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Saving computational budget in Bayesian network-based evolutionary algorithms

NATURAL COMPUTING 2021年第4期20卷 775-790页

作者： Scoczynski, Marcella Delgado, Myriam Luders, Ricardo Oliva, Diego Wagner, Markus Sung, Inkyung El Yafrani, Mohamed Fed Univ Technol Parana UTFPR Curitiba Parana Brazil Univ Oberta Catalunya IN3 Comp Sci Dept Barcelona Spain Univ Guadalajara Dept Ciencias Computac CUCEI Guadalajara Jalisco Mexico Univ Adelaide Optimisat & Logist Adelaide SA Australia Aalborg Univ Operat Res Grp Aalborg Denmark

During the evolutionary process, algorithms based on probability distributions for generating new individuals suffer from computational burden due to the intensive computation of probability distribution estimations, particularly when using Probabilistic Graph Models (PGMs). In the Bayesian Optimisation Algorithm (BOA), for instance, determining the optimal Bayesian network structure by a given solution sample is an NP-hard problem. To overcome this issue, we consider a new BOA-based optimisation approach (FBOA) which explores the fact that patterns of PGM adjustments can be used as a guide to reduce the frequency of PGM updates because significant changes in PGM structure might not occur so frequently, and because they can be particularly sparse at the end of evolution. In the present paper, this new approach is scrutinised in the search space of an NK-landscape optimisation problem for medium and large-size instances. Average gaps and success rates as well as the correlation between the landscape ruggedness of the problem and the expected runtime of FBOA and BOA are presented for medium-size instances. For large-size instances, optimisation results from FBOA and BOA are compared. The experiments show that, despite our FBOA being of almost three times faster than BOA, it still produces competitive optimisation results.

关键词： estimation of distribution algorithms Probabilistic graphical models Bayesian networks

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Improving dynamic facial expression recognition with feature subset selection

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PATTERN RECOGNITION LETTERS 2011年第5期32卷 740-748页

作者： Dornaika, F. Lazkano, E. Sierra, B. Univ Basque Country San Sebastian 20018 Spain Basque Fdn Sci IKERBASQUE Bilbao 48011 Spain

This paper addresses the dynamic recognition of basic facial expressions in videos using feature subset selection. Feature selection has been already used by some static classifiers where the facial expression is recognized from one single image. Past work on dynamic facial expression recognition has emphasized the issues of feature extraction and classification, however, less attention has been given to the critical issue of feature selection in the dynamic scenario. The main contributions of the paper are as follows. First, we show that dynamic facial expression recognition can be casted into a classical classification problem. Second, we combine a facial dynamics extractor algorithm with a feature selection scheme for generic classifiers. We show that the paradigm of feature subset selection with a wrapper technique can improve the dynamic recognition of facial expressions. We provide evaluations of performance on real video sequences using five standard machine learning approaches: Support Vector Machines, K Nearest Neighbor, Naive Bayes, Bayesian Networks, and Classification Trees. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Dynamic facial expression recognition Feature subset selection estimation of distribution algorithms Machine learning approaches Wrapper technique

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Extended artificial chromosomes genetic algorithm for permutation flowshop scheduling problems

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COMPUTERS & INDUSTRIAL ENGINEERING 2012年第2期62卷 536-545页

作者： Chen, Yuh-Min Chen, Min-Chih Chang, Pei-Chann Chen, Shih-Hsin Yuan Ze Univ Dept Informat Management Tao Yuan 32026 Taiwan Natl Cheng Kung Univ Inst Mfg Engn Tainan 70101 Taiwan Nanhua Univ Dept Elect Commerce Management Dalin Township 62248 Chiayi County Taiwan

In our previous researches, we proposed the artificial chromosomes with genetic algorithm (ACGA) which combines the concept of the estimation of distribution algorithms (EDAs) with genetic algorithms (GAS). The probabilistic model used in the ACGA is the univariate probabilistic model. We showed that ACGA is effective in solving the scheduling problems. In this paper, a new probabilistic model is proposed to capture the variable linkages together with the univariate probabilistic model where most EDAs could use only one statistic information. This proposed algorithm is named extended artificial chromosomes with genetic algorithm (eACGA). We investigate the usefulness of the probabilistic models and to compare eACGA with several famous permutation-oriented EDAs on the benchmark instances of the permutation flowshop scheduling problems (PFSPs). eACGA yields better solution quality for makespan criterion when we use the average error ratio metric as their performance measures. In addition, eACGA is further integrated with well-known heuristic algorithms, such as NEH and variable neighborhood search (VNS) and it is denoted as eACGA(hybrid) to solve the considered problems. No matter the solution quality and the computation efficiency, the experimental results indicate that eACGA(hybrid) outperforms other known algorithms in literature. As a result, the proposed algorithms are very competitive in solving the PFSPs. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Evolutionary algorithm with probabilistic models Scheduling problems estimation of distribution algorithms

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Inexact graph matching for model-based recognition:: Evaluation and comparison of optimization algorithms

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PATTERN RECOGNITION 2005年第11期38卷 2099-2113页

作者： Cesar, RM Bengoetxea, E Bloch, I Larrañaga, P ENST GET CNRS UMR 5141Dept TSILTCI Paris France Univ Sao Paulo IME Dept Comp Sci Sao Paulo Brazil Univ Basque Country Dept Comp Architecture & Technol San Sebastian Spain

A method for segmentation and recognition of image structures based on graph homomorphisms is presented in this paper. It is a model-based recognition method where the input image is over-segmented and the obtained regions are represented by an attributed relational graph (ARG). This graph is then matched against a model graph thus accomplishing the model-based recognition task. This type of problem calls for inexact graph matching through a homomorphism between the graphs since no bijective correspondence can be expected, because of the over-segmentation of the image with respect to the model. The search for the best homomorphism is carried out by optimizing an objective function based on similarities between object and relational attributes defined on the graphs. The following optimization procedures are compared and discussed: deterministic tree search, for which new algorithms are detailed, genetic algorithms and estimation of distribution algorithms. In order to assess the performance of theses algorithms using real data, experimental results on supervised, classification of facial features using face images from public databases are presented. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

关键词： inexact graph matching graph homomorphism tree search estimation of distribution algorithms model-based structure recognition

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Support condition monitoring of offshore wind turbines using model updating techniques

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2020年第4期19卷 1017-1031页

作者： Xu, Ying Nikitas, George Zhang, Tong Han, Qinghua Chryssanthopoulos, Marios Bhattacharya, Subhamoy Wang, Ying Tianjin Univ Key Lab Earthquake Engn Simulat & Seism Resilienc China Earthquake Adm Tianjin Peoples R China Univ Surrey Dept Civil & Environm Engn Guildford GU2 7XH Surrey England Kings Coll London Sch Biomed Engn & Imaging Sci London England

The offshore wind turbines are dynamically sensitive, whose fundamental frequency can be very close to the forcing frequencies activated by the environmental and turbine loads. Minor changes of support conditions may lead to the shift of natural frequencies, and this could be disastrous if resonance happens. To monitor the support conditions and thus to enhance the safety of offshore wind turbines, a model updating method is developed in this study. A hybrid sensing system was fabricated and set up in the laboratory to investigate the long-term dynamic behaviour of the offshore wind turbine system with monopile foundation in sandy deposits. A finite element model was constructed to simulate structural behaviours of the offshore wind turbine system. Distributed nonlinear springs and a roller boundary condition are used to model the soil-structure interaction properties. The finite element model and the test results were used to analyse the variation of the support condition of the monopile, through an finite element model updating process using estimation of distribution algorithms. The results show that the fundamental frequency of the test model increases after a period under cyclic loading, which is attributed to the compaction of the surrounding sand instead of local damage of the structure. The hybrid sensing system is reliable to detect both the acceleration and strain responses of the offshore wind turbine model and can be potentially applied to the remote monitoring of real offshore wind turbines. The estimation of distribution algorithm-based model updating technique is demonstrated to be successful for the support condition monitoring of the offshore wind turbine system, which is potentially useful for other model updating and condition monitoring applications.

关键词： Offshore wind turbines monopile sand model updating estimation of distribution algorithms soil-structure interaction

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Improvement of Kriging interpolation with learning kernel in environmental variables study

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2022年第4期60卷 1284-1297页

作者： Xu, Te Liu, Yongxia Tang, Lixin Liu, Chang Northeastern Univ Minist Educ Key Lab Data Analyt & Optimizat Smart Ind Shenyang Peoples R China Northeastern Univ Liaoning Engn Lab Operat Analyt & Optimizat Smart Shenyang Peoples R China Northeastern Univ Inst Ind & Syst Engn Shenyang Peoples R China Northeastern Univ Liaoning Key Lab Mfg Syst & Logist Shenyang Peoples R China Northeastern Univ Inst Ind & Syst Engn Shenyang 110819 Peoples R China

Kriging interpolation is a spatial interpolation method widely employed in the field of data analytics and prediction of environmental variables, which provides the best linear unbiased prediction of intermediate values. The core principle of Kriging interpolation is searching for data distribution regularity and predicting regionalised variable value, and it can be transferred into two descriptions of learning process: function fitting problem and coefficient optimisation problem. Although these two problems could be solved by many traditional algorithms like multiple linear regression method, the parameter estimation of variogram model becomes quite difficult when there are drifts or noises in the raw data. The purpose of this paper is to improve the Kriging interpolation algorithm with learning kernels based on estimation of distribution algorithms (EDAs) and Least-Squares Support Vector Machine (LSSVM). The experiments have been carried out based on a real-world case with environmental variables. Compared with other machine learning methods, experimental results verify the effectiveness of the proposed algorithm.

关键词： estimation of distribution algorithms least-squares support vector machine Kriging spatial interpolation machine learning

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ON THE PROBABILISTIC OPTIMIZATION OF SPIKING NEURAL NETWORKS

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INTERNATIONAL JOURNAL OF NEURAL SYSTEMS 2010年第6期20卷 481-500页

作者： Schliebs, Stefan Kasabov, Nikola Defoin-Platel, Michael Auckland Univ Technol Knowledge Engn & Discovery Res Inst Auckland New Zealand

The construction of a Spiking Neural Network (SNN), i.e., the choice of an appropriate topology and the configuration of its internal parameters, represents a great challenge for SNN based applications. Evolutionary algorithms (EAs) offer an elegant solution for these challenges and methods capable of exploring both types of search spaces simultaneously appear to be the most promising ones. A variety of such heterogeneous optimization algorithms have emerged recently, in particular in the field of probabilistic optimization. In this paper, a literature review on heterogeneous optimization algorithms is presented and an example of probabilistic optimization of SNN is discussed in detail. The paper provides an experimental analysis of a novel Heterogeneous Multi-Model estimation of distribution Algorithm (hMM-EDA). First, practical guidelines for configuring the method are derived and then the performance of hMM-EDA is compared to state-of-the-art optimization algorithms. Results show hMM-EDA as a light-weight, fast and reliable optimization method that requires the configuration of only very few parameters. Its performance on a synthetic heterogeneous benchmark problem is highly competitive and suggests its suitability for the optimization of SNN.

关键词： Spiking Neural Network heterogeneous optimization algorithms estimation of distribution algorithms Multi-Model EDA evolutionary algorithms

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An evolutionary algorithm for automated machine learning focusing on classifier ensembles: An improved algorithm and extended results

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THEORETICAL COMPUTER SCIENCE 2020年 805卷 1-18页

作者： Xavier-Junior, Joao C. Freitas, Alex A. Ludermir, Teresa B. Feitosa-Neto, Antonino Barreto, Cephas A. S. Univ Fed Rio Grande do Norte Digital Metropolis Inst Natal RN Brazil Univ Fed Rio Grande do Norte Dept Informat & Appl Math Natal RN Brazil Univ Fed Pernambuco Ctr Informat Technol Recife PE Brazil Univ Kent Sch Comp Canterbury Kent England

A large number of classification algorithms have been proposed in the machine learning literature. These algorithms have different pros and cons, and no algorithm is the best for all datasets. Hence, a challenging problem consists of choosing the best classification algorithm with its best hyper-parameter settings for a given input dataset. In the last few years, Automated Machine Learning (Auto-ML) has emerged as a promising approach for tackling this problem, by doing a heuristic search in a large space of candidate classification algorithms and their hyper-parameter settings. In this work we propose an improved version of our previous Evolutionary Algorithm (EA) - more precisely, an estimation of distribution Algorithm - for the Auto-ML task of automatically selecting the best classifier ensemble and its best hyper-parameter settings for an input dataset. The new version of this EA was compared against its previous version, as well as against a random forest algorithm (a strong ensemble algorithm) and a version of the well-known Auto-ML method Auto-WEKA adapted to search in the same space of classifier ensembles as the proposed EA. In general, in experiments with 21 datasets, the new EA version obtained the best results among all methods in terms of four popular predictive accuracy measures: error rate, precision, recall and F-measure. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Automated Machine Learning (Auto-ML) Classification Evolutionary algorithms estimation of distribution algorithms

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Optimization based on phylogram analysis

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 78卷 32-50页

作者： Soares, Antonio Rabelo, Ricardo Delbem, Alexandre Univ Fed Piaui Piaui Brazil Univ Sao Paulo Piaui Brazil

estimation of distribution algorithms (EDAs) is evolutionary algorithms with relevant performance in handling complex problems. Nevertheless, their efficiency and effectiveness directly depends on how accurate the deployed probabilistic models are, which in turn depend on methods of model building. Although the best models found in the literature are often built by computationally complex methods, whose corresponding EDAs require high running time, these methods may evaluate a lesser number of points in the search space. In order to find a better trade-off between running time (efficiency) and the number of evaluated points (effectiveness), this work uses probabilistic models built by algorithms of phylogenetic reconstruction, since some of them are able to efficiently produce accurate models. Then, an EDA, namely, Optimization based on Phylogram Analysis, and a new search technique, namely, Composed Exhaustive Search, are developed and proposed to find solutions for combinatorial optimization problems with different levels of difficulty. Experimental results show that the proposed new EDA features an interesting trade-off between running time and number of evaluated points, attaining solutions near to the best results found in the literature for each one of such performance measures. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： estimation of distribution algorithms Phylogram analysis Hierarchical clustering Combinatorial optimization

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Model accuracy in the Bayesian optimization algorithm

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SOFT COMPUTING 2011年第7期15卷 1351-1371页

作者： Lima, Claudio F. Lobo, Fernando G. Pelikan, Martin Goldberg, David E. Univ Nottingham Ctr Plant Integrat Biol Loughborough LE12 5RD Leics England Univ Algarve Dept Elect & Informat Engn P-8000117 Faro Portugal Univ Missouri Dept Math & Comp Sci CCB 320 St Louis MO 63121 USA Univ Illinois Dept Ind & Enterprise Syst Engn Urbana IL 61801 USA

Evolutionary algorithms (EAs) are particularly suited to solve problems for which there is not much information available. From this standpoint, estimation of distribution algorithms (EDAs), which guide the search by using probabilistic models of the population, have brought a new view to evolutionary computation. While solving a given problem with an EDA, the user has access to a set of models that reveal probabilistic dependencies between variables, an important source of information about the problem. However, as the complexity of the used models increases, the chance of overfitting and consequently reducing model interpretability, increases as well. This paper investigates the relationship between the probabilistic models learned by the Bayesian optimization algorithm (BOA) and the underlying problem structure. The purpose of the paper is threefold. First, model building in BOA is analyzed to understand how the problem structure is learned. Second, it is shown how the selection operator can lead to model overfitting in Bayesian EDAs. Third, the scoring metric that guides the search for an adequate model structure is modified to take into account the non-uniform distribution of the mating pool generated by tournament selection. Overall, this paper makes a contribution towards understanding and improving model accuracy in BOA, providing more interpretable models to assist efficiency enhancement techniques and human researchers.

关键词： estimation of distribution algorithms Bayesian optimization algorithm Bayesian networks Model overfitting Selection

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