检索结果-内蒙古大学图书馆

Learning Factorizations in estimation of distribution algorithms Using Affinity Propagation

EVOLUTIONARY COMPUTATION 2010年第4期18卷 515-546页

作者： Santana, Roberto Larranaga, Pedro Lozano, Jose A. Univ Politecn Madrid Fac Informat E-28660 Madrid Spain Univ Politecn Madrid Dept Inteligencia Artificial E-28660 Madrid Spain Univ Basque Country Intelligent Syst Grp Dept Comp Sci & Artificial Intelligence San Sebastian 20018 Spain

estimation of distribution algorithms (EDAs) that use marginal product model factorizations have been widely applied to a broad range of mainly binary optimization problems. In this paper, we introduce the affinity propagation EDA (AffEDA) which learns a marginal product model by clustering a matrix of mutual information learned from the data using a very efficient message-passing algorithm known as affinity propagation. The introduced algorithm is tested on a set of binary and nonbinary decomposable functions and using a hard combinatorial class of problem known as the HP protein model. The results show that the algorithm is a very efficient alternative to other EDAs that use marginal product model factorizations such as the extended compact genetic algorithm (ECGA) and improves the quality of the results achieved by ECGA when the cardinality of the variables is increased.

关键词： estimation of distribution algorithms affinity propagation factorized distribution algorithms message-passing algorithms marginal product models factorizations extended compact genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

A new initialization procedure for the distributed estimation of distribution algorithms

引用

SOFT COMPUTING 2011年第4期15卷 713-720页

作者： Muelas, Santiago Pena, Jose-Maria LaTorre, Antonio Robles, Victor Univ Politecn Madrid Fac Informat Dept Comp Syst Architecture & Technol Madrid Spain

estimation of distribution algorithms (EDAs) are one of the most promising paradigms in today's evolutionary computation. In this field, there has been an incipient activity in the so-called parallel estimation of distribution algorithms (pEDAs). One of these approaches is the distributed estimation of distribution algorithms (dEDAs). This paper introduces a new initialization mechanism for each of the populations of the islands based on the Voronoi cells. To analyze the results, a series of different experiments using the benchmark suite for the special session on Real-parameter Optimization of the IEEE CEC 2005 conference has been carried out. The results obtained suggest that the Voronoi initialization method considerably improves the performance obtained from a traditional uniform initialization.

关键词： estimation of distribution algorithms Distributed evolutionary algorithms Initialization Continuous optimization

来源：评论

学校读者我要写书评

暂无评论

Multi-marker tagging single nucleotide polymorphism selection using estimation of distribution algorithms

引用

ARTIFICIAL INTELLIGENCE IN MEDICINE 2010年第3期50卷 193-201页

作者： Santana, Roberto Mendiburu, Alexander Zaitlen, Noah Eskin, Eleazar Lozano, Jose A. Univ Politecn Madrid Fac Informat E-28660 Madrid Spain Univ Basque Country Intelligent Syst Grp San Sebastian 20018 Spain Univ Calif Los Angeles Comp Sci & Human Genet Grp Los Angeles CA 90095 USA

Objectives This paper presents an optimization algorithm for the automatic selection of a minimal subset of tagging single nucleotide polymorphisms (SNPs) Methods and materials The determination of the set of minimal tagging SNPs is approached as an optimization problem in which each tagged SNP can be covered by a single tagging SNP or by a pair of tagging SNPs The problem is solved using an estimation of distribution algorithm (EDA) which takes advantage of the underlying topological structure defined by the SNP correlations to model the problem interactions The EDA stochastically searches the constrained space of feasible solutions It is evaluated across HapMap reference panel data sets Results The EDA was compared with a SAT solver able to find the single-marker minimal tagging sets and with the Tagger program The percentage of reduction ranged from 10% to 43% in the number of tagging SNPs of the minimal multi-marker tagging set found by the EDA with respect to the other algorithms Conclusions The introduced algorithm is effective for the identification of minimal multi-marker SNP sets which considerably reduce the dimension of the tagging SNP set in comparison with single-marker sets Other variants of the SNP problem can be treated following the same approach (C) 2010 Elsevier B V All rights reserved

关键词： estimation of distribution algorithms Tagging single nucleotide polymorphism selection Multi-marker selection HapMap

来源：评论

学校读者我要写书评

暂无评论

On the Taxonomy of Optimization Problems Under estimation of distribution algorithms

引用

EVOLUTIONARY COMPUTATION 2013年第3期21卷 471-495页

作者： Echegoyen, Carlos Mendiburu, Alexander Santana, Roberto Lozano, Jose A. Univ Basque Country UPV EHU Dept Comp Sci & Artificial Intelligence Intelligent Syst Grp San Sebastian 20018 Spain Univ Basque Country UPV EHU Dept Comp Architecture & Technol Intelligent Syst Grp San Sebastian 20018 Spain

Understanding the relationship between a search algorithm and the space of problems is a fundamental issue in the optimization field. In this paper, we lay the foundations to elaborate taxonomies of problems under estimation of distribution algorithms (EDAs). By using an infinite population model and assuming that the selection operator is based on the rank of the solutions, we group optimization problems according to the behavior of the EDA. Throughout the definition of an equivalence relation between functions it is possible to partition the space of problems in equivalence classes in which the algorithm has the same behavior. We show that only the probabilistic model is able to generate different partitions of the set of possible problems and hence, it predetermines the number of different behaviors that the algorithm can exhibit. As a natural consequence of our definitions, all the objective functions are in the same equivalence class when the algorithm does not impose restrictions to the probabilistic model. The taxonomy of problems, which is also valid for finite populations, is studied in depth for a simple EDA that considers independence among the variables of the problem. We provide the sufficient and necessary condition to decide the equivalence between functions and then we develop the operators to describe and count the members of a class. In addition, we show the intrinsic relation between univariate EDAs and the neighborhood system induced by the Hamming distance by proving that all the functions in the same class have the same number of local optima and that they are in the same ranking positions. Finally, we carry out numerical simulations in order to analyze the different behaviors that the algorithm can exhibit for the functions defined over the search space {0, 1}(3).

关键词： estimation of distribution algorithms probabilistic models factorizations rank-based selection model of infinite population equivalence classes taxonomy of functions neighborhood system dynamical systems

来源：评论

学校读者我要写书评

暂无评论

On the model updating operators in univariate estimation of distribution algorithms

引用

NATURAL COMPUTING 2016年第2期15卷 335-354页

作者： Bronevich, Andrey G. de Oliveira, Jose Valente Natl Res Univ Higher Sch Econ Myasnitskaya 20 Moscow 101000 Russia Univ Algarve CEOT Campus Gambelas P-8005139 Faro Portugal Univ Algarve FCT Campus Gambelas P-8005139 Faro Portugal

The role of the selection operation-that stochastically discriminate between individuals based on their merit-on the updating of the probability model in univariate estimation of distribution algorithms is investigated. Necessary conditions for an operator to model selection in such a way that it can be used directly for updating the probability model are postulated. A family of such operators that generalize current model updating mechanisms is proposed. A thorough theoretical analysis of these operators is presented, including a study on operator equivalence. A comprehensive set of examples is provided aiming at illustrating key concepts, main results, and their relevance.

关键词： Compact genetic algorithm estimation of distribution algorithms Selection operator Theoretical analysis

来源：评论

学校读者我要写书评

暂无评论

Optimal row and column ordering to improve table interpretation using estimation of distribution algorithms

引用

JOURNAL OF HEURISTICS 2011年第5期17卷 567-588页

作者： Bengoetxea, E. Larranaga, P. Bielza, C. Fernandez del Pozo, J. A. Univ Basque Country Dept Comp Architecture & Technol San Sebastian 20018 Spain Univ Politecn Madrid Dept Inteligencia Artificial E-28660 Madrid Spain

A common information representation task in research as well as educational and statistical practice is to comprehensively and intuitively express data in two-dimensional tables. Examples include tables in scientific papers, as well as reports and the popular press. Data is often simple enough for users to reorder. In many other cases though, there are complex data patterns that make finding the best re-arrangement of rows and columns for optimum readability a tough problem. We propose that row and column ordering should be regarded as a combinatorial optimization problem and solved using evolutionary computation techniques. The use of genetic algorithms has already been proposed in the literature. This paper proposes for the first time the use of estimation of distribution algorithms for table ordering. We also propose alternative ways of representing the problem in order to reduce its dimensionality. By learning a selective naive Bayes classifier, we can find out how to jointly combine the parameters of these algorithms to get good table orderings. Experimental examples in this paper are on 2D tables.

关键词： Bertin matrices estimation of distribution algorithms Ordering of tables Conciseness Parametrization Selective naive Bayes

来源：评论

学校读者我要写书评

暂无评论

A hybrid method based on estimation of distribution algorithms to train convolutional neural networks for text categorization

引用

PATTERN RECOGNITION LETTERS 2022年 160卷 105-111页

作者： Grabiel Toledano-Lopez, Orlando Madera, Julio Gonzalez, Hector Simon-Cuevas, Alfredo Univ Ciencias Informat Havana Cuba Univ Camaguey Camaguey Cuba Univ Tecnol La Habana Jose Antonio Echeverria Havana Cuba

Convolutional Neural Networks for text categorization allows the extraction of features from the text represented through word embedding. The high dimensionality of the texts themselves implies a larger number of network parameters and a more complex optimization surface. Artificial neural network training is an NP-Hard optimization problem, which has been addressed by methods based on partial derivatives of the objective function and presents several theoretical and practical limitations, such as the probability of convergence to local minimums. In this work, we propose a hybrid method based on the estimation of distribution algorithms for training a Convolutional Neural Network. For this, we train together gradient-based methods with the estimation of Multivariate Normal Algorithm and Univariate Marginal distribution Algorithm by dividing the training process into two stages. The different variants obtained with the proposed method are compared with gradient-based methods on public benchmark datasets and statistical differences are analyzed by nonparametric tests. The proposed method increases the accuracy of the convolutional network applied to the text categorization task and overcome in about 0.22%-24% the state-of-the-art algorithms. (C) 2022 Elsevier B.V. All rights reserved.

关键词： estimation of distribution algorithms Convolutional neural network Text categorization

来源：评论

学校读者我要写书评

暂无评论

Unsupervised learning of Bayesian networks via estimation of distribution algorithms:: An application to gene expression data clustering

引用

INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS 2004年第Jan2004_sup期12卷 63-82页

作者： Peña, JM Lozano, JA Larrañaga, P Aalborg Univ Dept Comp Sci DK-9220 Aalborg Denmark Univ Basque Country Dept Comp Sci & Artificial Intelligence E-20018 Donostia San Sebastian Spain

This paper proposes using estimation of distribution algorithms for unsupervised learning of Bayesian networks, directly as well as within the framework of the Bayesian structural EM algorithm. Both approaches are empirically evaluated in synthetic and real data. Specifically, the evaluation in real data consists in the application of this paper's proposals to gene expression data clustering, i.e., the identification of clusters of genes with similar expression profiles across samples, for the leukemia database. The validation of the clusters of genes that are identified suggests that these may be biologically meaningful.

关键词： unsupervised learning Bayesian networks estimation of distribution algorithms gene expression data analysis

来源：评论

学校读者我要写书评

暂无评论

Learning Bayesian networks in the space of orderings with estimation of distribution algorithms

引用

INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2004年第4期18卷 607-625页

作者： Romero, T Larrañaga, P Sierra, B Department of Computer Science and Artificial Intelligence University of the Basque Country Spain

The search for the optimal ordering of a set of variables in order to solve a computational problem is a difficulty that can appear in several circumstances. One of these situations is the automatic learning of a network structure, for example, a Bayesian Network structure (BN) starting from a dataset. Searching in the space of structures is often unmanageable, especially if the number of variables is high. Popular heuristic approaches, like Cooper and Herskovits's K2 algorithm, depend on a given ordering of variables. estimation of distribution algorithms (EDAs) are a new paradigm for Evolutionary Computation that have been used as a search engine in the BN structure learning problem. In this paper, we will use two different EDAs to obtain not the best structure, but the optimal ordering of variables for the K2 algorithm: UMDA and MIMIC, both of them in discrete and continuous domains. We will also check whether the individual representation and its relation to the corresponding ordering play important roles, and whether MIMIC outperforms the results of UMDA.

关键词： Bayesian networks estimation of distribution algorithms space of orderings learning from data experimental results

来源：评论

学校读者我要写书评

暂无评论

Intelligent system for the analysis of microarray data using principal components and estimation of distribution algorithms

引用

EXPERT SYSTEMS WITH APPLICATIONS 2009年第3期36卷 4654-4663页

作者： Cano, C. Garcia, F. Lopez, F. J. Blanco, A. Univ Granada Dept Ciencias Computac & IA ETS Ingn Informat E-18071 Granada Spain

Background: Microarray technology allows to measure the expression of thousands of genes simultaneously, and under tens of specific conditions. Clustering and Biclustering are the main tools to analyze gene expression data obtained from microarray experiments. By grouping together genes with the same behavior across samples, relevant biological knowledge may be extracted. Non-exclusive groupings are required, since a gene may play more than one biological role. Gene Shaving [Hastie, T., et al. (2000). Gene Shaving as a method for identifying distinct sets of genes with similar expression. Genome Biology, 1, 1-21] is a popular clustering algorithm which looks for coherent clusters of genes with high variance across samples, allowing overlapping among the clusters. Method: In this paper, we present an intelligent system for analyzing microarray data. Our system implements three novel non-exclusive approaches for clustering and biclustering whose aim is to find coherent groups of genes with large between-sample variance: EDA-Clustering and EDA-Biclustering, based on estimation of distribution algorithms (EDA), and Gene-&-Sample Shaving, a biclustering algorithm based on Principal Components Analysis. Results: We integrated the three proposed methods into a web-based platform and tested their performance on two real datasets. The obtained results outperform Gene Shaving in terms of quality and size of revealed patterns. Furthermore, our system allows to visualize the results and validate them from a biological point of view by means of the annotations of the Gene Ontology. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Microarray Custering Biclustering estimation of distribution algorithms Principal components

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：