| Astigarraga A, Arruti A, Muguerza J, Santana R, Martin JI and Sierra B (2014), "User Adapted Motor-Imaginary Brain-Computer Interface by means of EEG Channel Selection based on Estimation of Distributed Algorithms", Mathematical Problems in Engineering. (151329)
[Abstract] [BibTeX] [URL]
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| Abstract: Brain-Computer Interfaces (BCIs) have become a research field with interesting applications, and it can be inferred from published papers that different persons activate different parts of the brain to perform the same action. This paper presents a personalized interface design method, for electroencephalogram- (EEG-) based BCIs, based on channel selection. We describe a novel two-step method in which firstly a computationally inexpensive greedy algorithm finds an adequate search range; and, then, an Estimation of Distribution Algorithm (EDA) is applied in the reduced range to obtain the optimal channel subset. The use of the EDA allows us to select the most interacting channels subset, removing the irrelevant and noisy ones, thus selecting the most discriminative subset of channels for each user improving accuracy. The method is tested on the IIIa dataset from the BCI competition III. Experimental results show that the resulting channel subset is consistent with motor-imaginary-related neurophysiological principles and, on the other hand, optimizes performance reducing the number of channels. |
BibTeX:
@article{Astigarraga_et_al:2014,
author = {Astigarraga, Aitzol and Arruti, Andoni and Muguerza, Javier and Santana, Roberto and Martin, Jose I and Sierra, Basilio},
title = {User Adapted Motor-Imaginary Brain-Computer Interface by means of EEG Channel Selection based on Estimation of Distributed Algorithms},
journal = {Mathematical Problems in Engineering},
year = {2014},
number = {151329},
url = {https://www.hindawi.com/journals/mpe/2016/1435321/}
}
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| Karshenas H, Santana R, Bielza C and Larrañaga P (2014), "Multiobjective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables", IEEE Transactions on Evolutionary Computation. Vol. 18(4), pp. 519-542.
[Abstract] [BibTeX] [URL]
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| Abstract: This paper proposes a new multiobjective estimation of distribution algorithm (EDA) based on joint probabilistic modeling of objectives and variables. This EDA uses the multidimensional Bayesian network as its probabilistic model. In this way, it can capture the dependencies between objectives, variables and objectives, as well as the dependencies learned between variables in other Bayesian network-based EDAs. This model leads to a problem decomposition that helps the proposed algorithm find better tradeoff solutions to the multiobjective problem. In addition to Pareto set approximation, the algorithm is also able to estimate the structure of the multiobjective problem. To apply the algorithm to many-objective problems, the algorithm includes four different ranking methods proposed in the literature for this purpose. The algorithm is first applied to the set of walking fish group problems, and its optimization performance is compared with a standard multiobjective evolutionary algorithm and another competitive multiobjective EDA. The experimental results show that on several of these problems, and for different objective space dimensions, the proposed algorithm performs significantly better and on some others achieves comparable results when compared with the other two algorithms. The algorithm is then tested on the set of CEC09 problems, where the results show that multiobjective optimization based on joint model estimation is able to obtain considerably better fronts for some of the problems compared with the search based on conventional genetic operators in the state-of-the-art multiobjective evolutionary algorithms. |
BibTeX:
@article{Karshenas_et_al:2014,
author = {H. Karshenas and R. Santana and C. Bielza and P. Larrañaga},
title = {Multiobjective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables},
journal = {IEEE Transactions on Evolutionary Computation},
year = {2014},
volume = {18},
number = {4},
pages = {519--542},
url = {https://ieeexplore.ieee.org/document/6600837?arnumber=6600837}
}
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| Roman I, Mendiburu A, Santana R and Lozano JA (2014), "Dynamic Kernel Selection Criteria for Bayesian Optimization", In 2014 NIPS Workshop on Bayesian Optimization. , pp. 1-8.
[Abstract] [BibTeX] [URL]
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| Abstract: In Bayesian Optimization, when using a Gaussian Process prior, some kernels adapt better than others to the objective function. This research evaluates the possibility of dynamically changing the kernel function based on the probability of improvement. Five kernel selection strategies are proposed and tested in well known synthetic functions. According to our preliminary experiments, these methods can improve the efficiency of the search when the best kernel for the problem is unknown. |
BibTeX:
@inproceedings{Roman_et_al:2014,
author = {Roman, Ibai and Mendiburu, Alexander and Santana, Roberto and Lozano, Jose A},
title = {Dynamic Kernel Selection Criteria for Bayesian Optimization},
booktitle = {2014 NIPS Workshop on Bayesian Optimization},
year = {2014},
pages = {1--8},
url = {https://bayesopt.github.io/papers/2014/paper13.pdf}
}
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| Santana R, Mendiburu A and Lozano JA (2014), "Customized Selection in Estimation of Distribution Algorithms", In Proceedings of the 10th International Conference Simulated Evolution and Learning (SEAL-2014). , pp. 94-105. Springer.
[Abstract] [BibTeX] [URL]
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| Abstract: Selection plays an important role in estimation of distribution algorithms. It determines the solutions that will be modeled to represent the promising areas of the search space. There is a strong relationship between the strength of selection and the type and number of dependencies that are captured by the models. In this paper we propose to use different selection probabilities to learn the structural and parametric components of the probabilistic graphical models. Customized selection is introduced as a way to enhance the effect of model learning in the exploratory and exploitative aspects of the search. We use a benchmark of over 15,000 instances of a simplified protein model to illustrate the gains in using customized selection. |
BibTeX:
@incollection{Santana_e_al:2014,
author = {Santana, Roberto and Mendiburu, Alexander and Lozano, Jose A},
title = {Customized Selection in Estimation of Distribution Algorithms},
booktitle = {Proceedings of the 10th International Conference Simulated Evolution and Learning (SEAL-2014)},
publisher = {Springer},
year = {2014},
pages = {94--105},
url = {https://link.springer.com/chapter/10.1007/978-3-319-13563-2_9}
}
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| Santana R, McDonald RB and Katzgraber HG (2014), "A probabilistic evolutionary optimization approach to compute quasiparticle braids", In Proceedings of the 10th International Conference Simulated Evolution and Learning (SEAL-2014). , pp. 13-24. Springer.
[BibTeX] [URL]
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BibTeX:
@incollection{Santana_et_al::2014a,
author = {R. Santana and R. B. McDonald and H. G. Katzgraber},
title = {A probabilistic evolutionary optimization approach to compute quasiparticle braids},
booktitle = {Proceedings of the 10th International Conference Simulated Evolution and Learning (SEAL-2014)},
publisher = {Springer},
year = {2014},
pages = {13--24},
url = {https://arxiv.org/abs/1410.0602}
}
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