| Carrera D, Santana R and Lozano JA (2018), "The Relationship Between Graphical Representations of Regular Vine Copulas and Polytrees", In Proceedings of the 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU-2018). Cadiz, Spain , pp. 678-690.
[Abstract] [BibTeX] [URL]
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| Abstract: Graphical models (GMs) are powerful statistical tools for modeling the (in)dependencies among random variables. In this paper, we focus on two different types of graphical models: R-vines and polytrees. Regarding the graphical representation of these models, the former uses a sequence of undirected trees with edges representing pairwise dependencies, whereas the latter uses a directed graph without cycles to encode independence relationships among the variables. The research problem we deal with is whether it is possible to build an R-vine that represents the largest number of independencies found in a polytree and vice versa. Two algorithms are proposed to solve this problem. One algorithm is used to induce an R-vine that represents in each tree the largest number of graphical independencies existing in a polytree. The other one builds a polytree that represents all the independencies found in the R-vine. Through simple examples, both procedures are illustrated. |
BibTeX:
@inproceedings{Carrera_et_al:2018,
author = {D. Carrera and R. Santana and J. A. Lozano},
title = {The Relationship Between Graphical Representations of Regular Vine Copulas and Polytrees},
booktitle = {Proceedings of the 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU-2018)},
year = {2018},
pages = {678--690},
url = {https://link.springer.com/chapter/10.1007/978-3-319-91479-4_56}
}
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| Cheriet A and Santana R (2018), "Modeling dependencies between decision variables and objectives with copula models", In Proceedings of the Genetic and Evolutionary Computation Conference Companion. , pp. 175-176.
[Abstract] [BibTeX] [URL]
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| Abstract: Probabilistic modeling in multi-objective optimization problems (MOPs) has mainly focused on capturing and representing the dependencies between decision variables in a set of selected solutions. Recently, some works have proposed to model also the dependencies between the objective variables, which are represented as random variables, and the decision variables. In this paper, we investigate the suitability of copula models to capture and exploit these dependencies in MOPs with a continuous representation. Copulas are very flexible probabilistic models able to represent a large variety of probability distributions. |
BibTeX:
@inproceedings{Cheriet_and_Santana:2018,
author = {Cheriet, Abdelhakim and Santana, Roberto},
title = {Modeling dependencies between decision variables and objectives with copula models},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
year = {2018},
pages = {175--176},
url = {https://dl.acm.org/doi/10.1145/3205651.3205694}
}
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| Garciarena U, Santana R and Mendiburu A (2018), "Evolved GANs for generating Pareto set approximations", In Proceedings of the 2018 on Genetic and Evolutionary Computation Conference. Kyoto, Japan , pp. 434-441. ACM.
[Abstract] [BibTeX] [URL]
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| Abstract: In machine learning, generative models are used to create data samples that mimic the characteristics of the training data. Generative adversarial networks (GANs) are neural-network based generator models that have shown their capacity to produce realistic samples in different domains. In this paper we propose a neuro-evolutionary approach for evolving deep GAN architectures together with the loss function and generator-discriminator synchronization parameters. We also propose the problem of Pareto set (PS) approximation as a suitable benchmark to evaluate the quality of neural-network based generators in terms of the accuracy of the solutions they generate. The covering of the Pareto front (PF) by the generated solutions is used as an indicator of the mode-collapsing behavior of GANs. We show that it is possible to evolve GANs that generate good PS approximations. Our method scales to up to 784 variables and that it is capable to create architecture transferable across dimensions and functions. |
BibTeX:
@inproceedings{Garciarena_et_al:2018,
author = {Unai Garciarena and Roberto Santana and Alexander Mendiburu},
title = {Evolved GANs for generating Pareto set approximations},
booktitle = {Proceedings of the 2018 on Genetic and Evolutionary Computation Conference},
publisher = {ACM},
year = {2018},
pages = {434--441},
url = {https://dl.acm.org/doi/abs/10.1145/3205455.3205550}
}
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| Garciarena U, Santana R and Mendiburu A (2018), "Expanding variational autoencoders for learning and exploiting latent representations in search distributions", In Proceedings of the 2018 on Genetic and Evolutionary Computation Conference. Kyoto, Japan , pp. 849-856. ACM.
[Abstract] [BibTeX] [URL]
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| Abstract: In the past, evolutionary algorithms (EAs) that use probabilistic modeling of the best solutions incorporated latent or hidden variables to the models as a more accurate way to represent the search distributions. Recently, a number of neural-network models that compute approximations of posterior (latent variable) distributions have been introduced. In this paper, we investigate the use of the variational autoencoder (VAE), a class of neural-network based generative model, for modeling and sampling search distributions as part of an estimation of distribution algorithm. We show that VAE can capture dependencies between decision variables and objectives. This feature is proven to improve the sampling capacity of model based EAs. Furthermore, we extend the original VAE model by adding a new, fitness-approximating network component. We show that it is possible to adapt the architecture of these models and we present evidence of how to extend VAEs to better fulfill the requirements of probabilistic modeling in EAs. While our results are not yet competitive with state of the art probabilistic-based optimizers, they represent a promising direction for the application of generative models within EDAs. |
BibTeX:
@inproceedings{Garciarena_et_al:2018a,
author = {Unai Garciarena and Roberto Santana and Alexander Mendiburu},
title = {Expanding variational autoencoders for learning and exploiting latent representations in search distributions},
booktitle = {Proceedings of the 2018 on Genetic and Evolutionary Computation Conference},
publisher = {ACM},
year = {2018},
pages = {849--856},
url = {https://dl.acm.org/doi/10.1145/3205455.3205645}
}
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| Garciarena U, Santana R and Mendiburu A (2018), "Analysis of the complexity of the automatic pipeline generation problem", In IEEE Congress on Evolutionary Computation (CEC-2018). Rio de Janeiro, Brazil , pp. 1-8.
[Abstract] [BibTeX] [URL]
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| Abstract: Strategies to automatize the selection of Machine Learning algorithms and their parameters have gained popularity in recent years, to the point of coining the term Automated Machine Learning. The most general version of this problem is pipeline optimization, which seeks an optimal combination of preprocessors and classifiers, along with their respective parameters. In this paper we address the pipeline generation problem from a broader perspective, that of problem complexity understanding as a previous step before proposing a solution, a comprehension we consider critical. The main contribution of this work is the analysis of the characteristics of the fitness landscape. Furthermore, a recently introduced tool for pipeline generation is used to investigate how an automatic method behaves in the previously studied landscape. Results show the high complexity of the pipeline optimization problem, as it can contain several disperse optima, and suffers from a severe lack of generality. Results also suggest that, depending on the dimensions of the search, the model quality target, and the data being modeled, basic search methods can produce results that match the user's expectations. |
BibTeX:
@inproceedings{Garciarena_et_al:2018b,
author = {Unai Garciarena and Roberto Santana and Alexander Mendiburu},
title = {Analysis of the complexity of the automatic pipeline generation problem},
booktitle = {IEEE Congress on Evolutionary Computation (CEC-2018)},
year = {2018},
pages = {1--8},
url = {https://ieeexplore.ieee.org/document/8477662}
}
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| Garciarena U, Mendiburu A and Santana R (2018), "Towards a more efficient representation of imputation operators in TPOT", CoRR. Vol. abs/1801.04407
[Abstract] [BibTeX] [URL]
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| Abstract: Automated Machine Learning encompasses a set of meta-algorithms intended to design and apply machine learning techniques (e.g., model selection, hyperparameter tuning, model assessment, etc.). TPOT, a software for optimizing machine learning pipelines based on genetic programming (GP), is a novel example of this kind of applications. Recently we have proposed a way to introduce imputation methods as part of TPOT. While our approach was able to deal with problems with missing data, it can produce a high number of unfeasible pipelines. In this paper we propose a strongly-typed-GP based approach that enforces constraint satisfaction by GP solutions. The enhancement we introduce is based on the redefinition of the operators and implicit enforcement of constraints in the generation of the GP trees. We evaluate the method to introduce imputation methods as part of TPOT. We show that the method can notably increase the efficiency of the GP search for optimal pipelines. |
BibTeX:
@article{Garciarena_et_al:2018c,
author = {Garciarena, Unai and Mendiburu, Alexander and Santana, Roberto},
title = {Towards a more efficient representation of imputation operators in TPOT},
journal = {CoRR},
year = {2018},
volume = {abs/1801.04407},
url = {http://arxiv.org/abs/1801.04407}
}
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| Irurozki E, Ceberio J, Santamaria J, Santana R and Mendiburu A (2018), "Algorithm 989: perm_mateda: A Matlab Toolbox of Estimation of Distribution Algorithms for Permutation-based Combinatorial Optimization Problems", ACM Transactions on Mathematical Software (TOMS). Vol. 44(4), pp. 47. ACM.
[Abstract] [BibTeX] [URL]
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| Abstract: Permutation problems are combinatorial optimization problems whose solutions are naturally codified as permutations. Due to their complexity, motivated principally by the factorial cardinality of the search space of solutions, they have been a recurrent topic for the artificial intelligence and operations research community. Recently, among the vast number of metaheuristic algorithms, new advances on estimation of distribution algorithms (EDAs) have shown outstanding performance when solving some permutation problems. These novel EDAs implement distance-based exponential probability models such as the Mallows and Generalized Mallows models. In this article, we present a Matlab package, perm_mateda, of estimation of distribution algorithms on permutation problems, which has been implemented as an extension to the Mateda-2.0 toolbox of EDAs. Particularly, we provide implementations of the Mallows and Generalized Mallows EDAs under the Kendall’s-τ, Cayley, and Ulam distances. In addition, four classical permutation problems have also been implemented: Traveling Salesman Problem, Permutation Flowshop Scheduling Problem, Linear Ordering Problem, and Quadratic Assignment Problem. |
BibTeX:
@article{Irurozki_et_al:2018,
author = {Ekhine Irurozki and Josu Ceberio and Josean Santamaria and Roberto Santana and Alexander Mendiburu},
title = {Algorithm 989: perm_mateda: A Matlab Toolbox of Estimation of Distribution Algorithms for Permutation-based Combinatorial Optimization Problems},
journal = {ACM Transactions on Mathematical Software (TOMS)},
publisher = {ACM},
year = {2018},
volume = {44},
number = {4},
pages = {47},
url = {https://dl.acm.org/doi/abs/10.1145/3206429}
}
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| Lima RHR, Fontoura V, Pozo ATR and Santana R (2018), "Evolutionary Multi-Objective System Design: Theory and Applications. Computer and Information Science Series" , pp. 151-119. Chapman /& Hall/CRC.
[Abstract] [BibTeX] [URL]
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| Abstract: his chapter briefly introduces the main aspects of the Protein Folding Problem and presents a reviews of the related works. It presents an overview of the Multi-Objective optimization context and the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Indicator-Based Algorithm (IBEA) algorithms are presented. The chapter presents the experimental benchmark and numerical results of the conducted experiments. It investigates the distance because more compact structures tend to have more hydrophobic contacts: as the lower the Euclidean distance between the amino acids is, the more compact the whole conformation will be. The prediction of protein structures has a wide range of important biotechnological and medical applications, e.g., design of new proteins and folds, structure-based drug design, and obtaining experimental structures from incomplete nuclear magnetic resonance data. The protein structures are the result of the so-called protein folding process in which the initially unfolded chain of amino acids is transformed into its final structure. |
BibTeX:
@inbook{Lima_et_al:2018,
author = {R. H. R. Lima and V. Fontoura and A. T. R. Pozo and R. Santana},
editor = {N. Nedjah and L. De-Macedo-Mourelle and H. Silverio-Lopes},
title = {Evolutionary Multi-Objective System Design: Theory and Applications. Computer and Information Science Series},
publisher = {Chapman /& Hall/CRC},
year = {2018},
pages = {151--119},
url = {https://www.taylorfrancis.com/chapters/edit/10.1201/9781315366845-8/multi-objective-approach-protein-structure-prediction-problem-ricardo-lima-vidal-fontoura-aurora-pozo-roberto-santana}
}
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| Martins MS, Delgado MR, Lüders R, Santana R, Gonçalves RA and Almeida CPd (2018), "Hybrid multi-objective Bayesian estimation of distribution algorithm: a comparative analysis for the multi-objective knapsack problem", Journal of Heuristics. Vol. 24(1), pp. 25-47.
[Abstract] [BibTeX] [URL]
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| Abstract: Nowadays, a number of metaheuristics have been developed for efficiently solving multi-objective optimization problems. Estimation of distribution algorithms are a special class of metaheuristic that intensively apply probabilistic modeling and, as well as local search methods, are widely used to make the search more efficient. In this paper, we apply a Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm (HMOBEDA) in multi and many objective scenarios by modeling the joint probability of decision variables, objectives, and the configuration parameters of an embedded local search (LS). We analyze the benefits of the online configuration of LS parameters by comparing the proposed approach with LS off-line versions using instances of the multi-objective knapsack problem with two to five and eight objectives. HMOBEDA is also compared with five advanced evolutionary methods using the same instances. Results show that HMOBEDA outperforms the other approaches including those with off-line configuration. HMOBEDA not only provides the best value for hypervolume indicator and IGD metric in most of the cases, but it also computes a very diverse solutions set close to the estimated Pareto front. |
BibTeX:
@article{Martins_et_al:2018,
author = {Martins, Marcella SR and Delgado, Myriam RBS and Lüders, Ricardo and Santana, Roberto and Gonçalves, Richard Aderbal and Almeida, Carolina Paula de},
title = {Hybrid multi-objective Bayesian estimation of distribution algorithm: a comparative analysis for the multi-objective knapsack problem},
journal = {Journal of Heuristics},
year = {2018},
volume = {24},
number = {1},
pages = {25-47},
url = {https://link.springer.com/article/10.1007/s10732-017-9356-7}
}
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| Martins MS, Delgado MR, Lüders R, Santana R, Gonçalves RA and Almeida CPd (2018), "Exploring the probabilistic graphic model of a hybrid multi-objective Bayesian estimation of distribution algorithm", Applied Soft Computing. Vol. 73, pp. 328-343.
[Abstract] [BibTeX] [URL]
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| Abstract: The Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm (HMOBEDA) has shown to be very competitive for Many Objective Optimization Problems (MaOPs). The Probabilistic Graphic Model (PGM) of HMOBEDA expands the possibilities for exploration as it provides the joint probability of decision variables, objectives, and configuration parameters of an embedded local search. This work investigates different sampling mechanisms of HMOBEDA, applying the considered approaches to two different combinatorial MaOPs. Moreover, the paper provides a broad set of statistical analyses on its PGM model. These analyses have been carried out to evaluate how the interactions among variables, objectives and local search parameters are captured by the model and how information collected from different runs can be aggregated and explored in a Probabilistic Pareto Front. In experiments, two variants of HMOBEDA are compared with the original version, each one with a different set of evidences fixed during the sampling process. Results for instances of multi-objective knapsack problem with 2–5 and 8 objectives show that the best variant outperforms the original HMOBEDA in terms of convergence and diversity in the solution set. This best variant is then compared with five state-of-the-art evolutionary algorithms using the knapsack problem instances as well as a set of MNK-landscape instances with 2, 3, 5 and 8 objectives. HMOBEDA outperforms all of them. |
BibTeX:
@article{Martins_et_al:2018a,
author = {Martins, Marcella SR and Delgado, Myriam RBS and Lüders, Ricardo and Santana, Roberto and Gonçalves, Richard Aderbal and Almeida, Carolina Paula de},
title = {Exploring the probabilistic graphic model of a hybrid multi-objective Bayesian estimation of distribution algorithm},
journal = {Applied Soft Computing},
year = {2018},
volume = {73},
pages = {328-343},
url = {https://www.sciencedirect.com/science/article/pii/S1568494618305015}
}
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| Martins MSR, El-Yafrani M, Santana R, Delgado M, Lueders R and Ahiod B (2018), "On the performance of multi-objective estimation of distribution algorithms for combinatorial problems", In IEEE Congress on Evolutionary Computation (CEC-2018). Rio de Janeiro, Brazil , pp. 1-8.
[Abstract] [BibTeX] [URL]
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| Abstract: Fitness landscape analysis investigates features with a high influence on the performance of optimization algorithms, aiming to take advantage of the addressed problem characteristics. In this work, a fitness landscape analysis using problem features is performed for a Multi-objective Bayesian Optimization Algorithm (mBOA) on instances of MNK-Iandscape problem for 2, 3, 5 and 8 objectives. We also compare the results of mBOA with those provided by NSGA-III through the analysis of their estimated runtime necessary to identify an approximation of the Pareto front. Moreover, in order to scrutinize the probabilistic graphic model obtained by mBOA, the Pareto front is examined according to a probabilistic view. The fitness landscape study shows that mBOA is moderately or loosely influenced by some problem features, according to a simple and a multiple linear regression model, which is being proposed to predict the algorithms performance in terms of the estimated runtime. Besides, we conclude that the analysis of the probabilistic graphic model produced at the end of evolution can be useful to understand the convergence and diversity performances of the proposed approach. |
BibTeX:
@inproceedings{Martins_et_al:2018b,
author = {M. S. R. Martins and M. El-Yafrani and R. Santana and M. Delgado and R. Lueders and B. Ahiod},
title = {On the performance of multi-objective estimation of distribution algorithms for combinatorial problems},
booktitle = {IEEE Congress on Evolutionary Computation (CEC-2018)},
year = {2018},
pages = {1--8},
url = {https://ieeexplore.ieee.org/document/8477970}
}
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| Murua M, Suárez A, de-Lacalle NL, Santana R and Wretland A (2018), "Feature extraction based prediction of tool wear of Inconel 718 in face turning", Insight. Non-Destructive Testing and Condition Monitoring. Vol. 60(8), pp. 1-8. The British Institute of Non-Destructive Testing.
[Abstract] [BibTeX] [URL]
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| Abstract: Tool wear is a recurring topic in the cutting field, so obtaining knowledge about the tool wear process and the capability of predicting tool wear is of special importance. Cutting processes can be optimised with predictive models that are able to forecast tool wear with a suitable level of accuracy. This research focuses on the application of some regression approaches, based on machine learning techniques, to a face-turning process for Inconel 718. To begin with, feature extraction of the cutting forces is considered, to generate regression models. Subsequently, the regression models are improved with a reduced set of features obtained by computing the feature importance. The results provide evidence that the gradient-boosting regressor allows an increment in the wear prediction accuracy and the random forest regressor has the capability of detecting relevant features that characterise the turning process. They also reveal higher accuracy in predicting tool wear under high-pressure cooling as opposed to conventional lubrication. |
BibTeX:
@article{Murua_et_al:2018,
author = {M. Murua and A. Suárez and N. López-de-Lacalle and R. Santana and A. Wretland},
title = {Feature extraction based prediction of tool wear of Inconel 718 in face turning},
journal = {Insight. Non-Destructive Testing and Condition Monitoring},
publisher = {The British Institute of Non-Destructive Testing},
year = {2018},
volume = {60},
number = {8},
pages = {1--8},
url = {https://www.ingentaconnect.com/content/bindt/insight/2018/00000060/00000008/art00006}
}
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| Strickler A, Castro Jr O, Pozo A and Santana R (2018), "An investigation of the selection strategies impact on MOEDAs: CMA-ES and UMDA", Applied Soft Computing. Vol. 62, pp. 963-973. Elsevier.
[Abstract] [BibTeX] [URL]
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| Abstract: Researchers have acknowledged the importance of the selection, replacement, and archiving strategies in the behavior of evolutionary algorithms (EAs). However, these important relationships have not been deeply investigated in terms of Estimation of Distribution algorithms (EDAs). In a preliminary research, we focused on UMDA, a simple EDA that uses univariate Gaussian factorizations. Experimental results confirm that the choice of the selection method can provoke probabilistic modeling to be more effective for some classes of functions. Motivated by these results, this study is extended by evaluating several variants of selection strategies and probabilistic modeling approaches. The aim is to detect possible interactions between these two important components of evolutionary algorithms. Specifically, we use the selection strategies as defined for NSGA2, SPEA2, and IBEA algorithms, and the probabilistic models implemented as part of UMDA and CMA-ES, and a simple crossover operator (SBX). The recently introduced COCO framework comprising 55 bi-objective functions is used as the benchmark for the analysis. The results show that probabilistic modeling has an advantage over the classical genetic operator regardless of the selection method applied. Nevertheless, the results also show that some selection methods have a better performance when applied together with EDAs. |
BibTeX:
@article{Strickler_et_al:2018,
author = {Strickler, Andrei and Castro Jr, Olacir and Pozo, Aurora and Santana, Roberto},
title = {An investigation of the selection strategies impact on MOEDAs: CMA-ES and UMDA},
journal = {Applied Soft Computing},
publisher = {Elsevier},
year = {2018},
volume = {62},
pages = {963--973},
url = {https://www.sciencedirect.com/science/article/pii/S1568494617305707}
}
|