| Carrera D, Santana R and Lozano JA (2016), "Vine copula classifiers for the mind reading problem", Progress in Artificial Intelligence. , pp. 1-17. Springer.
[Abstract] [BibTeX] [URL]
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| Abstract: In this paper we introduce vine copulas to model probabilistic dependencies in supervised classification problems. Vine copulas allow the representation of the dependence structure of multidimensional distributions as a factorization of bivariate pair-copulas. The flexibility of this model lies in the fact that we can mix different types of pair-copulas in a factorization, which allows covering a wide range of types of dependencies, i.e., from independence to much more complex forms of bivariate correlations. This property motivates us to use vine copulas as classifiers, particularly for problems for which the type and strength of bivariate interactions between the variables show a great variability. This is the case of brain signal classification problems where information is represented as multiple time series, each one recorded from different brain region. Our experimental results on a real-word Mind Reading Problem reveal that vine copula-based classifiers perform competitively compared to the four best classification methods presented at the Mind Reading Challenge Competition 2011. |
BibTeX:
@article{Carrera_et_al:2016,
author = {Carrera, Diana and Santana, Roberto and Lozano, Jose A},
title = {Vine copula classifiers for the mind reading problem},
journal = {Progress in Artificial Intelligence},
publisher = {Springer},
year = {2016},
pages = {1--17},
url = {https://link.springer.com/article/10.1007/s13748-016-0095-z}
}
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| Garciarena U and Santana R (2016), "Evolutionary Optimization of Compiler Flag Selection by Learning and Exploiting Flags Interactions", In Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion. New York, NY, USA , pp. 1159-1166. ACM.
[Abstract] [BibTeX] [DOI] [URL]
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| Abstract: Compiler flag selection can be an effective way to increase the quality of executable code according to different code quality criteria. Evolutionary algorithms have been successfully applied to this optimization problem. However, previous approaches have only partially addressed the question of capturing and exploiting the interactions between compilation options to improve the search. In this paper we deal with this question comparing estimation of distribution algorithms (EDAs) and a traditional genetic algorithm approach. We show that EDAs that learn bivariate interactions can improve the results of GAs for some of the programs considered. We also show that the probabilistic models generated as a result of the search for optimal flag combinations can be used to unveil the (problem-dependent) interactions between the flags, allowing the user a more informed choice of compilation options. |
BibTeX:
@inproceedings{Garciarena_and_Santana:2016,
author = {Garciarena, Unai and Santana, Roberto},
title = {Evolutionary Optimization of Compiler Flag Selection by Learning and Exploiting Flags Interactions},
booktitle = {Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion},
publisher = {ACM},
year = {2016},
pages = {1159--1166},
url = {http://doi.acm.org/10.1145/2908961.2931696},
doi = {10.1145/2908961.2931696}
}
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| Martins MS, Delgado MR, Santana R, Lüders R, Gonçalves RA and Almeida CPd (2016), "HMOBEDA: Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm", In Proceedings of the 2016 on Genetic and Evolutionary Computation Conference. , pp. 357-364.
[Abstract] [BibTeX] [URL]
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| Abstract: Probabilistic modeling of selected solutions and incorporation of local search methods are approaches that can notably improve the results of multi-objective evolutionary algorithms (MOEAs). In the past, these approaches have been jointly applied to multi-objective problems (MOPs) with excellent results. In this paper, we introduce for the first time a joint probabilistic modeling of (1) local search methods with (2) decision variables and (3) the objectives in a framework named HMOBEDA. The proposed approach is compared with six evolutionary methods (including a modified version of NSGA-III, adapted to solve combinatorial optimization) on instances of the multi-objective knapsack problem with 3, 4, and 5 objectives. Results show that HMOBEDA is a competitive approach. It outperforms the other methods according to the hypervolume indicator. |
BibTeX:
@inproceedings{Martins_et_al:2016,
author = {Martins, Marcella SR and Delgado, Myriam RBS and Santana, Roberto and Lüders, Ricardo and Gonçalves, Richard Aderbal and Almeida, Carolina Paula de},
title = {HMOBEDA: Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm},
booktitle = {Proceedings of the 2016 on Genetic and Evolutionary Computation Conference},
year = {2016},
pages = {357--364},
url = {https://dl.acm.org/doi/10.1145/2908812.2908826}
}
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| Picek S, Santana R and Jakobovic D (2016), "Maximal nonlinearity in balanced boolean functions with even number of inputs, revisited", In 2016 IEEE Congress on Evolutionary Computation (CEC). , pp. 3222-3229.
[Abstract] [BibTeX] [URL]
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| Abstract: The problem of obtaining maximal nonlinearity in Boolean functions is well researched, both from the cryptographic and the evolutionary computation side. However, the results are still not conclusive enough to be able to show how good a heuristic approach is when tackling this problem. In this paper, we investigate how to obtain the maximal possible nonlinearity in balanced Boolean functions, but we also analyze how difficult is the problem itself. In order to do so, we conduct experiments with Estimation of distribution algorithms as well as the fitness landscape analysis and the deception analysis. Our results indicate that the first difficulties arise from the inappropriate fitness function and representation of solutions coupled with a huge search space. The fitness landscape analysis does not reveal any significant differences that could justify the assumed jump in problem difficulty when going from Boolean functions with 6 inputs to those with 8 inputs. Finally, we show that this problem is not order-1 deceptive. |
BibTeX:
@inproceedings{Picek_et_al:2016,
author = {Picek, Stjepan and Santana, Roberto and Jakobovic, Domagoj},
title = {Maximal nonlinearity in balanced boolean functions with even number of inputs, revisited},
booktitle = {2016 IEEE Congress on Evolutionary Computation (CEC)},
year = {2016},
pages = {3222--3229},
url = {https://ieeexplore.ieee.org/document/7744197}
}
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| Castro-Jr. OR, Santana R and Pozo A (2016), "C-Multi: A competent multi-swarm approach for many-objective problems", Neurocomputing. Vol. 180, pp. 68-78.
[Abstract] [BibTeX] [URL]
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| Abstract: One of the major research topics in the evolutionary multi-objective community is handling a large number of objectives also known as many-objective optimization problems (MaOPs). Most existing methodologies have demonstrated success for problems with two and three objectives but face significant challenges in many-objective optimization. To tackle these challenges, a hybrid multi-swarm algorithm called C-Multi was proposed in a previous work. The project of C-Multi is based on two phases; the first uses a unique particle swarm optimization (PSO) algorithm to discover different regions of the Pareto front. The second phase uses multiple swarms to specialize on a dedicate part. On each sub-swarm, an estimation of distribution algorithm (EDA) is used to focus on convergence to its allocated region. In this study, the influence of two critical components of C-Multi, the archiving method and the number of swarms, is investigated by empirical analysis. As a result of this investigation, an improved variant of C-Multi is obtained, and its performance is compared to I-Multi, a multi-swarm algorithm that has a similar approach but does not use EDAs. Empirical results fully demonstrate the superiority of our proposed method on almost all considered test instances. |
BibTeX:
@article{Rodrigues_et_al:2016,
author = {Olacir Rodrigues-Castro-Jr. and Roberto Santana and Aurora Pozo},
title = {C-Multi: A competent multi-swarm approach for many-objective problems},
journal = {Neurocomputing},
year = {2016},
volume = {180},
pages = {68--78},
url = {https://www.sciencedirect.com/science/article/pii/S0925231215016215}
}
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| Castro OR, Pozo A, Lozano JA and Santana R (2016), "Transfer weight functions for injecting problem information in the multi-objective CMA-ES", Memetic Computing. , pp. 1-28. Springer.
[Abstract] [BibTeX] [URL]
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| Abstract: The covariance matrix adaptation evolution strategy (CMA-ES) is one of the state-of-the-art evolutionary algorithms for optimization problems with continuous representation. It has been extensively applied to single-objective optimization problems, and different variants of CMA-ES have also been proposed for multi-objective optimization problems (MOPs). When applied to MOPs, the traditional steps of CMA-ES have to be modified to accommodate for multiple objectives. This fact is particularly evident when the number of objectives is higher than 3 and, with a high probability, all the solutions produced become non-dominated. An open question is to what extent information about the objective values of the non-dominated solutions can be injected in the CMA-ES model for a more effective search. In this paper, we investigate this general question using several metrics that describe the quality of the solutions already evaluated, different transfer weight functions, and a set of difficult benchmark instances including many-objective problems. We introduce a number of new strategies that modify how the probabilistic model is learned in CMA-ES. By conducting an exhaustive empirical analysis on two difficult benchmarks of many-objective functions we show that the proposed strategies to infuse information about the quality indicators into the learned models can achieve consistent improvements in the quality of the Pareto fronts obtained and enhance the convergence rate of the algorithm. Moreover, we conducted a comparison with a state-of-the-art algorithm from the literature, and achieved competitive results in problems with irregular Pareto fronts. |
BibTeX:
@article{Rodrigues_et_al:2016a,
author = {Castro, Olacir R and Pozo, Aurora and Lozano, Jose A and Santana, Roberto},
title = {Transfer weight functions for injecting problem information in the multi-objective CMA-ES},
journal = {Memetic Computing},
publisher = {Springer},
year = {2016},
pages = {1--28},
url = {https://link.springer.com/article/10.1007/s12293-016-0202-5}
}
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| Santana R, Zhu Z and Katzgraber HG (2016), "Evolutionary Approaches to Optimization Problems in Chimera Topologies", In Proceedings of the 2016 Conference on Genetic and Evolutionary Computation (GECCO-2016). , pp. 397-404.
[Abstract] [BibTeX] [URL]
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| Abstract: Chimera graphs define the topology of one of the first commercially available quantum computers. A variety of optimization problems have been mapped to this topology to evaluate the behavior of quantum enhanced optimization heuristics in relation to other optimizers, being able to efficiently solve problems classically to use them as benchmarks for quantum machines. In this paper we investigate for the first time the use of Evolutionary Algorithms (EAs) on Ising spin glass instances defined on the Chimera topology. Three genetic algorithms (GAs) and three estimation of distribution algorithms (EDAs) are evaluated over 1000 hard instances of the Ising spin glass constructed from Sidon sets. We focus on determining whether the information about the topology of the graph can be used to improve the results of EAs and on identifying the characteristics of the Ising instances that influence the success rate of GAs and EDAs. |
BibTeX:
@inproceedings{Santana_et_al:2016,
author = {Santana, Roberto and Zhu, Zheng and Katzgraber, Helmut G},
title = {Evolutionary Approaches to Optimization Problems in Chimera Topologies},
booktitle = {Proceedings of the 2016 Conference on Genetic and Evolutionary Computation (GECCO-2016)},
year = {2016},
pages = {397--404},
url = {https://arxiv.org/abs/1608.05105}
}
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| Santana R, Mendiburu A and Lozano JA (2016), "A review of message passing algorithms in estimation of distribution algorithms", Natural Computing. Vol. 15(1), pp. 165-180. Springer Netherlands.
[Abstract] [BibTeX] [URL]
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| Abstract: Message passing algorithms (MPAs) have been traditionally used as an inference method in probabilistic graphical models. Some MPA variants have recently been introduced in the field of estimation of distribution algorithms (EDAs) as a way to improve the efficiency of these algorithms. Multiple developments on MPAs point to an increasing potential of these methods for their application as part of hybrid EDAs. In this paper we review recent work on EDAs that apply MPAs and propose ways to further extend the useful synergies between MPAs and EDAs. Furthermore, we analyze some of the implications that MPA developments can have in their future application to EDAs and other evolutionary algorithms. |
BibTeX:
@article{Santana_et_al:2016a,
author = {Santana, Roberto and Mendiburu, Alexander and Lozano, Jose A},
title = {A review of message passing algorithms in estimation of distribution algorithms},
journal = {Natural Computing},
publisher = {Springer Netherlands},
year = {2016},
volume = {15},
number = {1},
pages = {165--180},
url = {https://link.springer.com/article/10.1007/s11047-014-9473-2}
}
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| Strickler A, Castro O, Pozo A and Santana R (2016), "Investigating selection strategies in multi-objective probabilistic model based algorithms", In 2016 5th Brazilian Conference on Intelligent Systems (BRACIS). Recife, Brazil , pp. 7-12.
[Abstract] [BibTeX] [URL]
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| Abstract: Recent advances on multi-objective evolutionary algorithm (MOEAs) have acknowledged the important role played by selection, replacement, and archiving strategies in the behavior of these algorithms. However, the influence of these methods has been scarcely investigated for the particular class of MOEAs that use probabilistic modeling of the solutions. In this paper we fill this void by proposing an analysis of the role of the aforementioned strategies on an extensive set of bi-objective functions. We focus on the class of algorithms that use Gaussian univariate marginal models, and study how typical selection and replacement strategies used together with this probabilistic model impact the behavior of the search. Our analysis is particularized for a set of bi-objective functions that exhibit a representative set of characteristics (e.g. decomposable, ill-conditioned, non-linear, etc.). The experimental results shows that MOEAs that use simple probabilistic modeling outperform traditional MOEAs based on crossover operators. |
BibTeX:
@inproceedings{Strickler_et_al:2016,
author = {Strickler, Andrei and Castro, Olacir and Pozo, Aurora and Santana, Roberto},
title = {Investigating selection strategies in multi-objective probabilistic model based algorithms},
booktitle = {2016 5th Brazilian Conference on Intelligent Systems (BRACIS)},
year = {2016},
pages = {7--12},
url = {https://ieeexplore.ieee.org/document/7839554}
}
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| Zangari-de-Souza M, Santana R, Mendiburu A and Pozo A (2016), "On the Design of Hard mUBQP Instances", In Proceedings of the 2016 on Genetic and Evolutionary Computation Conference. , pp. 421-428.
[Abstract] [BibTeX] [URL]
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| Abstract: This paper proposes a new method for the design and analysis of multi-objective unconstrained binary quadratic programming (mUBQP) instances, commonly used for testing discrete multi-objective evolutionary algorithms (MOEAs). These instances are usually generated considering the sparsity of the matrices and the correlation between objectives but randomly selecting the values for the matrix cells. Our hypothesis is that going beyond the use of objective correlations by considering different types of variables interactions in the generation of the instances can help to obtain more diverse problem benchmarks, comprising harder instances. We propose a parametric approach in which small building blocks of deceptive functions are planted into the matrices that define the mUBQP. The algorithm for creating the new instances is presented, and the difficulty of the functions is tested using variants of a decomposition-based MOEA. Our experimental results confirm that the instances generated by planting deceptive blocks require more function evaluations to be solved than instances generated using other methods. |
BibTeX:
@inproceedings{Zangari_et_al:2016,
author = {Zangari-de-Souza, Murilo and Santana, Roberto and Mendiburu, Alexander and Pozo, Aurora},
title = {On the Design of Hard mUBQP Instances},
booktitle = {Proceedings of the 2016 on Genetic and Evolutionary Computation Conference},
year = {2016},
pages = {421--428},
url = {https://dl.acm.org/doi/10.1145/2908812.2908889}
}
|