| Echegoyen C, Santana R and Lozano JA (2007), "Aprendizaje exacto de redes Bayesianas en algoritmos de estimación de distribuciones", In Actas de las Jornadas de Algoritmos Evolutivos y Metaheurísticas (JAEM I). , pp. 277-284. Thomson.
[BibTeX] |
BibTeX:
@inproceedings{Echegoyen_et_al:2007a,
author = {C. Echegoyen and R. Santana and J. A. Lozano},
editor = {E. Alba and F. Chicano and F. Herrera and F. Luna and G. Luque and A. J. Nebro},
title = {Aprendizaje exacto de redes Bayesianas en algoritmos de estimación de distribuciones},
booktitle = {Actas de las Jornadas de Algoritmos Evolutivos y Metaheurísticas (JAEM I)},
publisher = {Thomson},
year = {2007},
pages = {277--284}
}
|
| Höns R, Santana R, Larrañaga P and Lozano JA (2007), "Optimization by max-propagation using Kikuchi approximations". Research Report at: Department of Computer Science and Artificial Intelligence, University of the Basque Country., November, 2007. (EHU-KZAA-IK-2/07) |
| Abstract: In this paper we address the problem of using region-based approximations to find the optimal points of a given function. Our approach combines the use of Kikuchi approximations with the application of generalized belief propagation (GBP) using maximization instead of marginalization. The relationship between the fixed points of maximum GBP and the free energy is elucidated. A straightforward connection between the function to be optimized and the Kikuchi approximation (which holds only for maximum GBP, not for marginal GBP) is proven. Later, we show that maximum GBP can be combined with a dynamic programming algorithm to find the most probable configurations of a graphical model. We then analyze the dynamics of the procedure proposed and show how its different steps can be manipulated to influence the search for optimal solutions. |
BibTeX:
@techreport{Hoens_et_al:2007,
author = {R. Höns and R. Santana and P. Larrañaga and J. A. Lozano},
title = {Optimization by max-propagation using Kikuchi approximations},
school = {Department of Computer Science and Artificial Intelligence, University of the Basque Country},
year = {2007},
number = {EHU-KZAA-IK-2/07},
url = {https://www.researchgate.net/publication/228923642_Optimization_by_max-propagation_using_Kikuchi_approximations}
}
|
| Mendiburu A, Santana R, Bengoetxea E and Lozano J (2007), "A parallel framework for loopy belief propagation", In Proceedings of the Genetic and Evolutionary Computation Conference GECCO-2007. London, UK Vol. II, pp. 2843-2850. ACM Press. |
| Abstract: There are many innovative proposals introduced in the literature under the evolutionary computation field, from which estimation of distribution algorithms (EDAs) is one of them. Their main characteristic is the use of probabilistic models to represent the (in) dependencies between the variables of a concrete problem. Such probabilistic models have also been applied to the theoretical analysis of EDAs, providing a platform for the implementation of other optimization methods that can be incorporated into the EDA framework. Some of these methods, typically used for probabilistic inference, are belief propagation algorithms. In this paper we present a parallel approach for one of these inference-based algorithms, the loopy belief propagation algorithm for factor graphs. Our parallel implementation was designed to provide an algorithm that can be executed in clusters of computers or multiprocessors in order to reduce the total execution time. In addition, this framework was also designed as a flexible tool where many parameters, such as scheduling rules or stopping criteria, can be adjusted according to the requirements of each particular experiment and problem. |
BibTeX:
@inproceedings{Mendiburu_et_al:2007,
author = {A. Mendiburu and R. Santana and E. Bengoetxea and J. Lozano},
editor = {D. Thierens et al.},
title = {A parallel framework for loopy belief propagation},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference GECCO-2007},
publisher = {ACM Press},
year = {2007},
volume = {II},
pages = {2843--2850},
note = {Companion material},
url = {http://dl.acm.org/citation.cfm?id=1274084}
}
|
| Mendiburu A, Santana R and Lozano JA (2007), "Introducing belief propagation in estimation of distribution algorithms: A parallel framework". Research Report at: Department of Computer Science and Artificial Intelligence, University of the Basque Country., October, 2007. (EHU-KAT-IK-11/07) |
| Abstract: This paper incorporates Belief Propagation into an instance of Estimation of Distribution Algorithms called Estimation of Bayesian Networks Algorithm. Estimation of Bayesian Networks Algorithm learns a Bayesian network at each step. The objective of the proposed variation is to increase the search capabilities by extracting information of the, computationally costly to learn, Bayesian network. Belief Propagation applied to graphs with cycles, allows to find (with a low computational cost), in many scenarios, the point with the highest probability of a Bayesian network. We carry out some experiments to show how this modification can increase the potentialities of Estimation of Distribution Algorithms. Due to the computational time implied in the resolution of high dimensional optimization problems, we give a parallel version of the Belief Propagation algorithm for graphs with cycles and introduce it in a parallel framework for Estimation of Distribution Algorithms [13]. In addition we point out many ideas on how to incorporate Belief Propagation algorithms into Estimation Distribution Algorithms. |
BibTeX:
@techreport{Mendiburu_et_al:2007a,
author = {A. Mendiburu and R. Santana and J. A. Lozano},
title = {Introducing belief propagation in estimation of distribution algorithms: A parallel framework},
school = {Department of Computer Science and Artificial Intelligence, University of the Basque Country},
year = {2007},
number = {EHU-KAT-IK-11/07},
url = {https://www.researchgate.net/publication/228570851_Introducing_belief_propagation_in_estimation_of_distribution_algorithms_A_parallel_framework}
}
|
| Santana R, Larrañaga P and Lozano JA (2007), "Side Chain Placement Using Estimation of Distribution Algorithms", Artificial Intelligence in Medicine. Vol. 39(1), pp. 49-63. |
| Abstract: Objective This paper presents an algorithm for the solution of the side chain placement problem. Methods and materials: The algorithm combines the application of the Goldstein elimination criterion with the univariate marginal distribution algorithm (UMDA), which stochastically searches the space of possible solutions. The suitability of the algorithm to address the problem is investigated using a set of proteins. Results: For a number of difficult instances where inference algorithms do not converge, it has been shown that UMDA is able to find better structures. Conclusions: The results obtained show that the algorithm can achieve better structures than those obtained with other state-of-the-art methods like inference-based techniques. Additionally, a theoretical and empirical analysis of the computational cost of the algorithm introduced has been presented. |
BibTeX:
@article{Santana_et_al:2007,
author = {R. Santana and P. Larrañaga and J. A. Lozano},
title = {Side Chain Placement Using Estimation of Distribution Algorithms},
journal = {Artificial Intelligence in Medicine},
year = {2007},
volume = {39},
number = {1},
pages = {49--63},
url = {http://dx.doi.org/10.1016/j.artmed.2006.04.004}
}
|
| Santana R, Larrañaga P and Lozano JA (2007), "The role of a priori information in the minimization of contact potentials by means of estimation of distribution algorithms", In Proceedings of the Fifth European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. Valencia, Spain Vol. 4447, pp. 247-257. Springer. |
| Abstract: Directed search methods and probabilistic approaches have been used as two alternative ways for computational protein design. This paper presents a hybrid methodology that combines features from both approaches. Three estimation of distribution algorithms are applied to the solution of a protein design problem by minimization of contact potentials. The combination of probabilistic models able to represent probabilistic dependencies with the use of information about residues interactions in the protein contact graph is shown to improve the efficiency of search for the problems evaluated. |
BibTeX:
@inproceedings{Santana_et_al:2007b,
author = {R. Santana and P. Larrañaga and J. A. Lozano},
editor = {E. Marchiori and J. H. Moore and J. C. Rajapakse},
title = {The role of a priori information in the minimization of contact potentials by means of estimation of distribution algorithms},
booktitle = {Proceedings of the Fifth European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics},
publisher = {Springer},
year = {2007},
volume = {4447},
pages = {247-257},
url = {http://dx.doi.org/10.1007/978-3-540-71783-6_24}
}
|
| Santana R, Larrañaga P and Lozano JA (2007), "Challenges and open problems in discrete EDAs". Research Report at: Department of Computer Science and Artificial Intelligence, University of the Basque Country., October, 2007. (EHU-KZAA-IK-1/07) |
| Abstract: In this paper, we treat the identification of some of the problems that are relevant for the improvement and development of estimation of distribution algorithms. We present a survey of current challenges where further research must provide answers that extend the potential and applicability of the algorithms. In each case we state the problem and elaborate on the reasons that make it relevant for estimation of distribution algorithms. In some cases current work or possible alternatives for the solution of the problem are discussed. |
BibTeX:
@techreport{Santana_et_al:2007f,
author = {R. Santana and P. Larrañaga and J. A. Lozano},
title = {Challenges and open problems in discrete EDAs},
school = {Department of Computer Science and Artificial Intelligence, University of the Basque Country},
year = {2007},
number = {EHU-KZAA-IK-1/07},
url = {https://www.researchgate.net/publication/228927458_Challenges_and_open_problems_in_discrete_EDAs}
}
|
| Santana R, Lozano JA and Larrañaga P (2007), "Algoritmos de Estimación de Distribuciones para el problema de la determinación de la cadena lateral de una proteína", In Memorias del V Congreso Español de Algoritmos Evolutivos y Bioinspirados. Tenerife, Spain , pp. 663-670. |
| Abstract: Este trabajo analiza diferentes mejoras a una aplicación precedente de los algoritmos de estimación de distribuciones (EDA) al problema de la determinación de la cadena lateral de una proteína. EDAs simples como el UMDA han permitido la obtención de cadenas laterales con menor valor de energía que las obtenidas con otros métodos para diferentes secuencias. Sin embargo para algunas secuencias, los resultados obtenidos con el UMDA no mejoran aquellos obtenidos por otros métodos. Por lo tanto, un problema de interés consiste en el estudio de métodos para aumentar la eficacia de los EDAs en la obtención de cadenas laterales de proteínas. Presentamos dos posibles alternativas utilizadas con este propósito: el uso de algoritmos de optimización local y el aprendizaje de modelos probabilíticos que tienen en cuenta las interacciones entre las variables del problema. Los algoritmos introducidos son evaluados en un conjunto de instancias difíles. Los resultados obtenidos en este conjunto son superiores a los alcanzados con algoritmos de optimización basados en inferencia |
BibTeX:
@inproceedings{Santana_et_al:2007g,
author = {R. Santana and and J. A. Lozano and P. Larrañaga},
title = {Algoritmos de Estimación de Distribuciones para el problema de la determinación de la cadena lateral de una proteína},
booktitle = {Memorias del V Congreso Español de Algoritmos Evolutivos y Bioinspirados},
year = {2007},
pages = {663-670}
}
|