检索结果-内蒙古大学图书馆

Evolutionary optimization with Markov random field prior

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2004年第6期8卷 567-579页

作者： Wang, X Wang, H Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

This paper discusses an evolutionary algorithm in which the constituent variables of a solution are modeled by a Markov random field (MRF). We maintain a population of potential solutions. at every generation and for each solution a fitness value is calculated. The evolution, however, is not achieved through genetic recombination. Instead, each variable in a solution will be updated by sampling from its probability distribution. According to the MRF prior, local exploitation is encoded in the conditional probabilities. For evolutionary exploration, we estimate the probabilities as fitness-weighted statistics. These two kinds of search are combined smoothly in our algorithm. We compare it with two representative algorithms [iterated conditional modes (ICM) and simulated annealing (SA)] on noisy and textured image segmentation. Remarkable performance is observed.

关键词： combinatorial optimization estimation of distribution algorithms evolutionary computation genetic algorithms (GAs) hybrid search algorithm image segmentation Markov random fields (MRF)

来源：评论

学校读者我要写书评

暂无评论

Design optimization of a cable-based parallel tracking system by using evolutionary algorithms

引用

ROBOTICA 2015年第3期33卷 599-610页

作者： Hernandez, Eusebio E. Valdez, S. -I. Ceccarelli, M. Hernandez, A. Botello, S. Natl Polytech Inst ESIME UPT Sect Grad Studies & Res Mexico City DF Mexico Ctr Res Math CIMAT Dept Comp Sci Guanajuato Mexico Univ Cassino Lab Robot & Mechatron I-03043 Cassino Fr Italy

In this paper, an optimization design of a 6 DOF parallel measuring system is analyzed. First, a closed form direct kinematics formulation based on Cayley-Menger determinants is considered in the objective function, in order to measure the manipulator singularities, then an estimation of distribution algorithm is proposed to solve the optimization problem. It is shown that the evolutionary algorithm can find close to optimal solutions for minimum pose error estimation. Additionally, these global optimizers significantly reduce the computational burden in comparison with exhaustive search and other global optimization techniques. The sensitivity of the pose error estimation in the prescribed robots' workspace is analyzed and used to guide a designer in choosing the best structural configuration. Numerical examples are discussed to show the feasibility of the proposed optimization methodology.

关键词： Robotics Parallel mechanism design Tracking system Optimization estimation of distribution algorithms

来源：评论

学校读者我要写书评

暂无评论

On the application of different evolutionary algorithms to the alignment problem in statistical machine translation

引用

NEUROCOMPUTING 2008年第4-6期71卷 755-765页

作者： Rodriguez, Luis Garcia-Varea, Ismael Gamez, Jose A. Univ Castilla La Mancha Dept Sistemas Informat SIMD Albacete 02071 Spain

In statistical machine translation, an alignment defines a mapping between the words in the source and in the target sentence. Alignments are used, on the one hand, to train the statistical models and, on the other, during the decoding process to link the words in the source sentence to the words in the partial hypotheses generated. In both cases, the quality of the alignments is crucial for the success of the translation process. In this paper, we propose several evolutionary algorithms for computing alignments between two sentences in a parallel corpus. This algorithm has been tested on different tasks involving different pair of languages. Specifically, in the two shared tasks proposed in the HLT-NAACL 2003 and in the ACL 2005, the EDA-based algorithm outperforms the best participant systems. In addition, the experiments show that, because of the limitations of the well known statistical alignment models, new improvements in alignments quality could not be achieved by using improved search algorithms only. (c) 2007 Elsevier B.V. All rights reserved.

关键词： evolutionary algorithms estimation of distribution algorithms statistical machine translation statistical alignments

来源：评论

学校读者我要写书评

暂无评论

An augmented EDA with dynamic diversity control and local neighborhood search for coevolution of optimal negotiation strategies

引用

APPLIED INTELLIGENCE 2013年第4期38卷 600-619页

作者： Gwak, Jeonghwan Sim, Kwang Mong Gwangju Inst Sci & Technol Sch Informat & Mechatron Kwangju 500712 South Korea Univ Kent Sch Comp Chatham ME4 4AG Kent England

In this paper, we present an estimation of distribution algorithm (EDA) augmented with enhanced dynamic diversity controlling and local improvement methods to solve competitive coevolution problems for agent-based automated negotiations. Since optimal negotiation strategies ensure that interacting agents negotiate optimally, finding such strategies-particularly, for the agents having incomplete information about their opponents-is an important and challenging issue to support agent-based automated negotiation systems. To address this issue, we consider the problem of finding optimal negotiation strategies for a bilateral negotiation between self-interested agents with incomplete information through an EDA-based coevolution mechanism. Due to the competitive nature of the agents, EDAs should be able to deal with competitive coevolution based on two asymmetric populations each consisting of self-interested agents. However, finding optimal negotiation solutions via coevolutionary learning using conventional EDAs is difficult because the EDAs suffer from premature convergence and their search capability deteriorates during coevolution. To solve these problems, even though we have previously devised the dynamic diversity controlling EDA ((DC)-C-2-EDA), which is mainly characterized by a diversification and refinement (DR) procedure, (DC)-C-2-EDA suffers from the population reinitialization problem that leads to a computational overhead. To reduce the computational overhead and to achieve further improvements in terms of solution accuracy, we have devised an improved (DC)-C-2-EDA ((IDC)-C-2-EDA) by adopting an enhanced DR procedure and a local neighborhood search (LNS) method. Favorable empirical results support the effectiveness of the proposed (IDC)-C-2-EDA compared to conventional and the other proposed EDAs. Furthermore, (IDC)-C-2-EDA finds solutions very close to the optimum.

关键词： Negotiation agents Optimal negotiation strategy Population diversity estimation of distribution algorithms Dynamic diversity control

来源：评论

学校读者我要写书评

暂无评论

A FACTOR GRAPH BASED GENETIC ALGORITHM

引用

INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE 2014年第3期24卷 621-633页

作者： Helmi, B. Hoda Rahmani, Adel T. Pelikan, Martin Iran Univ Sci & Technol Dept Comp Engn Tehran Iran Google Inc Mountain View CA USA

We propose a new linkage learning genetic algorithm called the Factor Graph based Genetic Algorithm (FGGA). In the FGGA, a factor graph is used to encode the underlying dependencies between variables of the problem. In order to learn the factor graph from a population of potential solutions, a symmetric non-negative matrix factorization is employed to factorize the matrix of pair-wise dependencies. To show the performance of the FGGA, encouraging experimental results on different separable problems are provided as support for the mathematical analysis of the approach. The experiments show that FGGA is capable of learning linkages and solving the optimization problems in polynomial time with a polynomial number of evaluations.

关键词： optimization problems genetic algorithms estimation of distribution algorithms factor graph matrix factorization

来源：评论

学校读者我要写书评

暂无评论

Improving the performance of the BioHEL learning classifier system

引用

EXPERT SYSTEMS WITH APPLICATIONS 2013年第15期40卷 6019-6032页

作者： Xia, Xiao-Lei Xing, Huanlai Jiaxing Univ Sch Mech & Elect Engn Jiaxing 314001 Peoples R China Univ Nottingham Sch Comp Sci & IT Nottingham NG8 1BB England

The identification of significant attributes is of major importance to the performance of a variety of Learning Classifier Systems including the newly-emerged Bioinformatics-oriented Hierarchical Evolutionary Learning (BioHEL) algorithm. However, the BioHEL fails to deliver on a set of synthetic datasets which are the checkerboard data mixed with Gaussian noises due to the fact the significant attributes were not successfully recognised. To address this issue, a univariate estimation of distribution Algorithm (EDA) technique is introduced to BioHEL which primarily builds a probabilistic model upon the outcome of the generalization and specialization operations. The probabilistic model which estimates the significance of each attribute provides guidance for the exploration of the problem space. Experiment evaluations showed that the proposed BioHEL systems achieved comparable performance to the conventional one on a number of real-world small-scale datasets. Research efforts were also made on finding the optimal parameter for the traditional and proposed BioHEL systems. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Learning Classifier Systems Bioinformatics-oriented Hierarchical Evolutionary Learning estimation of distribution algorithms

来源：评论

学校读者我要写书评

暂无评论

HPBILc: A histogram-based EDA for continuous optimization

引用

APPLIED MATHEMATICS AND COMPUTATION 2009年第3期215卷 973-982页

作者： Xiao, Jing Yan, YuPing Zhang, Jun Sun Yat Sen Univ Sch Informat Sci & Technol Guangzhou 510275 Guangdong Peoples R China

Designing different estimation of distribution algorithms for continuous optimization is a recent emerging focus in the evolutionary computation field. This paper proposes an improved population-based incremental learning algorithm using histogram probabilistic model for continuous optimization. Histogram models are advantageous in describing the solution distribution of complex and multimodal continuous problems. The algorithm utilizes the sub-dividing strategy to guarantee the accuracy of optimal solutions. Experimental results show that the proposed algorithm is effective and it obtains better performance than the fast evolutionary programming (FEP) and those newly published EDAs in most test functions. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Histogram probabilistic model estimation of distribution algorithms Continuous optimization

来源：评论

学校读者我要写书评

暂无评论

Dynamic search space transformations for software test data generation

引用

COMPUTATIONAL INTELLIGENCE 2008年第1期24卷 23-61页

作者： Sagana, Ramon Lozano, Jose A. Univ Basque Country Integrated Syst Grp Dept Comp Sci & Artificial Intelligence Madrid Spain

Among the tasks in software testing, test data generation is particularly difficult and costly. In recent years, several approaches that use metaheuristic search techniques to automatically obtain the test inputs have been proposed. Although work in this field is very active, little attention has been paid to the selection of an appropriate search space. The present work describes an alternative to this issue. More precisely, two approaches which employ an estimation of distribution Algorithm as the metaheuristic technique are explained. In both cases, different regions are considered in the search for the test inputs. Moreover, to depart from a region near to the one containing the optimum, the definition of the initial search space incorporates static information extracted from the source code of the software under test. If this information is not enough to complete the definition, then a grid search method is used. According to the results of the experiments conducted, it is concluded that this is a promising option that can be used to enhance the test data generation process.

关键词： software testing evolutionary algorithms dynamic representations estimation of distribution algorithms search based test data generation

来源：评论

学校读者我要写书评

暂无评论

Comparison-based algorithms are robust and Randomized algorithms are anytime

引用

EVOLUTIONARY COMPUTATION 2007年第4期15卷 411-434页

作者： Gelly, Sylvain Ruette, Sylvie Teytaud, Olivier Univ Paris 11 CNRS LRI Equipe TAO INRIA FutursUMR 8623 F-91405 Orsay France Univ Paris 11 CNRS Math Lab UMR 8628 F-91405 Orsay France

Randomized search heuristics (e.g., evolutionary algorithms, simulated annealing etc.) are very appealing to practitioners, they are easy to implement and usually provide good performance. The theoretical analysis of these algorithms usually focuses on convergence rates. This paper presents a mathematical study of randomized search heuristics which use comparison based selection mechanism. The two main results are that comparison-based algorithms are the best algorithms for some robustness criteria and that introducing randomness in the choice of offspring improves the anytime behavior of the algorithm. An original estimation of distribution Algorithm combining both results is proposed and successfully experimented.

关键词： theory robust optimization randomized search heuristics anytime optimization estimation of distribution algorithms

来源：评论

学校读者我要写书评

暂无评论

Application of L-EDA in metabonomics data handling: global metabolite profiling and potential biomarker discovery of epithelial ovarian cancer prognosis

引用

METABOLOMICS 2011年第4期7卷 614-622页

作者： Chen, Jing Zhang, Yang Zhang, Xiaoyan Cao, Rui Chen, Shili Huang, Qiang Lu, Xin Wan, Xiaoping Wu, Xiaohua Xu, Congjian Xu, Guowang Lin, Xiaohui Chinese Acad Sci Dalian Inst Chem Phys CAS Key Lab Separat Sci Analyt Chem Dalian 116023 Peoples R China Dalian Univ Technol Sch Comp Sci & Technol Dalian 116024 Peoples R China Fudan Univ Inst Biomed Sci Shanghai Key Lab Female Reprod Endocrine Related Obstet & Gynecol HospShanghai Med Sch Shanghai 200011 Peoples R China Dalian Med Univ Dept Obstet Dalian 116033 Peoples R China Shanghai Jiao Tong Univ Sch Med Int Peace Matern & Child Hlth Hosp Shanghai 200030 Peoples R China Fudan Univ Dept Gynecol Oncol Canc Hosp Shanghai 200032 Peoples R China Dalian Med Univ Gynecol Hosp Dalian 116033 Peoples R China

Solution capacity limited estimation of distribution algorithm (L-EDA) is proposed and applied to ovarian cancer prognosis biomarker discovery to expatiate on its potential in metabonomics studies. Sera from healthy women, epithelial ovarian cancer (EOC), recurrent EOC and non-recurrent EOC patients were analyzed by liquid chromatography-mass spectrometry. The metabolite data were processed by L-EDA to discover potential EOC prognosis biomarkers. After L-EDA filtration, 78 out of 714 variables were selected, and the relationships among four groups were visualized by principle component analysis, it was observed that with the L-EDA filtered variables, non-recurrent EOC and recurrent EOC groups could be separated, which was not possible with the initial data. Five metabolites (six variables) with P < 0.05 in Wilcoxon test were discovered as potential EOC prognosis biomarkers, and their classification accuracy rates were 86.9% for recurrent EOC and non-recurrent EOC, and 88.7% for healthy + non-recurrent EOC and EOC + recurrent EOC. The results show that L-EDA is a powerful tool for potential biomarker discovery in metabonomics study.

关键词： Metabonomics Ovarian cancer Prognosis biomarker Solution capacity limited EDA estimation of distribution algorithms

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：