| Echegoyen C, Santana R, Mendiburu A and Lozano JA (2009), "Estudio de la probabilidad del óptimo en EDAs basados en redes Bayesianas", In Proceedings of the X Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB-2009). Thomson.
[BibTeX] |
BibTeX:
@inproceedings{Echegoyen_et_al:2008a,
author = {C. Echegoyen and R. Santana and A. Mendiburu and J. A. Lozano},
title = {Estudio de la probabilidad del óptimo en EDAs basados en redes Bayesianas},
booktitle = {Proceedings of the X Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB-2009)},
publisher = {Thomson},
year = {2009}
}
|
| Echegoyen C, Mendiburu A, Santana R and Lozano JA (2009), "Analyzing the probability of the optimum in EDAs based on Bayesian networks", In Proceedings of the 2009 Congress on Evolutionary Computation CEC-2009. Norway , pp. 1652-1659. IEEE Press. |
| Abstract: In this paper we quantitatively analyze the probability distributions generated by an EDA during the search. In particular, we record the probabilities to the optimal solution, the solution with the highest probability and that of the best individual of the population, when the EDA is solving a trap function. By using different structures in the probabilistic models we can analyze the influence of the structural model accuracy on the aforementioned probability values. In addition, the objective function values of these solutions are contrasted with their probability values in order to study the connection between the function and the probabilistic model. The results provide new information about the behavior of the EDAs and they open a discussion regarding which are the minimum (in)dependences necessary to reach the optimum. |
BibTeX:
@inproceedings{Echegoyen_et_al:2009,
author = {C. Echegoyen and A. Mendiburu and R. Santana and J. A. Lozano},
title = {Analyzing the probability of the optimum in EDAs based on Bayesian networks},
booktitle = {Proceedings of the 2009 Congress on Evolutionary Computation CEC-2009},
publisher = {IEEE Press},
year = {2009},
pages = {1652--1659},
url = {http://dx.doi.org/10.1109/CEC.2009.4983140}
}
|
| Echegoyen C, Mendiburu A, Santana R and Lozano JA (2009), "A quantitative analysis of estimation of distribution algorithms based on Bayesian networks". Research Report at: Department of Computer Science and Artificial Intelligence, University of the Basque Country., October, 2009. (EHU-KZAA-IK-3) |
| Abstract: The successful application of estimation of distribution algorithms (EDAs) to solve different kinds of problems has reinforced their candidature as promising black-box optimization tools. However, their internal behavior is still not completely understood and therefore it is necessary to work in this direction in order to advance their development. This paper presents a new methodology of analysis which provides new information about the behavior of EDAs by quantitatively analyzing the probabilistic models learned during the search. We particularly focus on calculating the probabilities of the optimal solutions, the most probable solution given by the model and the best individual of the population at each step of the algorithm. We carry out the analysis by optimizing functions of different nature such as Trap5, two variants of Ising spin glass and Max-SAT. By using different structures in the probabilistic models, we also analyze the influence of the structural model accuracy in the quantitative behavior of EDAs. In addition, the objective function values of our analyzed key solutions are contrasted with their probability values in order to study the connection between function and probabilistic models. The results not only show information about the EDA behavior, but also about the quality of the optimization process and setup of the parameters, the relationship between the probabilistic model and the fitness function, and even about the problem itself. Furthermore, the results allow us to discover common patterns of behavior in EDAs and propose new ideas in the development of this type of algorithms. |
BibTeX:
@techreport{Echegoyen_et_al:2009a,
author = {C. Echegoyen and A. Mendiburu and R. Santana and J. A. Lozano},
title = {A quantitative analysis of estimation of distribution algorithms based on Bayesian networks},
school = {Department of Computer Science and Artificial Intelligence, University of the Basque Country},
year = {2009},
number = {EHU-KZAA-IK-3},
url = {https://addi.ehu.es/handle/10810/4626}
}
|
| Santana R, Bielza C, Lozano JA and Larrañaga P (2009), "Mining probabilistic models learned by EDAs in the optimization of multi-objective problems", In Proceedings of the Genetic and Evolutionary Computation Conference GECCO-2009. New York, NY, USA , pp. 445-452. ACM. |
| Abstract: One of the uses of the probabilistic models learned by estimation of distribution algorithms is to reveal previous unknown information about the problem structure. In this paper we investigate the mapping between the problem structure and the dependencies captured in the probabilistic models learned by EDAs for a set of multi-objective satisfiability problems. We present and discuss the application of different data mining and visualization techniques for processing and visualizing relevant information from the structure of the learned probabilistic models. We show that also in the case of multi-objective optimization problems, some features of the original problem structure can be translated to the probabilistic models and unveiled by using algorithms that mine the model structures. |
BibTeX:
@inproceedings{Santana_et_al:2009,
author = {R. Santana and C. Bielza and J. A. Lozano and P. Larrañaga},
title = {Mining probabilistic models learned by EDAs in the optimization of multi-objective problems},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference GECCO-2009},
publisher = {ACM},
year = {2009},
pages = {445-452},
url = {http://dl.acm.org/citation.cfm?id=1569963}
}
|
| Santana R, Echegoyen C, Mendiburu A, Bielza C, Lozano JA, Larrañaga P, Armañanzas R and Shakya S (2009), "MATEDA: A suite of EDA programs in Matlab". Research Report at: Department of Computer Science and Artificial Intelligence, University of the Basque Country., February, 2009. (EHU-KZAA-IK-2/09) |
| Abstract: This paper describes MATEDA-2.0, a suite of programs in Matlab for estimation of distribution algorithms. The package allows the optimization of single and multi-objective problems with estimation of distribution algorithms (EDAs) based on undirected graphical models and Bayesian networks. The implementation is conceived for allowing the incorporation by the user of different combinations of selection, learning, sampling, and local search procedures. Other included methods allow the analysis of the structures learned by the probabilistic models, the visualization of particular features of these structures and the use of the probabilistic models as fitness modeling tools. |
BibTeX:
@techreport{Santana_et_al:2009a,
author = {R. Santana and C. Echegoyen and A. Mendiburu and C. Bielza and J. A. Lozano and P. Larrañaga and R. Armañanzas and S. Shakya},
title = {MATEDA: A suite of EDA programs in Matlab},
school = {Department of Computer Science and Artificial Intelligence, University of the Basque Country},
year = {2009},
number = {EHU-KZAA-IK-2/09},
url = {http://hdl.handle.net/10810/4622}
}
|
| Santana R, Larrañaga P and Lozano JA (2009), "Research topics on discrete estimation of distribution algorithms", Memetic Computing. Vol. 1(1), pp. 35-54. |
| Abstract: In this paper, we identify a number of topics relevant for the improvement and development of discrete estimation of distribution algorithms. Focusing on the role of probability distributions and factorizations in estimation of distribution algorithms, we present a survey of current challenges where further research must provide answers that extend the potential and applicability of these algorithms. In each case we state the research topic 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:
@article{Santana_et_al:2009b,
author = {R. Santana and P. Larrañaga and J. A. Lozano},
title = {Research topics on discrete estimation of distribution algorithms},
journal = {Memetic Computing},
year = {2009},
volume = {1},
number = {1},
pages = {35-54},
url = {http://dx.doi.org/10.1007/s12293-008-0002-7}
}
|
| Santana R, Mendiburu A, Zaitlen N, Eskin E and Lozano JA (2009), "On the application of estimation of distribution algorithms to multi-marker tagging SNP selection". Research Report at: Department of Computer Science and Artificial Intelligence, University of the Basque Country., July, 2009. (EHU-KZAA-IK-4/09) |
| Abstract: This paper presents an algorithm for the automatic selection of a minimal subset of tagging single nucleotide polymorphisms (SNPs) using an estimation of distribution algorithm (EDA). The EDA stochastically searches the constrained space of possible feasible solutions and takes advantage of the underlying topological structure defined by the SNP correlations to model the problem interactions. The algorithm is evaluated across the HapMap reference panel data sets. 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. New reduced tagging sets are obtained for all the HapMap SNP regions considered. We also show that the information extracted from the interaction graph representing the correlations between the SNPs can help to improve the efficiency of the optimization algorithm. |
BibTeX:
@techreport{Santana_et_al:2009d,
author = {R. Santana and A. Mendiburu and N. Zaitlen and E. Eskin and J. A. Lozano},
title = {On the application of estimation of distribution algorithms to multi-marker tagging SNP selection},
school = {Department of Computer Science and Artificial Intelligence, University of the Basque Country},
year = {2009},
number = {EHU-KZAA-IK-4/09},
url = {https://addi.ehu.es/bitstream/handle/10810/4623/tr09-00-4.pdf?sequence=1&isAllowed=y}
}
|