检索结果-内蒙古大学图书馆

Delineation of site-specific management zones using estimation of distribution algorithms

INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2023年第4期30卷 1703-1729页

作者： Velasco, Jonas Vicencio, Salvador Lozano, Jose A. Cid-Garcia, Nestor M. CONACYT Ctr Invest Matemat CIMAT AC Fray Bartolome Casas 314 Aguascalientes 20259 Aguascalientes Mexico Ctr Invest Matemat CIMAT AC Fray Bartolome Casas 314 Aguascalientes 20259 Aguascalientes Mexico Univ Basque Country UPV EHU Basque Ctr Appl Math BCAM Paseo Manuel Lardizabal 1 Donostia San Sebastian 20018 Spain CONACYT Ctr Invest Ciencias Informac Geoespacial Lab Nacl Geointeligencia Circuito Tecnopolo Norte 107Tecnopolo Pocitos 2 Aguascalientes 20313 Aguascalientes Mexico

In this paper, we present a novel methodology to solve the problem of delineating homogeneous site-specific management zones (SSMZ) in agricultural fields. This problem consists of dividing the field into small regions for which a specific rate of inputs is required. The objective is to minimize the number of management zones, which must be homogeneous according to a specific soil property: physical or chemical. Furthermore, as opposed to oval zones, SSMZ with rectangular shapes are preferable since they are more practical for agricultural technologies. The methodology we propose is based on evolutionary computation, specifically on a class of the estimation of distribution algorithms (EDAs). One of the strongest contributions of this study is the representation used to model the management zones, which generates zones with orthogonal shapes, for example, L or T shapes, and minimizes the number of zones required to delineate the field. The experimental results show that our method is efficient to solve real field and randomly generated instances. The average improvement of our method consists in reducing the number of management zones in the agricultural fields concerning other operations research methods presented in the literature. The improvement depends on the size of the field and the level of homogeneity established for the resulting management zones.

关键词： site‐ specific management zones estimation of distribution algorithms orthogonal shapes evolutionary computation combinatorial optimization

来源：评论

学校读者我要写书评

暂无评论

Addressing the advantages of using ensemble probabilistic models in estimation of distribution algorithms for scheduling problems

引用

INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2013年第1期141卷 24-33页

作者： Chen, Shih-Hsin Chen, Min-Chih Nanhua Univ Dept Elect Commerce Management Dalin 62248 Taiwan WuFeng Univ Dept Informat Management Wufeng 62153 Taiwan

estimation of distribution algorithms (EDAs) have recently been recognized as a prominent alternative to traditional evolutionary algorithms due to their increasing popularity. The core of EDAs is a probabilistic model which directly impacts performance of the algorithm. Previous EDAs have used a univariate, bi-variate, or multi-variable probabilistic model each time. However, application of only one probabilistic model may not represent the parental distribution well. This paper advocates the importance of using ensemble probabilistic models in EDAs. We combine the univariate probabilistic model with the bi-variate probabilistic model which learns different population characteristics. To explain how to employ the two probabilistic models, we proposed the Ensemble Self-Guided Genetic Algorithm (eSGGA). The extensive computation results on two NP-hard scheduling problems indicate the advantages of adopting two probabilistic models. Most important of all, eSGGA can avoid the computation effort overhead when compared with other EDAs employing two models. As a result, this paper might point out a next generation approach for EDAs. (c) 2012 Elsevier B.V. All rights reserved.

关键词： estimation of distribution algorithms Single machine scheduling problem Permutation flowshop scheduling problem Self-Guided Genetic Algorithm

来源：评论

学校读者我要写书评

暂无评论

Structure Learning of Bayesian Networks by estimation of distribution algorithms with Transpose Mutation

引用

JOURNAL OF APPLIED RESEARCH AND TECHNOLOGY 2013年第4期11卷 586-596页

作者： Kim, D. W. Ko, S. Kang, B. Y. Chung Ang Univ Sch Comp Sci & Engn Seoul 156756 South Korea Kyungpook Natl Univ Sch Mech Engn Taegu 702701 South Korea

estimation of distribution algorithms (EDAs) constitute a new branch of evolutionary optimization algorithms that were developed as a natural alternative to genetic algorithms (GAs). Several studies have demonstrated that the heuristic scheme of EDAs is effective and efficient for many optimization problems. Recently, it has been reported that the incorporation of mutation into EDAs increases the diversity of genetic information in the population, thereby avoiding premature convergence into a suboptimal solution. In this study, we propose a new mutation operator, a transpose mutation, designed for Bayesian structure learning. It enhances the diversity of the offspring and it increases the possibility of inferring the correct arc direction by considering the arc directions in candidate solutions as bi-directional, using the matrix transpose operator. As compared to the conventional EDAs, the transpose mutation-adopted EDAs are superior and effective algorithms for learning Bayesian networks.

关键词： estimation of distribution algorithms Mutation Bayesian network Structure learning Optimization

来源：评论

学校读者我要写书评

暂无评论

Optimizing logistic regression coefficients for discrimination and calibration using estimation of distribution algorithms

引用

TOP 2008年第2期16卷 345-366页

作者： Robles, V. Bielza, C. Larranaga, P. Gonzalez, S. Ohno-Machado, L. Univ Politecn Madrid Dept Arquitectura & Tecnol Sistemas Informat Madrid Spain Univ Politecn Madrid Dept Inteligencia Artificial Madrid Spain Harvard Univ Div Hlth Sci & Technol Boston MA 02115 USA MIT Boston MA USA

Logistic regression is a simple and efficient supervised learning algorithm for estimating the probability of an outcome or class variable. In spite of its simplicity, logistic regression has shown very good performance in a range of fields. It is widely accepted in a range of fields because its results are easy to interpret. Fitting the logistic regression model usually involves using the principle of maximum likelihood. The Newton-Raphson algorithm is the most common numerical approach for obtaining the coefficients maximizing the likelihood of the data. This work presents a novel approach for fitting the logistic regression model based on estimation of distribution algorithms (EDAs), a tool for evolutionary computation. EDAs are suitable not only for maximizing the likelihood, but also for maximizing the area under the receiver operating characteristic curve (AUC). Thus, we tackle the logistic regression problem from a double perspective: likelihood-based to calibrate the model and AUC-based to discriminate between the different classes. Under these two objectives of calibration and discrimination, the Pareto front can be obtained in our EDA framework. These fronts are compared with those yielded by a multiobjective EDA recently introduced in the literature.

关键词： Logistic regression Evolutionary algorithms estimation of distribution algorithms Calibration and discrimination 62J12 90C59 90C29

来源：评论

学校读者我要写书评

暂无评论

estimation of distribution algorithms based on two copula selection methods

引用

INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2012年第4期3卷 317-331页

作者： Wang, Xiaoping Gao, Huimin Zeng, Jianchao Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou 730050 Gansu Peoples R China Jiaxing Univ Sch Business Jiaxing 314001 Zhejing Peoples R China Jiaxing Univ Mech & Elect Engn Coll Jiaxing 314001 Zhejing Peoples R China Taiyuan Univ Sci & Technol Complex Syst & Computat Intelligence Lab Taiyuan 030024 Shanxi Peoples R China

In order to solve the copula selection problem, an innovative method, called switched copula selection method is introduced, which is inspired by the mixed copula selection method. The new method can ignore how to determine a fixed copula beforehand, whereas it organises different kinds of copulas through a copula selection pool. Applying copula selection methods to estimation of distribution algorithms (EDAs), the procedures of proposed mixed copula-EDAs and switched copula-EDAs are described in detail. Comparing with the single copula-EDAs, the experiment results show that switched copula-EDAs is easy to implement and provides a balance between accuracy and efficiency of the copula selection, in the meantime it promotes the performance whereas the computation complexity is almost the same.

关键词： estimation of distribution algorithms EDAs copula selection switched copula mixed copula optimisation

来源：评论

学校读者我要写书评

暂无评论

estimation of distribution algorithms as Logistic Regression Regularizers of Microarray Classifiers

引用

METHODS OF INFORMATION IN MEDICINE 2009年第3期48卷 236-241页

作者： Bielza, C. Robles, V. Larranaga, P. Univ Politecn Madrid Dept Inteligencia Artificial E-28040 Madrid Spain Univ Politecn Madrid Dept Arquitectura & Tecnol Sistemas Informat E-28040 Madrid Spain

Objectives: The "large k (genes), small N (samples)" phenomenon complicates the problem of microarray classification with logistic regression. The indeterminacy of the maximum likelihood solutions, multicollinearity of predictor variables and data over-fitting cause unstable parameter estimates. Moreover, computational problems arise due to the large number of predictor (genes) variables. Regularized logistic regression excels as a solution. However, the difficulties found here involve an objective function hard to be optimized from a mathematical viewpoint and a careful required tuning of the regularization parameters. Methods: Those difficulties are tackled by introducing a new way of regularizing the logistic regression. estimation of distribution algorithms (EDAs), a kind of evolutionary algorithms, emerge as natural regularizers. Obtaining the regularized estimates of the logistic classifier amounts to maximizing the likelihood function via our EDA, without having to be penalized. Likelihood penalties add a number of difficulties to the resulting optimization problems, which vanish in our case. Simulation of new estimates during the evolutionary process of EDAs is performed in such a way that guarantees their shrinkage while maintaining their probabilistic dependence relationships learnt. The EDA process is embedded in an adapted recursive feature elimination procedure, thereby providing the genes that are best markers for the classification. Results: The consistency with the literature and excellent classification performance achieved with our algorithm are illustrated on four microarray data sets: Breast, Colon, Leukemia and Prostate. Details on the last two data sets are available as supplementary material. Conclusions: We have introduced a novel EDA-based logistic regression regularizer. It implicitly shrinks the coefficients during EDA evolution process while optimizing the usual likelihood function. The approach is combined with a gene subset selection pr

关键词： Logistic regression regularization estimation of distribution algorithms DNA microarrays

来源：评论

学校读者我要写书评

暂无评论

Wrapper discretization by means of estimation of distribution algorithms

引用

INTELLIGENT DATA ANALYSIS 2007年第5期11卷 525-545页

作者： Flores, J. L. Inza, I. Larranaga, P. Univ Basque Country Dept Comp Sci & Artificial Intelligence Intelligent Syst Grp San Sebastian 20080 Spain

We present a supervised wrapper approach to discretization. In contrast to many classical approaches, the discretization process is multivariate: all variables are discretized simultaneously, and the proposed discretization is evaluated with the Naive-Bayes classifier. The search for the optimal discretization is carried out as an optimization process with the learning model estimated accuracy guiding it. The global optimization algorithm is based on estimation of distribution algorithms, a set of novel algorithms which are special kinds of evolutionary algorithms. In order to evaluate the behaviour of the algorithm, an analysis of different parameters is performed by means of analysis of variance (ANOVA). The evaluation was carried out using artificial datasets, and with UCI datasets. The results suggest that the proposed method provides an effective and robust technique for discretizating variables.

关键词： discretization classification evolutionary computation estimation of distribution algorithms

来源：评论

学校读者我要写书评

暂无评论

estimation of distribution algorithms for OLAP Data Cubes

Estimation of Distribution Algorithms for OLAP Data Cubes

引用

2011 International Conference on Management Science and Intelligent Control(ICMSIC 2011)

作者： Chen Jia School of mathematics and computer science Wuhan Textile University Wuhan China Xuhui Chen School of mathematics and computer science Wuhan Textile University Wuhan China

On-Line Analytical Processing （OLAP） tools are frequently used in business,science and health to extract useful knowledge from massive *** important and hard optimization problem in OLAP data warehouses is the view selection problem,consisting of selecting a set of aggregate views of the data for speeding up future query *** apply one n estimation of distribution algorithms （EDAs） to view selection under a size *** emphasis is to determine the suitability of the combination of EDAs with constraint handling to the view selection problem,compared to a widely used genetic *** EDAs are competitive with the genetic algorithm on a variety of problem instances,often nding approximate optimal solutions in a reasonable amount of time.

关键词： query optimization estimation of distribution algorithms data warehousing OLAP view selection

来源：评论

学校读者我要写书评

暂无评论

TO ENHANCE CONTINUOUS estimation of distribution algorithms BY DENSITY ENSEMBLES

TO ENHANCE CONTINUOUS ESTIMATION OF DISTRIBUTION ALGORITHMS ...

引用

International Conference on Machine Learning and Cybernetics

作者： Hong, Yi Li, He-Long Kwong, Sam Ren, Qing-Sheng City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China South China Univ Technol Dept Elect Commerce Guangzhou Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9781424447053

This paper deals with using density ensembles methods to enhance continuous estimation of distribution algorithms. In particular, two density ensembles methods are applied: one is resampling method and the other is subspaces method. In resampling continuous estimation of distribution algorithms, a population of densities are obtained by resampling operator and density estimation operator, and new candidate solutions are reproduced by sampling from all obtained densities. In subspaces continuous estimation of distribution algorithms, a population of densities are obtained by randomly selecting a subset of all variables and estimating the density of high quality solutions in this subspace. The above steps iterate and many densities of high quality solutions in different subspaces are achieved. New candidate solutions are reproduced through perturbing old promising solutions in these subspaces.

关键词： estimation of distribution algorithms optimization

来源：评论

学校读者我要写书评

暂无评论

Runtime Analysis of estimation of distribution algorithms for a Simple Scheduling Problem 19th

Runtime Analysis of Estimation of Distribution Algorithms fo...

引用

19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Liu, Rui Qian, Bin Zhang, Sen Hu, Rong Yu, Nai-Kang Kunming Univ Sci & Technol Sch Informat Engn & Automat Kunming 650500 Yunnan Peoples R China

ISBN: (纸本)9789819947546;9789819947553

estimation of distribution Algorithm (EDA) is an intelligent optimization technique widely applied in production scheduling. In algorithm application, time complexity is an important criterion of concern. However, there is relatively little theoretical research on the time complexity of these algorithms. We propose a single machine scheduling with deteriorating effect (SMSDE) and give a proof of its property. Under the objective of minimizing the makespan, the convergence time (CT) of EDA to solve SMSDE is obtained. Then, we obtain the First Hitting Time (FHT) of EDA solving SMSDE from CT. This study provides some theoretical support for the application of EDA.

关键词： estimation of distribution algorithms first hitting time deteriorating effect computational time complexity

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：