computation and Probabilistic Graphical Models. Proceedings of the Third Symposium on Adaptive Systems (ISAS2001). Havana, Cuba, March, 2001. , pp. 158165.
[Abstract] [BibTeX] [URL]

Abstract: The class of non overlapping additively decomposed functions subject to unitation constraints are of interest for studying the behavior of Factorized Distribution Algorithms (FDAs) in constrained problems. In this paper we define a theoretical framework for the analysis of Constraint FDAs (CFDAs). This framework is used to investigate the factors that could explain the behavior of FDAs for the class of functions under consideration. Empirical evidence is shown to demonstrate our assertions. 
BibTeX:
@inproceedings{Santana_et_al:2001c,
author = {R. Santana and A. Ochoa and M. R. Soto},
title = {Factorized Distribution Algorithms for functions with unitation constraints},
booktitle = {Evolutionary Computation and Probabilistic Graphical Models. Proceedings of the Third Symposium on Adaptive Systems (ISAS2001)},
year = {2001},
pages = {158165},
url = {http://www.sc.ehu.es/ccwbayes/members/rsantana/papers/2001/Isas_Constraints_lastnew.pdf}
}

Santana R, Ochoa A and Soto MR (2001), "A Factorized Distribution Algorithm for problems with integer representation", In Proceedings of the Genetic and Evolutionary Computation Conference GECCO2001. San Francisco, CA , pp. 780. Morgan Kaufmann Publishers.
[Abstract] [BibTeX] [URL]

Abstract: In this poster we present a Factorized Distribution Algorithm (FDA) that considers up to second order statistics, and permits to carry out the optimization of integer problems with a high cardinality of the variables. 
BibTeX:
@inproceedings{Santana_et_al:2001d,
author = {R. Santana and A. Ochoa and M. R. Soto},
editor = {L. Spector and E. Goodman and A. Wu and W.B. Langdon and H.M. Voigt and M. Gen and S. Sen and M. Dorigo and S. Pezeshk and M. Garzon and E. Burke},
title = {A Factorized Distribution Algorithm for problems with integer representation},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference GECCO2001},
publisher = {Morgan Kaufmann Publishers},
year = {2001},
pages = {780},
url = {https://dl.acm.org/doi/pdf/10.5555/2955239.2955377}
}

Santana R, Ochoa A and Soto MR (2001), "On the use of Factorized Distribution Algorithms for problems defined on graphs", In Electronic Notes in Discrete Mathematics. Vol. 8 Elsevier.
[Abstract] [BibTeX] [URL]

Abstract: This short paper surveys current work on the use of Factorized Distribution Algorithms for the solution of combinatorial optimization problems defined on graphs. We also advance a number of approaches for future work along this line. 
BibTeX:
@inproceedings{Santana_et_al:2001e,
author = {Roberto Santana and Alberto Ochoa and Marta R. Soto},
editor = {Hajo Broersma and Ulrich Faigle and Johann Hurink and Stefan Pickl},
title = {On the use of Factorized Distribution Algorithms for problems defined on graphs},
booktitle = {Electronic Notes in Discrete Mathematics},
publisher = {Elsevier},
year = {2001},
volume = {8},
url = {http://www.sc.ehu.es/ccwbayes/members/rsantana/papers/1999/FDA_Graphs.pdf}
}

Soto MR, Ochoa A and Santana R (2001), "On the use of Polytrees in Evolutionary Computation", Investigación Operacional. Vol. 22(3), pp. 132138.
[Abstract] [BibTeX] [URL]

Abstract: Bayesian networks, are usefull tools for the representation of nonlinear interactions among variables. Recently, they have been combined with evolutionary methods to form a new class of optimization algorithms: the Factorized Distribution Algorithms (FDAs). FDAs have been proved to be significantly better than their genetic ancestors. They learn and sample distributions instead of using crossover and mutation operators. Most of the members of the FDAs that have been designed, learn general Bayesian networks. However, in this work we study a FDA that learns polytrees, which are single connected directed graphs. Key words: Bayesian networks, evolutionary algorithms, FDAs. MSC: 90B15, 90C35. RESUMEN Las redes Bayesianas son herramientas muy útiles para la representación de las interacciones no lineales entre las variables. Recientemente estas han sido combinadas con los métodos evolutivos para formar una nueva clase de algoritmos de optimización: los Algoritmos con Distribución Factorizada (FDA). Se ha probado que los FDA son mejores que sus antecesores, los Algoritmos Genéticos, en problemas donde existe una fuerte interacción entre las variables. Estos algoritmos aprenden y simulan distribuciones probabilísticas, en lugar de usar operadores de cruzamiento y mutación. La mayoría de los FDA diseñados hasta el momento aprenden redes Bayesianas generales. Sin embargo, en este trabajo nosotros estudiamos un FDA que aprende poliárboles, los cuales son grafos dirigidos simplemente conectados. Palabras clave: redes Bayesianas, algoritmos evolutivos, FDA. 
BibTeX:
@article{Soto_et_al_2001,
author = {Marta Rosa Soto and Alberto Ochoa and Roberto Santana},
title = {On the use of Polytrees in Evolutionary Computation},
journal = {Investigación Operacional},
year = {2001},
volume = {22},
number = {3},
pages = {132138},
url = {https://www.researchgate.net/publication/268320573_On_the_use_of_polytrees_in_evolutionary_optimization}
}
