Alba-Cabrera E and Santana R (2010), "Generación de matrices para evaluar el desempeño de estrategias de búsqueda de testores típicos", Avances en Ciencias e Ingenierías. Vol. 2(2), pp. A30-A35. |
Abstract: Los testores, y en particular los testores típicos, han sido utilizados en problemas de selección de variable y problemas de clasificación supervisada. Comunmente se ha usado algoritmos determinísticos para hallar testores típicos. A principios de esta decada comenzó a desarrollarse un nuevo enfoque basado en algoritmos evolutivos. Un problema común para probar el comportamiento de ambos métodos es la necesidad de conocer a priori el número de testores típicos de una matriz dada. Para una matriz arbitraria, no se puede saber este número a menos de que se hayan encontrado todos los testores típicos. Por lo tanto, este trabajo introduce, por primera vez, una estrategia para generar matrices básicas para las cuales el número de testores típicos es conocido sin necesidad de aplicar un algoritmo para encontrarlos. Este método se ilustra con algunos ejemplos. |
BibTeX:
@article{Alba_and_Santana:2010, author = {Eduardo Alba-Cabrera and R. Santana}, title = {Generación de matrices para evaluar el desempeño de estrategias de búsqueda de testores típicos}, journal = {Avances en Ciencias e Ingenierías}, year = {2010}, volume = {2}, number = {2}, pages = {A30-A35}, url = {https://revistas.usfq.edu.ec/index.php/avances/article/view/23} } |
Cuesta-Infante A, Santana R, Hidalgo JI, Bielza C and Larrañaga P (2010), "Bivariate empirical and n-variate Archimedean copulas in estimation of distribution algorithms", In Proceedings of the 2010 Congress on Evolutionary Computation CEC-2010. Barcelone, Spain , pp. 1-8. IEEE. |
Abstract: This paper investigates the use of empirical and Archimedean copulas as probabilistic models of continuous estimation of distribution algorithms (EDAs). A method for learning and sampling empirical bivariate copulas to be used in the context of n-dimensional EDAs is first introduced. Then, by using Archimedean copulas instead of empirical makes possible to construct n-dimensional copulas with the same purpose. Both copula-based EDAs are compared to other known continuous EDAs on a set of 24 functions and different number of variables. Experimental results show that the proposed copula-based EDAs achieve a better behaviour than previous approaches in a 20 percentage of the benchmark functions. |
BibTeX:
@inproceedings{Cuesta_et_al:2010, author = {Alfredo Cuesta-Infante and Roberto Santana and J. Ignacio Hidalgo and Concha Bielza and Pedro Larrañaga}, title = {Bivariate empirical and n-variate Archimedean copulas in estimation of distribution algorithms}, booktitle = {Proceedings of the 2010 Congress on Evolutionary Computation CEC-2010}, publisher = {IEEE}, year = {2010}, pages = {1-8}, url = {http://dx.doi.org/10.1109/CEC.2010.5586557} } |
Echegoyen C, Mendiburu A, Santana R and Lozano JA (2010), "Estimation of Bayesian networks algorithms in a class of complex networks", In Proceedings of the 2010 Congress on Evolutionary Computation CEC-2010. Barcelone, Spain IEEE Press. |
Abstract: In many optimization problems, regardless of the domain to which it belongs, the structural component that the interactions among variables provides can be seen as a network. The impact that the topological characteristics of that network has, both in the hardness of the problem and in the performance of the optimization techniques, constitutes a very important subject of research. In this paper, we study the behavior of estimation of distribution algorithms (EDAs) in functions whose structure is defined by using different network topologies which include grids, small-world networks and random graphs. In order to do that, we use several descriptors such as the population size, the number of evaluations as well as the structures learned during the search. Furthermore, we take measures from the field of complex networks such as clustering coefficient or characteristic path length in order to quantify the topological properties of the function structure and analyze their relation with the behavior of EDAs. The results show that these measures are useful to have better understanding of this type of algorithms which have exhibited a high sensitivity to the topological characteristics of the function structure. This study creates a link between EDAs based on Bayesian networks and the emergent field of complex networks. |
BibTeX:
@inproceedings{Echegoyen_et_al:2010, author = {C. Echegoyen and A. Mendiburu and R. Santana and J. A. Lozano}, title = {Estimation of Bayesian networks algorithms in a class of complex networks}, booktitle = {Proceedings of the 2010 Congress on Evolutionary Computation CEC-2010}, publisher = {IEEE Press}, year = {2010}, url = {http://dx.doi.org/10.1109/CEC.2010.5586511} } |
Echegoyen C, Mendiburu A, Santana R and Lozano JA (2010), "Analyzing the k most probable solutions in EDAs based on Bayesian networks", In Exploitation of Linkage Learning in Evolutionary Algorithms. , pp. 163-189. Springer. |
Abstract: Estimation of distribution algorithms (EDAs) have been successfully applied to a wide variety of problems but, for themost complex approaches, there is no clear understanding of the way these algorithms complete the search. For that reason, in this work we exploit the probabilistic models that EDAs based on Bayesian networks are able to learn in order to provide new information about their behavior. Particularly, we analyze the k solutions with the highest probability in the distributions estimated during the search. In order to study the relationship between the probabilistic model and the fitness function, we focus on calculating, for the k most probable solutions (MPSs), the probability values, the function values and the correlation between both sets of values at each step of the algorithm. Furthermore, the objective functions of the k MPSs are contrasted with the k best individuals in the population. We complete the analysis by calculating the position of the optimum in the k MPSs during the search and the genotypic diversity of these solutions. We carry out the analysis by optimizing functions of different natures such as Trap5, two variants of Ising spin glass and Max-SAT. The results not only show information about the relationship between the probabilistic model and the fitness function, but also allow us to observe characteristics of the search space, the quality of the setup of the parameters and even distinguish between successful and unsuccessful runs. |
BibTeX:
@inproceedings{Echegoyen_et_al:2010a, author = {C. Echegoyen and A. Mendiburu and R. Santana and J. A. Lozano}, title = {Analyzing the k most probable solutions in EDAs based on Bayesian networks}, booktitle = {Exploitation of Linkage Learning in Evolutionary Algorithms}, publisher = {Springer}, year = {2010}, pages = {163-189}, url = {http://dx.doi.org/10.1007/978-3-642-12834-9_8} } |
Santana R, Larrañaga P and Lozano JA (2010), "Learning factorizations in estimation of distribution algorithms using affinity propagation", Evolutionary Computation. Vol. 18(4), pp. 515-546. |
Abstract: Estimation of distribution algorithms (EDAs) that use marginal product model factorizations have been widely applied to a broad range of mainly binary optimization problems. In this paper, we introduce the affinity propagation EDA (AffEDA) which learns a marginal product model by clustering a matrix of mutual information learned from the data using a very efficient message-passing algorithm known as affinity propagation. The introduced algorithm is tested on a set of binary and nonbinary decomposable functions and using a hard combinatorial class of problem known as the HP protein model. The results show that the algorithm is a very efficient alternative to other EDAs that use marginal product model factorizations such as the extended compact genetic algorithm (ECGA) and improves the quality of the results achieved by ECGA when the cardinality of the variables is increased. |
BibTeX:
@article{Santana_et_al:2009c, author = {R. Santana and P. Larrañaga and J. A. Lozano}, title = {Learning factorizations in estimation of distribution algorithms using affinity propagation}, journal = {Evolutionary Computation}, year = {2010}, volume = {18}, number = {4}, pages = {515-546}, url = {http://www.mitpressjournals.org/doi/abs/10.1162/EVCO_a_00002} } |
Santana R, Bielza C, Larrañaga P, Lozano JA, Echegoyen C, Mendiburu A, Armañanzas R and Shakya S (2010), "Mateda-2.0: A MATLAB package for the implementation and analysis of estimation of distribution algorithms", Journal of Statistical Software. Vol. 35(7), pp. 1-30. American Statistical Association. |
Abstract: This paper describes Mateda-2.0, a MATLAB package for estimation of distribution algorithms (EDAs). This package can be used to solve single and multi-objective discrete and continuous optimization problems using EDAs based on undirected and directed probabilistic graphical models. The implementation contains several methods commonly employed by EDAs. It is also conceived as an open package to allow users to incorporate different combinations of selection, learning, sampling, and local search procedures. Additionally, it includes methods to extract, process and visualize the structures learned by the probabilistic models. This way, it can unveil previously unknown information about the optimization problem domain. Mateda-2.0 also incorporates a module for creating and validating function models based on the probabilistic models learned by EDAs. |
BibTeX:
@article{Santana_et_al:2009h, author = {R. Santana and C. Bielza and P. Larrañaga and J. A. Lozano and C. Echegoyen and A. Mendiburu and R. Armañanzas and S. Shakya}, title = {Mateda-2.0: A MATLAB package for the implementation and analysis of estimation of distribution algorithms}, journal = {Journal of Statistical Software}, publisher = {American Statistical Association}, year = {2010}, volume = {35}, number = {7}, pages = {1-30}, url = {http://www.jstatsoft.org/v35/i07} } |
Santana R, Bielza C and Larrañaga P (2010), "Synergies between network-based representations and probabilistic graphical modeling in the solution of problems from neuroscience", In Proceedings of the Twenty Third International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Córdoba, España Vol. 6098, pp. 149-158. Springer. |
Abstract: Neural systems network-based representations are useful tools to analyze numerous phenomena in neuroscience. Probabilistic graphical models (PGMs) give a concise and still rich representation of complex systems from different domains, including neural systems. In this paper we analyze the characteristics of a bidirectional relationship between networks-based representations and PGMs. We show the way in which this relationship can be exploited introducing a number of methods for the solution of classification, inference and optimization problems. To illustrate the applicability of the introduced methods, a number of problems from the field of neuroscience, in which ongoing research is conducted, are used. |
BibTeX:
@inproceedings{Santana_et_al:2010a, author = {R. Santana and Concha Bielza and Pedro Larrañaga}, editor = {N. García-Pedrajas et. al.}, title = {Synergies between network-based representations and probabilistic graphical modeling in the solution of problems from neuroscience}, booktitle = {Proceedings of the Twenty Third International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems}, publisher = {Springer}, year = {2010}, volume = {6098}, pages = {149-158}, url = {http://dx.doi.org/10.1007/978-3-642-13033-5_16} } |
Santana R, Bielza C and Larrañaga P (2010), "Using probabilistic dependencies improves the search of conductance-based compartmental neuron models", In Proceedings of the 8th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. Istanbul, Turkey Vol. 6023, pp. 170-181. Springer. |
Abstract: Conductance-based compartmental neuron models are traditionally used to investigate the electrophysiological properties of neurons. These models require a number of parameters to be adjusted to biological experimental data and this question can be posed as an optimization problem. In this paper we investigate the behavior of different estimation of distribution algorithms (EDAs) for this problem. We focus on studying the influence that the interactions between the neuron model conductances have in the complexity of the optimization problem. We support evidence that the use of these interactions during the optimization process can improve the EDA behavior. |
BibTeX:
@inproceedings{Santana_et_al:2010c, author = {R. Santana and C. Bielza and P. Larrañaga}, editor = {Clara Pizzuti and Marylyn D. Ritchie and Mario Giacobini}, title = {Using probabilistic dependencies improves the search of conductance-based compartmental neuron models}, booktitle = {Proceedings of the 8th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics}, publisher = {Springer}, year = {2010}, volume = {6023}, pages = {170-181}, url = {http://dx.doi.org/10.1007/978-3-642-12211-8_15} } |
Santana R, Mendiburu A, Zaitlen N, Eskin E and Lozano JA (2010), "Multi-marker tagging single nucleotide polymorphism selection using estimation of distribution algorithms", Artificial Intelligence in Medicine. Vol. 50, pp. 193-201. |
Abstract: Objectives This paper presents an optimization algorithm for the automatic selection of a minimal subset of tagging single nucleotide polymorphisms (SNPs). Methods and materials: The determination of the set of minimal tagging SNPs is approached as an optimization problem in which each tagged SNP can be covered by a single tagging SNP or by a pair of tagging SNPs. The problem is solved using an estimation of distribution algorithm (EDA) which takes advantage of the underlying topological structure defined by the SNP correlations to model the problem interactions. The EDA stochastically searches the constrained space of feasible solutions. It is evaluated across HapMap reference panel data sets. Results: The EDA was compared with a SAT solver, able to find the single-marker minimal tagging sets, and with the Tagger program. The percentage of reduction ranged from 10 to 43 percentage in the number of tagging SNPs of the minimal multi-marker tagging set found by the EDA with respect to the other algorithms. Conclusions: The introduced algorithm is effective for the identification of minimal multi-marker SNP sets, which considerably reduce the dimension of the tagging SNP set in comparison with single-marker sets. Other variants of the SNP problem can be treated following the same approach. |
BibTeX:
@article{Santana_et_al:2010d, author = {R. Santana and A. Mendiburu and N. Zaitlen and E. Eskin and J. A. Lozano}, title = {Multi-marker tagging single nucleotide polymorphism selection using estimation of distribution algorithms}, journal = {Artificial Intelligence in Medicine}, year = {2010}, volume = {50}, pages = {193-201}, url = {http://www.sciencedirect.com/science/article/pii/S0933365710000758} } |
Santana R, Bielza C and Larrañaga P (2010), "Network measures for re-using problem information in EDAs". Research Report at: Department of Artificial Intelligence, Faculty of Informatics, Technical University of Madrid., June, 2010. (UPM-FI/DIA/2010-3) |
Abstract: Probabilistic graphical models (PGMs) are used in estimation of distribution algorithms (EDAs) as a model of the search space. Graphical components of PGMs can be also analyzed as networks. In this paper we show that topological measures extracted from these networks capture characteristic information of the optimization problem. The measures can be also used to describe the EDA behavior. Using a simplified protein folding optimization problem, we show that the network information extracted from a set of problem instances can be effectively used to predict characteristics of similar instances. |
BibTeX:
@techreport{Santana_et_al:2010e, author = {R. Santana and C. Bielza and P. Larrañaga}, title = {Network measures for re-using problem information in EDAs}, school = {Department of Artificial Intelligence, Faculty of Informatics, Technical University of Madrid}, year = {2010}, number = {UPM-FI/DIA/2010-3}, url = {https://www.researchgate.net/publication/228738540_Network_measures_for_re-using_problem_information_in_EDAs} } |