| Ceberio J, Santana R, Mendiburu A and Lozano JA (2015), "Mixtures of Generalized Mallows models for solving the quadratic assignment problem", In Proceedings of the IEEE Congress on Evolutionary Computation CEC 2015. Sendai, Japan , pp. 2050-2057. IEEE press. |
| Abstract: Recently, distance-based exponential probability models have demonstrated their validity in the context of estimation of distribution algorithms when solving permutationbased combinatorial optimisation problems. However, despite their successful performance, some of these models are unimodal, and, therefore, they might not be flexible enough to model the different modalities that may be represented in heterogeneous populations. In this paper, we address the particular case of the Generalized Mallows models under the Cayley distance, and propose mixtures of these models in the context of estimation of distribution algorithms. In order to evaluate their competitiveness, we considered the quadratic assignment problem as a case of study, and conducted experiments over a set of 90 instances for four different configurations of mixtures. Results reveal that the EDA with mixtures is able to outperform the Generalized Mallows EDA, especially in large instances. |
BibTeX:
@inproceedings{Ceberio_et_al:2015,
author = {J. Ceberio and R. Santana and A. Mendiburu and J. A. Lozano},
title = {Mixtures of Generalized Mallows models for solving the quadratic assignment problem},
booktitle = {Proceedings of the IEEE Congress on Evolutionary Computation CEC 2015},
publisher = {IEEE press},
year = {2015},
pages = {2050-2057},
url = {https://ieeexplore.ieee.org/document/7257137}
}
|
| Echegoyen C, Santana R, Mendiburu A and Lozano JA (2015), "Comprehensive characterization of the behaviors of estimation of distribution algorithms", Theoretical Computer Science. Vol. 598, pp. 64-86. Elsevier. |
| Abstract: Estimation of distribution algorithms (EDAs) are a successful example of how to use machine learning techniques for designing robust and efficient heuristic search algorithms. Understanding the relationship between EDAs and the space of optimization problems is a fundamental issue for the successful application of this type of algorithms. A step forward in this matter is to create a taxonomy of optimization problems according to the different behaviors that an EDA can exhibit. This paper substantially extends previous work in the proposal of a taxonomy of problems for univariate EDAs, mainly by generalizing those results to EDAs that are able to deal with multivariate dependences among the variables of the problem. Through the definition of an equivalence relation between functions, it is possible to partition the space of problems into equivalence classes in which the algorithm has the same behavior. We provide a sufficient and necessary condition to determine the equivalence between functions. This condition is based on a set of matrices which provides a novel encoding of the relationship between the function and the probabilistic model used by the algorithm. The description of the equivalent functions belonging to a class is studied in depth for EDAs whose probabilistic model is given by a chordal Markov network. Assuming this class of factorization, we unveil the intrinsic connection between the behaviors of EDAs and neighborhood systems defined over the search space. In addition, we carry out numerical simulations that effectively reveal the different behaviors of EDAs for the injective functions defined over the search space . Finally, we provide a novel approach to extend the analysis of equivalence classes to non-injective functions. |
BibTeX:
@article{Echegoyen_et_al:2015,
author = {Echegoyen, Carlos and Santana, Roberto and Mendiburu, Alexander and Lozano, Jose A},
title = {Comprehensive characterization of the behaviors of estimation of distribution algorithms},
journal = {Theoretical Computer Science},
publisher = {Elsevier},
year = {2015},
volume = {598},
pages = {64--86},
url = {https://www.sciencedirect.com/science/article/pii/S0304397515003229}
}
|
| Fritsche G, Strickler A, Pozo A and Santana R (2015), "Capturing Relationships in Multi-Objective Optimization", In Proceedings of the Brazilian Conference on Intelligent Systems (BRACIS 2015). Natal, Brazil , pp. 222-227. |
| Abstract: When applied to multi-objective problems (MOPs), evolutionary algorithms (EAs) can be noticeably improved by representing and exploiting information about the interactions between the components of the problem (variables and objectives). However, accurate detection of such relationships is a challenging question that involves other related issues such as finding the right metric for measuring the interaction, deciding about the timing for testing the interactions, and deciding on appropriate ways to represent the relationships found. In this paper we investigate the performance of three correlation measures (Kendall, Spearman and Pearson) in the context of multi-objective optimization using the MOEA/D-DRA algorithm. We analyze the accuracy of the measures at different stages of the evolution and for different types of relationships. Moreover, the paper proposes a meaningful way for visualizing and interpreting the captured interactions. |
BibTeX:
@inproceedings{Fritsche_et_al:2015,
author = {G. Fritsche and A. Strickler and A. Pozo and R. Santana},
title = {Capturing Relationships in Multi-Objective Optimization},
booktitle = {Proceedings of the Brazilian Conference on Intelligent Systems (BRACIS 2015)},
year = {2015},
pages = {222-227},
url = {https://ieeexplore.ieee.org/document/7424023}
}
|
| Picek S, McKay RI, Santana R and Gedeon TD (2015), "Fighting the Symmetries: The Structure of Cryptographic Boolean Function Spaces", In Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference. Madrid, Spain , pp. 457-464. |
| Abstract: We explore the problem space of maximum nonlinearity problems for balanced Boolean functions, examining the symmetry structure and fitness landscapes in the most common (bit string) representation. We present theoretical analyses of well understood aspects, together with detailed enumeration of the 4-bit problem, sampling of the 6-bit problem based on known optima, and sampling of the 8-bit problem based on its fittest known solutions. We show that these problems have many more symmetries than is generally noted, with implications for crossover and for distributional methods. We explore the large-scale plateau structure of the problem, with similar implications for local search. We show that symmetries yield additional information that may yield more effective search methods. |
BibTeX:
@inproceedings{Picek_et_al:2015,
author = {S. Picek and R. I. McKay and R. Santana and T. D. Gedeon},
title = {Fighting the Symmetries: The Structure of Cryptographic Boolean Function Spaces},
booktitle = {Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference},
year = {2015},
pages = {457--464},
url = {https://dl.acm.org/doi/10.1145/2739480.2754739}
}
|
| Roman I, Santana R, Mendiburu A and Lozano JA (2015), "Kernel hautapen dinamikoa Optimizazio Bayesiarrean", In I. Ikergazte: Nazioarteko ikerketa euskaraz. Kongresuko artikulu-bilduma. , pp. 690-695. |
| Abstract: Optimizazio Bayesiarra Prozesu Gaussiarren bitartez egiten denean, kernel batzuk beste batzuk baino hobeto egokitzen dira helburu-funtziora. Lan honetan, kernel hauek dinamikoki aldatzeko aukera aztertu dugu, hobekuntza-probabilitatean oinarriturik. Kernelen hautaketa aurrera eramateko bost irizpide aurkeztu eta helburu-funtzio ezagunen bidez ebaluatu ditugu. Lortutako emaitzen arabera, irizpide hauek algoritmoaren errendimendua hobetzen dute kernel egokiena aurretiaz ezezaguna denean. |
BibTeX:
@inproceedings{Roman_et_al:2015,
author = {Roman, Ibai and Santana, Roberto and Mendiburu, Alexander and Lozano, Jose Antonio},
title = {Kernel hautapen dinamikoa Optimizazio Bayesiarrean},
booktitle = {I. Ikergazte: Nazioarteko ikerketa euskaraz. Kongresuko artikulu-bilduma},
year = {2015},
pages = {690--695},
url = {http://www.ueu.eus/download/liburua/IKERGAZTE_0.2015.pdf}
}
|
| Santana R, Mendiburu A and Lozano JA (2015), "Evolving MNK-landscapes with structural constraints", In Proceedings of the IEEE Congress on Evolutionary Computation CEC 2015. Sendai, Japan , pp. 1364-1371. IEEE press. |
| Abstract: In this paper we propose a method for the generation of instances of the MNK-landscapes that maximize different measures used to characterize multi-objective problems. In contrast to previous approaches, the introduced algorithm works by modifying the neighborhood structure of the variables of the MNK-landscape while keeping fixed the local parameters of its functions. A variant of the algorithm is presented to deal with situations in which the exhaustive enumeration of search space is unfeasible. We show how the introduced method can be used to generate instances with an increased number of solutions in the Pareto front. Furthermore, we investigate whether direct optimization of the correlation between objectives can be used as an indirect method to increase the size of the Pareto fronts of the generated instances. |
BibTeX:
@inproceedings{Santana_et_al:2015,
author = {R. Santana and A. Mendiburu and J. A. Lozano},
title = {Evolving MNK-landscapes with structural constraints},
booktitle = {Proceedings of the IEEE Congress on Evolutionary Computation CEC 2015},
publisher = {IEEE press},
year = {2015},
pages = {1364-1371},
url = {https://ieeexplore.ieee.org/document/7257047}
}
|
| Santana R, Mendiburu A and Lozano JA (2015), "Multi-objective NM-landscapes", In Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference. Madrid, Spain , pp. 1477-1478. |
| Abstract: In this paper we propose an extension of the NM-landscape to model multi-objective problems (MOPs). We illustrate the link between the introduced model and previous landscapes used to study MOPs. Empirical results are presented for a variety of configurations of the multi-objective NM-landscapes. |
BibTeX:
@inproceedings{Santana_et_al:2015b,
author = {R. Santana and A. Mendiburu and J. A. Lozano},
title = {Multi-objective NM-landscapes},
booktitle = {Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference},
year = {2015},
pages = {1477--1478},
url = {https://dl.acm.org/doi/abs/10.1145/2739482.2764704}
}
|
| Santana R, Mendiburu A and Lozano JA (2015), "Computing factorized approximations of Pareto-fronts using mNM-landscapes and Boltzmann distributions", CoRR. Vol. abs/1512.03466 |
| Abstract: NM-landscapes have been recently introduced as a class of tunable rugged models. They are a subset of the general interaction models where all the interactions are of order less or equal M. The Boltzmann distribution has been extensively applied in single-objective evolutionary algorithms to implement selection and study the theoretical properties of model-building algorithms. In this paper we propose the combination of the multi-objective NM-landscape model and the Boltzmann distribution to obtain Pareto-front approximations. We investigate the joint effect of the parameters of the NM-landscapes and the probabilistic factorizations in the shape of the Pareto front approximations. |
BibTeX:
@article{Santana_et_al:2015c,
author = {Roberto Santana and Alexander Mendiburu and José Antonio Lozano},
title = {Computing factorized approximations of Pareto-fronts using mNM-landscapes and Boltzmann distributions},
journal = {CoRR},
year = {2015},
volume = {abs/1512.03466},
url = {http://arxiv.org/abs/1512.03466}
}
|
| Santana R, Mendiburu A and Lozano JA (2015), "Multi-view classification of psychiatric conditions based on saccades", Applied Soft Computing. Vol. 31, pp. 308-316. Elsevier. |
| Abstract: Early diagnosis of psychiatric conditions can be enhanced by taking into account eye movement behavior. However, the implementation of prediction algorithms which are able to assist physicians in the diagnostic is a difficult task. In this paper we propose, for the first time, an automatic approach for classification of multiple psychiatric conditions based on saccades. In particular, the goal is to classify 6 medical conditions: Alcoholism, Alzheimer's disease, opioid dependence (two groups of subjects with measurements respectively taken prior to and after administering synthetic opioid), Parkinson's disease, and Schizophrenia. Our approach integrates different feature spaces corresponding to complementary characterizations of the saccadic behavior. We define a multi-view model of saccades in which the feature representations capture characteristic temporal and amplitude patterns of saccades. Four of the current most advanced classification methods are used to discriminate among the psychiatric conditions and leave-one-out cross-validation is used to evaluate the classifiers. Classification accuracies well above the chance levels are obtained for the different classification tasks investigated. The confusion matrices reveal that it is possible to separate conditions into different groups. We conclude that using relatively simple descriptors of the saccadic behavior it is possible to simultaneously classify among 6 different types of psychiatric conditions. Conceptually, our multi-view classification method excels other approaches that focus on statistical differences in the saccadic behavior of cases and controls because it can be used for predicting unseen cases. Classification integrating different characterizations of the saccades can actually help to predict the conditions of new patients, opening the possibility to integrate automatic analysis of saccades as a practical procedure for differential diagnosis in Psychiatry. |
BibTeX:
@article{Santana_et_al:2015d,
author = {Santana, Roberto and Mendiburu, Alexander and Lozano, Jose A},
title = {Multi-view classification of psychiatric conditions based on saccades},
journal = {Applied Soft Computing},
publisher = {Elsevier},
year = {2015},
volume = {31},
pages = {308--316},
url = {https://www.sciencedirect.com/science/article/abs/pii/S1568494615001398}
}
|
| Santana R (2015), "Supervised classification of vowel speech imagery", In Actas de la XVI Conferencia CAEPIA. , pp. 951-961. |
| Abstract: Accurate and fast decoding of speech imagery from electroencephalographic (EEG) data could serve as a basis for a new generation of brain computer interfaces (BCIs), more portable and easier to use. However, decoding of speech imagery from EEG is a hard problem due to many factors. In this paper we focus on the analysis of the classification step of speech imagery decoding for a three-class vowel speech imagery recognition problem. We empirically show that different classification subtasks may require different classifiers for accurately decoding and obtain a classification accuracy that improves the best results previously published. We further investigate the relationship between the classifiers and different sets of features selected by the common spatial patterns method. Our results indicate that further improvement on BCIs based on speech imagery could be achieved by carefully selecting an appropriate combination of classifiers for the subtasks involved. |
BibTeX:
@inproceedings{Santana:2015,
author = {Santana, Roberto},
title = {Supervised classification of vowel speech imagery},
booktitle = {Actas de la XVI Conferencia CAEPIA},
year = {2015},
pages = {951--961},
url = {https://addi.ehu.es/bitstream/handle/10810/11154/tr13-2.pdf?sequence=1&isAllowed=y}
}
|
| Zangari-de-Souza M, Santana R, Pozo ATR and Mendiburu A (2015), "MOEA/D-GM: Using probabilistic graphical models in MOEA/D for solving combinatorial optimization problems", CoRR. Vol. abs/1511.05625 |
| Abstract: Evolutionary algorithms based on modeling the statistical dependencies (interactions) between the variables have been proposed to solve a wide range of complex problems. These algorithms learn and sample probabilistic graphical models able to encode and exploit the regularities of the problem. This paper investigates the effect of using probabilistic modeling techniques as a way to enhance the behavior of MOEA/D framework. MOEA/D is a decomposition based evolutionary algorithm that decomposes a multi-objective optimization problem (MOP) in a number of scalar single-objective subproblems and optimizes them in a collaborative manner. MOEA/D framework has been widely used to solve several MOPs. The proposed algorithm, MOEA/D using probabilistic Graphical Models (MOEA/D-GM) is able to instantiate both univariate and multi-variate probabilistic models for each subproblem. To validate the introduced framework algorithm, an experimental study is conducted on a multi-objective version of the deceptive function Trap5. The results show that the variant of the framework (MOEA/D-Tree), where tree models are learned from the matrices of the mutual information between the variables, is able to capture the structure of the problem. MOEA/D-Tree is able to achieve significantly better results than both MOEA/D using genetic operators and MOEA/D using univariate probability models, in terms of the approximation to the true Pareto front. |
BibTeX:
@article{Zangari_et_al:2015,
author = {Zangari-de-Souza, Murilo and Roberto Santana and Aurora Trinidad Ramirez Pozo and Alexander Mendiburu},
title = {MOEA/D-GM: Using probabilistic graphical models in MOEA/D for solving combinatorial optimization problems},
journal = {CoRR},
year = {2015},
volume = {abs/1511.05625},
url = {http://arxiv.org/abs/1511.05625}
}
|
| Zangari M, Santana R, Mendiburu A and Pozo A (2015), "PBIL: un mismo nombre para distintos algoritmos. un caso de estudio sobre un problema de optimización multi-objetivo", Proceedings of the Spanish Asociation in Artificial Inteligence (CAEPIA). , pp. 283-295. |
| Abstract: La optimización basada en el uso de técnicas que tratan de obtener un modelado probabilístico del espacio de búsqueda es una de las líneas de investigación que más ha avanzado en los últimos años en el ámbito de los Algoritmos Evolutivos. El algoritmo de aprendizaje incremental basado en poblaciones (PBIL) en una de las primeras propuestas introducidas en este campo, y ha sido ampliamente utilizado para resolver diferentes problemas de optimización. En este artículo queremos alertar de que las diferentes aplicaciones de PBIL publicadas en las literatura corresponden en realidad a dos implementaciones diferentes, en función de cómo ha sido definida la fase de aprendizaje del modelo. Estudiamos, analítica y empíricamente, el impacto que el método de aprendizaje utilizado tiene sobre el comportamiento del algoritmo. Como resultado de nuestra investigación, mostramos un caso de uso sobre un problema multi-objetivo, donde la elección de la variante PBIL puede producir resultados cualitativamente diferentes a lo largo del proceso de búsqueda. Utilizando diferentes versiones de PBIL como modelo base del algoritmo MOEA/D, evaluamos su efecto en la eficiencia de la búsqueda. |
BibTeX:
@inproceedings{Zangari_et_al:2015a,
author = {Zangari, M and Santana, R and Mendiburu, A and Pozo, A},
title = {PBIL: un mismo nombre para distintos algoritmos. un caso de estudio sobre un problema de optimización multi-objetivo},
journal = {Proceedings of the Spanish Asociation in Artificial Inteligence (CAEPIA)},
year = {2015},
pages = {283--295}
}
|
| Zheng W-L, Santana R and Lu B-L (2015), "Comparison of Classification Methods for EEG-based Emotion Recognition", In Proceedings of the 2015 World Congress on Medical Physics and Biomedical Engineering. , pp. 1184-1187. Springer. |
| Abstract: In this paper, we review different classification methods for emotion recognition from EEG and perform a detailed comparison of these methods on a relatively larger dataset of 45 experiments. We propose to combine the classifiers using stacking to improve the emotion recognition accuracies. Experimental results show that the combination of classifiers using stacking can achieve higher average accuracies than that without stacking methods. The weights derived from the classifiers are investigated to extract the relevant features and present their biological interpretation as critical brain areas and critical frequency bands. |
BibTeX:
@inproceedings{Zheng_et_al:2015,
author = {W.-L Zheng and R. Santana and B.-L Lu},
title = {Comparison of Classification Methods for EEG-based Emotion Recognition},
booktitle = {Proceedings of the 2015 World Congress on Medical Physics and Biomedical Engineering},
publisher = {Springer},
year = {2015},
pages = {1184--1187},
url = {https://link.springer.com/chapter/10.1007/978-3-319-19387-8_287}
}
|