Echegoyen C, Zhang Q, Mendiburu A, Santana R and Lozano JA (2011), "On the limits of effectiveness in estimation of distribution algorithms", In Proceedings of the 2011 Congress on Evolutionary Computation CEC2007. , pp. 15731580. IEEE Press.
[Abstract] [BibTeX] [URL]

Abstract: Which problems a search algorithm can effectively solve is a fundamental issue that plays a key role in understanding and developing algorithms. In order to study the ability limit of estimation of distribution algorithms (EDAs), this paper experimentally tests three different EDA implementations on a sequence of additively decomposable functions (ADFs) with an increasing number of interactions among binary variables. The results show that the ability of EDAs to solve problems could be lost immediately when the degree of variable interaction is larger than a threshold. We argue that this phasetransition phenomenon is closely related with the computational restrictions imposed in the learning step of this type of algorithms. Moreover, we demonstrate how the use of unrestricted Bayesian networks rapidly becomes inefficient as the number of subfunctions in an ADF increases. The study conducted in this paper is useful in order to identify patterns of behavior in EDAs and, thus, improve their performances. 
BibTeX:
@inproceedings{Echegoyen_et_al:2011,
author = {C. Echegoyen and Q. Zhang and A. Mendiburu and R. Santana and J. A. Lozano},
title = {On the limits of effectiveness in estimation of distribution algorithms},
booktitle = {Proceedings of the 2011 Congress on Evolutionary Computation CEC2007},
publisher = {IEEE Press},
year = {2011},
pages = {15731580},
url = {http://dx.doi.org/10.1109/CEC.2011.5949803}
}

Echegoyen C, Zhang Q, Mendiburu A, Santana R and Lozano JA (2011), "Analyzing limits of effectiveness in different implementations of estimation of distribution algorithms networks". Research Report at: Department of Computer Science and Artificial Intelligence, University of the Basque Country. (EHUKZAA)
[Abstract] [BibTeX] [URL]

Abstract: Conducting research in order to know the range of problems in which a search algorithm is effective constitutes a fundamental issue to understand the algorithm and to continue the development of new techniques. In this work, by progressively increasing the degree of interaction in the problem, we study to what extent different EDA implementations are able to reach the optimal solutions. Specifically, we deal with additively decomposable functions whose complexity essentially depends on the number of subfunctions added. With the aim of analyzing the limits of this type of algorithms, we take into account three common EDA implementations that only differ in the complexity of the probabilistic model. The results show that the ability of EDAs to solve problems quickly vanishes after certain degree of interaction with a phasetransition effect. This collapse of performance is closely related with the computational restrictions that this type of algorithms have to impose in the learning step in order to be efficiently applied. Moreover, we show how the use of unrestricted Bayesian networks to solve the problems rapidly becomes inefficient as the number of subfunctions increases. The results suggest that this type of models might not be the most appropriate tool for the the development of new techniques that solve problems with increasing degree of interaction. In general, the experiments proposed in the present work allow us to identify patterns of behavior in EDAs and provide new ideas for the analysis and development of this type of algorithms. 
BibTeX:
@techreport{Echegoyen_et_al:2011a,
author = {C. Echegoyen and Q. Zhang and A. Mendiburu and R. Santana and J. A. Lozano},
title = {Analyzing limits of effectiveness in different implementations of estimation of distribution algorithms networks},
school = {Department of Computer Science and Artificial Intelligence, University of the Basque Country},
year = {2011},
number = {EHUKZAA},
url = {https://addi.ehu.es/handle/10810/4763}
}

Karshenas H, Santana R, Bielza C and Larrañaga P (2011), "Multiobjective optimization with joint probabilistic modeling of objectives and variables", In Evolutionary MultiCriterion Optimization: Sixth International Conference, EMO 2011. , pp. 298312. Springer BerlinHeidelberg.
[Abstract] [BibTeX] [URL]

Abstract: The objective values information can be incorporated into the evolutionary algorithms based on probabilistic modeling in order to capture the relationships between objectives and variables. This paper investigates the effects of joining the objective and variable information on the performance of an estimation of distribution algorithm for multiobjective optimization. A joint Gaussian Bayesian network of objectives and variables is learnt and then sampled using the information about currently best obtained objective values as evidence. The experimental results obtained on a set of multiobjective functions and in comparison to two other competitive algorithms are presented and discussed. 
BibTeX:
@inproceedings{Karshenas_et_al:2011,
author = {H. Karshenas and R. Santana and C. Bielza and P. Larrañaga},
title = {Multiobjective optimization with joint probabilistic modeling of objectives and variables},
booktitle = {Evolutionary MultiCriterion Optimization: Sixth International Conference, EMO 2011},
publisher = {Springer BerlinHeidelberg},
year = {2011},
pages = {298312},
url = {http://dx.doi.org/10.1007/9783642198939_21}
}

Karshenas H, Santana R, Bielza C and Larrañaga P (2011), "Regularized Model Learning in Estimation of Distribution Algorithms for Continuous Optimization Problems". Research Report at: Department of Artificial Intelligence, Faculty of Informatics, Technical University of Madrid., January, 2011. (UPMFI/DIA/20111)
[Abstract] [BibTeX] [URL]

Abstract: Regularization is a wellknown technique in statistics for model estimation which is used to improve the generalization ability of the estimated model. Some of the regularization methods can also be used for variable selection that is especially useful in highdimensional problems. This paper studies the use of regularized model learning in estimation of distribution algorithms (EDAs) for continuous optimization based on Gaussian distributions. We introduce two approaches to the regularized model estimation and analyze their effect on the accuracy and computational complexity of model learning in EDAs. We then apply the proposed algorithms to a number of continuous optimization functions and compare their results with other Gaussian distributionbased EDAs. The results show that the optimization performance of the proposed RegEDAs is less affected by the increase in the problem size than other EDAs, and they are able to obtain significantly better optimization values for many of the functions in highdimensional settings. 
BibTeX:
@techreport{Karshenas_et_al:2011a,
author = {H. Karshenas and R. Santana and C. Bielza and P. Larrañaga},
title = {Regularized Model Learning in Estimation of Distribution Algorithms for Continuous Optimization Problems},
school = {Department of Artificial Intelligence, Faculty of Informatics, Technical University of Madrid},
year = {2011},
number = {UPMFI/DIA/20111},
url = {https://www.sciencedirect.com/science/article/pii/S1568494612005376}
}

LaTorre A, Muelas S, Pena J, Santana R, MerchanPerez A and Rodriguez J (2011), "A differential evolution algorithm for the detection of synaptic vesicles", In Evolutionary Computation (CEC), 2011 IEEE Congress on. , pp. 16871694.
[Abstract] [BibTeX] [URL]

Abstract: Neurotransmitters used by chemical synapses are stored in synaptic vesicles that accumulate in axon terminals. The number and position of these vesicles have been related to some important functional properties of the synapse. For this reason, an accurate mechanism for semiautomatically counting these small cellular structures will be of great help for neuroscientists. In this paper, we present a Differential Evolution algorithm that quantifies the number of synaptic vesicles in electron micrographs. The algorithm has been tested on several images that have been obtained from the somatosensory cortex of the rat and compared with some traditional approaches for detecting circular structures. Finally, the results have been validated by two independent expert anatomists. 
BibTeX:
@inproceedings{LaTorre_et_al:2011,
author = {LaTorre, A. and Muelas, S. and Pena, J.M. and Santana, R. and MerchanPerez, A. and Rodriguez, J.R.},
title = {A differential evolution algorithm for the detection of synaptic vesicles},
booktitle = {Evolutionary Computation (CEC), 2011 IEEE Congress on},
year = {2011},
pages = {16871694},
url = {http://dx.doi.org/10.1109/CEC.2011.5949818}
}

LozadaChang L and Santana R (2011), "Univariate marginal distribution algorithm dynamics for a class of parametric functions with unitation constraints", Information Sciences. Vol. 181(11), pp. 23402355.
[Abstract] [BibTeX] [URL]

Abstract: In this paper, we introduce a mathematical model for analyzing the dynamics of the univariate marginal distribution algorithm (UMDA) for a class of parametric functions with isolated global optima. We prove a number of results that are used to model the evolution of UMDA probability distributions for this class of functions. We show that a theoretical analysis can assess the effect of the function parameters on the convergence and rate of convergence of UMDA. We also introduce for the first time a long string limit analysis of UMDA. Finally, we relate the results to ongoing research on the application of the estimation of distribution algorithms for problems with unitation constraints. 
BibTeX:
@article{Lozada_and_Santana:2011,
author = {L. LozadaChang and R. Santana},
title = {Univariate marginal distribution algorithm dynamics for a class of parametric functions with unitation constraints},
journal = {Information Sciences},
year = {2011},
volume = {181},
number = {11},
pages = {23402355},
url = {http://dx.doi.org/10.1016/j.ins.2011.01.024}
}

Santana R, Muelas S, Latorre A and Peña JM (2011), "A direct optimization approach to the P300 speller", In Proceedings of the 2011 Genetic and Evolutionary Computation Conference GECCO2011. Dublin, Ireland , pp. 17471754.
[Abstract] [BibTeX] [URL]

Abstract: The P300 component of the brain eventrelatedpotential is one of the most used signals in brain computer interfaces (BCIs). One of the required steps for the application of the P300 paradigm is the identification of this component in the presence of stimuli. In this paper we propose a direct optimization approach to the P300 classification problem. A general formulation of the problem is introduced. Different classes of optimization algorithms are applied to solve the problem and the concepts of kbest and kworst ensembles of solutions are introduced as a way to improve the accuracy of single solutions. The introduced approaches are able to achieve a classification rate over 80 percentage on test data. 
BibTeX:
@inproceedings{Santana_et_al:2011,
author = {R. Santana and S. Muelas and A. Latorre and J. M. Peña},
title = {A direct optimization approach to the P300 speller},
booktitle = {Proceedings of the 2011 Genetic and Evolutionary Computation Conference GECCO2011},
year = {2011},
pages = {17471754},
url = {http://dl.acm.org/citation.cfm?id=2001811}
}

Santana R, Bielza C and Larrañaga P (2011), "Optimizing brain networks topologies using multiobjective evolutionary computation", Neuroinformatics. Vol. 9(1), pp. 319.
[Abstract] [BibTeX] [URL]

Abstract: The analysis of brain network topological features has served to better understand these networks and reveal particular characteristics of their functional behavior. The distribution of brain network motifs is particularly useful for detecting and describing differences between brain networks and random and computationally optimized artificial networks. In this paper we use a multiobjective evolutionary optimization approach to generate optimized artificial networks that have a number of topological features resembling brain networks. The Pareto set approximation of the optimized networks is used to extract network descriptors that are compared to brain and random network descriptors. To analyze the networks, the clustering coefficient, the average path length, the modularity and the betweenness centrality are computed. We argue that the topological complexity of a brain network can be estimated using the number of evaluations needed by an optimization algorithm to output artificial networks of similar complexity. For the analyzed network examples, our results indicate that while original brain networks have a reduced structural motif number and a high functional motif number, they are not optimal with respect to these two topological features. We also investigate the correlation between the structural and functional motif numbers, the average path length and the clustering coefficient in random, optimized and brain networks. 
BibTeX:
@article{Santana_et_al:2011a,
author = {R. Santana and C. Bielza and P. Larrañaga},
title = {Optimizing brain networks topologies using multiobjective evolutionary computation},
journal = {Neuroinformatics},
year = {2011},
volume = {9},
number = {1},
pages = {319},
url = {http://dx.doi.org/10.1007/s1202101090857}
}

Santana R, Bielza C and Larrañaga P (2011), "Affinity propagation enhanced by estimation of distribution algorithms", In Proceedings of the 2011 Genetic and Evolutionary Computation Conference GECCO2011. Dublin, Ireland , pp. 331338.
[Abstract] [BibTeX] [URL]

Abstract: Tumor classification based on gene expression data can be applied to set appropriate medical treatment according to the specific tumor characteristics. In this paper we propose the use of estimation of distribution algorithms (EDAs) to enhance the performance of affinity propagation (AP) in classification problems. AP is an efficient clustering algorithm based on messagepassing methods and which automatically identifies exemplars of each cluster. We introduce an EDAbased procedure to compute the preferences used by the AP algorithm. Our results show that AP performance can be notably improved by using the introduced approach. Furthermore, we present evidence that classification of new data is improved by employing previously identified exemplars with only minor decrease in classification accuracy. 
BibTeX:
@inproceedings{Santana_et_al:2011b,
author = {R. Santana and C. Bielza and P. Larrañaga},
title = {Affinity propagation enhanced by estimation of distribution algorithms},
booktitle = {Proceedings of the 2011 Genetic and Evolutionary Computation Conference GECCO2011},
year = {2011},
pages = {331338},
url = {http://dl.acm.org/citation.cfm?id=2001622}
}

Santana R, Karshenas H, Bielza C and Larrañaga P (2011), "Regularized korder Markov models in EDAs", In Proceedings of the 2011 Genetic and Evolutionary Computation Conference GECCO2011. Dublin, Ireland , pp. 593600.
[Abstract] [BibTeX] [URL]

Abstract: korder Markov models have been introduced to estimation of distribution algorithms (EDAs) to solve a particular class of optimization problems in which each variable depends on its previous k variables in a given, fixed order. In this paper we investigate the use of regularization as a way to approximate korder Markov models when k is increased. The introduced regularized models are used to balance the complexity and accuracy of the korder Markov models. We investigate the behavior of the EDAs in several instances of the hydrophobicpolar (HP) protein problem, a simplified protein folding model. Our preliminary results show that EDAs that use regularized approximations of the korder Markov models offer a good compromise between complexity and efficiency, and could be an appropriate choice when the number of variables is increased. 
BibTeX:
@inproceedings{Santana_et_al:2011c,
author = {R. Santana and H. Karshenas and C. Bielza and P. Larrañaga},
title = {Regularized korder Markov models in EDAs},
booktitle = {Proceedings of the 2011 Genetic and Evolutionary Computation Conference GECCO2011},
year = {2011},
pages = {593600},
url = {http://dl.acm.org/citation.cfm?id=2001658}
}

Santana R, Karshenas H, Bielza C and Larrañaga P (2011), "Quantitative genetics in multiobjective optimization algorithms: From useful insights to effective methods", In Proceedings of the 2011 Genetic and Evolutionary Computation Conference GECCO2011. Dublin, Ireland , pp. 9192.
[Abstract] [BibTeX] [URL]

Abstract: This paper shows that statistical algorithms proposed for the quantitative trait loci (QTL) mapping problem, and the equation of the multivariate response to selection can be of application in multiobjective optimization. We introduce the conditional dominance relationships between the objectives and propose the use of results from QTL analysis and Gmatrix theory to the analysis of multiobjective evolutionary algorithms (MOEAs). 
BibTeX:
@inproceedings{Santana_et_al:2011d,
author = {R. Santana and H. Karshenas and C. Bielza and P. Larrañaga},
title = {Quantitative genetics in multiobjective optimization algorithms: From useful insights to effective methods},
booktitle = {Proceedings of the 2011 Genetic and Evolutionary Computation Conference GECCO2011},
year = {2011},
pages = {9192},
url = {http://dl.acm.org/citation.cfm?id=2001911}
}

Santana R, Bielza C and Larrañaga P (2011), "An ensemble of classifiers approach with multiple sources of information", In Proceedings of ICANN/PASCAL2 Challenge: MEG Mind Reading. , pp. 2530. Aalto University.
[Abstract] [BibTeX] [URL]

Abstract: This paper describes the main characteristics of our approach to the ICANN2011 Mind reading from MEG  PASCAL Challenge. The distinguished features of our method are: 1) The use of different sources of information as input to the classifiers. We simultaneously use information coming from raw data, channels correlations, mutual information between channels, and channel interactions graphs as features for the classifiers. 2) The use of ensemble of classifiers based on regularized multilogistic regression, regression trees, and an affinity propagation based classifier. 
BibTeX:
@inproceedings{Santana_et_al:2011f,
author = {R. Santana and C. Bielza and P. Larrañaga},
editor = {A. Klami},
title = {An ensemble of classifiers approach with multiple sources of information},
booktitle = {Proceedings of ICANN/PASCAL2 Challenge: MEG Mind Reading},
publisher = {Aalto University},
year = {2011},
pages = {2530},
url = {http://www.cis.hut.fi/icann2011/meg/megicann_santanaetal.pdf}
}

Santana R (2011), "Estimation of distribution algorithms: from available implementations to potential developments", In Companion proceedings of the 2011 Genetic and Evolutionary Computation Conference GECCO2011. Dublin, Ireland , pp. 679686.
[Abstract] [BibTeX] [URL]

Abstract: This paper focuses on the analysis of estimation of distribution algorithms (EDAs) software. The important role played by EDAs implementations in the usability and range of applications of these algorithms is considered. A survey of available EDA software is presented, and classifications based on the class of programming languages and design strategies used for their implementations are discussed. The paper also reviews different directions to improve current EDA implementations. A number of lines for further expanding the areas of application for EDAs software are proposed. 
BibTeX:
@inproceedings{Santana:2011,
author = {R. Santana},
title = {Estimation of distribution algorithms: from available implementations to potential developments},
booktitle = {Companion proceedings of the 2011 Genetic and Evolutionary Computation Conference GECCO2011},
year = {2011},
pages = {679686},
url = {http://dl.acm.org/citation.cfm?id=2002067}
}
