检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

161 篇 期刊文献
146 篇 会议
2 篇 学位论文

馆藏范围

309 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

282 篇 工学
- 253 篇 计算机科学与技术...
- 52 篇 电气工程
- 41 篇 软件工程
- 26 篇 控制科学与工程
- 9 篇 机械工程
- 8 篇 信息与通信工程
- 4 篇 材料科学与工程（可...
- 3 篇 力学（可授工学、理...
- 3 篇 生物医学工程（可授...
- 2 篇 仪器科学与技术
- 2 篇 化学工程与技术
- 1 篇 电子科学与技术（可...
- 1 篇 石油与天然气工程
- 1 篇 交通运输工程
- 1 篇 航空宇航科学与技...
- 1 篇 生物工程
74 篇 理学
- 51 篇 数学
- 28 篇 生物学
- 4 篇 统计学（可授理学、...
- 1 篇 物理学
- 1 篇 系统科学
55 篇 管理学
- 54 篇 管理科学与工程(可...
- 3 篇 工商管理
- 1 篇 公共管理
7 篇 农学
- 5 篇 作物学
5 篇 医学
- 4 篇 基础医学(可授医学...
- 1 篇 临床医学
4 篇 经济学
- 4 篇 应用经济学

主题

309 篇 estimation of di...
32 篇 evolutionary alg...
29 篇 genetic algorith...
25 篇 evolutionary com...
16 篇 bayesian network...
15 篇 combinatorial op...
13 篇 bayesian optimiz...
13 篇 optimization
13 篇 machine learning
12 篇 multi-objective ...
9 篇 genetic programm...
9 篇 particle swarm o...
7 篇 genetic algorith...
7 篇 efficiency enhan...
6 篇 linkage learning
6 篇 model building
5 篇 feature subset s...
5 篇 hierarchical boa
5 篇 data mining
5 篇 denoising autoen...

机构

5 篇 univ queensland ...
4 篇 univ birmingham ...
4 篇 johannes gutenbe...
3 篇 univ illinois de...
3 篇 univ politecn ma...
3 篇 chulalongkorn un...
3 篇 jiaxing univ col...
3 篇 univ kent sch co...
3 篇 tsinghua univ st...
3 篇 lanzhou univ tec...
3 篇 taiyuan univ sci...
2 篇 yuan ze univ dep...
2 篇 hokkaido univ in...
2 篇 univ adelaide op...
2 篇 aalborg univ ope...
2 篇 univ tecnol fed ...
2 篇 univ illinois il...
2 篇 univ missouri de...
2 篇 univ fed parana ...
2 篇 univ essex dept ...

作者

26 篇 santana roberto
22 篇 lozano jose a.
17 篇 mendiburu alexan...
13 篇 pelikan martin
12 篇 larranaga pedro
8 篇 sastry kumara
8 篇 rothlauf franz
8 篇 bielza concha
8 篇 goldberg david e...
6 篇 gamez jose a.
6 篇 ceberio josu
6 篇 larrañaga p
5 篇 martins marcella...
5 篇 wittenberg david
5 篇 luders ricardo
5 篇 grahl joern
5 篇 irurozki ekhine
5 篇 bosman peter a. ...
4 篇 chen shih-hsin
4 篇 shakya siddharth...

语言

300 篇 英文
7 篇 其他
1 篇 西班牙文
1 篇 中文

检索条件"主题词=Estimation of Distribution algorithms"

共 309 条记录，以下是261-270 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Investigating selection strategies in multi-objective probabilistic model based algorithms 5

Investigating selection strategies in multi-objective probab...

引用

5th Brazilian Conference on Intelligent Systems (BRACIS)

作者： Strickler, Andrei Castro, Olacir, Jr. Pozo, Aurora Santana, Roberto Univ Fed Parana Comp Sci Dept Curitiba Parana Brazil Univ Basque Country Dept Comp Sci & Artificial Intelligence San Sebastian 20080 Spain

ISBN: (纸本)9781509035663

Recent advances on multi-objective evolutionary algorithm (MOEAs) have acknowledged the important role played by selection, replacement, and archiving strategies in the behavior of these algorithms. However, the influence of these methods has been scarcely investigated for the particular class of MOEAs that use probabilistic modeling of the solutions. In this paper we fill this void by proposing an analysis of the role of the aforementioned strategies on an extensive set of bi-objective functions. We focus on the class of algorithms that use Gaussian univariate marginal models, and study how typical selection and replacement strategies used together with this probabilistic model impact the behavior of the search. Our analysis is particularized for a set of bi-objective functions that exhibit a representative set of characteristics (e.g. decomposable, ill-conditioned, non-linear, etc.). The experimental results shows that MOEAs that use simple probabilistic modeling outperform traditional MOEAs based on crossover operators.

关键词： multi-objective optimization estimation of distribution algorithms Gaussian UMDA selection

来源：评论

学校读者我要写书评

暂无评论

Parameter-Less Population Pyramid for Permutation-Based Problems 16th

Parameter-Less Population Pyramid for Permutation-Based Prob...

引用

16th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Wozniak, Szymon Przewozniczek, Michal W. Komarnicki, Marcin M. Wroclaw Univ Sci & Technol Dept Computat Intelligence Wroclaw Poland

ISBN: (纸本)9783030581114;9783030581121

Linkage learning is frequently employed in state-of-the-art methods dedicated to discrete optimization domains. Information about linkage identifies a subgroup of genes that are found dependent on each other. If such information is precise and properly used, it may significantly improve a method's effectiveness. The recent research shows that to solve problems with so-called overlapping blocks, it is not enough to use linkage of high quality - it is also necessary to use many different linkages that are diverse. Taking into account that the overlapping nature of problem structure is typical for practical problems, it is important to propose methods that are capable of gathering many different linkages (preferably of high quality) to keep them diverse. One of such methods is a Parameter-less Population Pyramid (P3) that was shown highly effective for overlapping problems in binary domains. Since P3 does not apply to permutation optimization problems, we propose a new P3-based method to fill this gap. Our proposition, namely the Parameter-less Population Pyramid for Permutations (P4), is compared with the state-of-the-art methods dedicated to solving permutation optimization problems: Generalized Mallows estimation of distribution Algorithm (GM-EDA) and Linkage Tree Gene-pool Optimal Mixing Evolutionary Algorithm (LT-GOMEA) for Permutation Spaces. As a test problem, we use the Permutation Flowshop Scheduling problem (Taillard benchmark). Statistical tests show that P4 significantly outperforms GM-EDA for almost all considered problem instances and is superior compared to LT-GOMEA for large instances of this problem.

关键词： Genetic algorithms Linkage learning Linkage diversity Parameter-less estimation of distribution algorithms Permutation problems Random keys Scheduling

来源：评论

学校读者我要写书评

暂无评论

Upper Bounds on the Runtime of the Univariate Marginal distribution Algorithm on OneMax 17

Upper Bounds on the Runtime of the Univariate Marginal Distr...

引用

Genetic and Evolutionary Computation Conference (GECCO)

作者： Witt, Carsten Tech Univ Denmark DTU Compute DK-2800 Lyngby Denmark

ISBN: (纸本)9781450349208

A runtime analysis of the Univariate Marginal distribution Algorithm (UMDA) is presented on the OneMax function for wide ranges of the parameters mu and lambda. If mu >= c logn for some constant c > 0 and lambda = (1 +Theta(1))mu, a general bound O(mu n) on the expected runtime is obtained. This bound crucially assumes that all marginal probabilities of the algorithm are confined to the interval [1/n;1 - 1/n]. If mu >= c'root n logn for a constant c' > 0 and lambda = (1 + Theta(1))mu, the behavior of the algorithm changes and the bound on the expected runtime becomes O(mu root n), which typically even holds if the borders on the marginal probabilities are omitted. The results supplement the recently derived lower bound Omega(mu(root n+ n logn) by Krejca and WiS (FOGA 2017) and turn out as tight for the two very different values mu = c logn and mu = c'root n logn. They also improve the previously best known upper bound O(n logn log logn) by Dang and Lehre (GECCO 2015).

关键词： Theory estimation of distribution algorithms UMDA Runtime Analysis

来源：评论

学校读者我要写书评

暂无评论

Mixtures of Kikuchi approximations

Mixtures of Kikuchi approximations

引用

17th European Conference on Machine Learning (ECML 2006)

作者： Santana, Roberto Larranaga, Pedro Lozano, Jose A. Univ Basque Country Intelligent Syst Grp Dept Comp Sci & Artificial Intelligence E-20080 San Sebastian Spain

ISBN: (纸本)354045375X

Mixtures of distributions concern modeling a probability distribution by a weighted sum of other distributions. Kikuchi approximations of probability distributions follow an approach to approximate the free energy of statistical systems. In this paper, we introduce the mixture of Kikuchi approximations as a probability model. We present an algorithm for learning Kikuchi approximations from data based on the expectation-maximization (EM) paradigm. The proposal is tested in the approximation of probability distributions that arise in evolutionary computation.

关键词： mixture of distributions Kikuchi approximations estimation of distribution algorithms EM

来源：评论

学校读者我要写书评

暂无评论

Entropy-Based Evaluation Relaxation Strategy for Bayesian Optimization Algorithm

Entropy-Based Evaluation Relaxation Strategy for Bayesian Op...

引用

23rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

作者： Luong, Hoang Ngoc Nguyen, Hai Thanh Thi Ahn, Chang Wook Sungkyunkwan Univ Sch Informat & Commun Engn Suwon 440746 South Korea

ISBN: (纸本)9783642130243

Bayesian Optimization Algorithm (BOA) belongs to the advanced evolutionary algorithms (EA) capable of solving problems with multivariate interactions However, to attain wide applicability in real-world world optimization, BOA needs to be coupled with various efficiency enhancement techinques A BOA incorporated with a novel entropy-based (cBOA) is developed in this regard Composed of an on-demand evaluation strategy (ODES) and a sporadic evaluation method eBOA significantly reduces the number of (fitness) evaluations without, imposing any larger population-sizing requirement experiments adduce the grounds for its significant improvement, in the number of evaluations until reliable convergence Furthermore;the evaluation relaxation does not negatively affect the scalability performance

关键词： estimation of distribution algorithms ayesian Optimization Algorithm Evaluation Relaxation On-Demand Evaluation

来源：评论

学校读者我要写书评

暂无评论

Domino Convergence: Why One Should Hill-Climb on Linear Functions 18

Domino Convergence: Why One Should Hill-Climb on Linear Func...

引用

Genetic and Evolutionary Computation Conference (GECCO)

作者： Witt, Carsten Tech Univ Denmark DTU Compute DK-2800 Lyngby Denmark

ISBN: (纸本)9781450356183

In the theory community of evolutionary computation, linear pseudo-boolean functions are often regarded as easy problems since all of them can be optimized in expected time O(n logn) by simple unbiased algorithms. However, results from genetic algorithms and estimation-of-distribution algorithms indicate that these algorithms treat different linear functions differently. More precisely, an effect called "domino convergence" is described in the literature, which means that bits of large weight in the linear function are optimized earlier than bits of low weight. Hence, different linear functions may lead to rather different expected optimization times. The present paper conducts a study of domino convergence. By rigorous runtime analyses, it is shown that domino convergence is mostly a consequence of the crossover underlying genetic algorithms and EDAs. Here a performance gap of order O(n/logn) between different linear functions is proved. In simple mutationonly EAs the effect of domino convergence is much less pronounced, with the typical performance gap being logarithmic in the population size. The effect disappears when population size 1 is used and the algorithm is reduced to hillclimbing. Different selection mechanisms, including cut and tournament selection are investigated and their impact on domino convergence is analyzed.

关键词： Theory estimation of distribution algorithms Genetic algorithms Runtime Analysis

来源：评论

学校读者我要写书评

暂无评论

Kernels of Mallows Models for Solving Permutation-based Problems 15

Kernels of Mallows Models for Solving Permutation-based Prob...

引用

17th Genetic and Evolutionary Computation Conference (GECCO)

作者： Ceberio, Josu Mendiburu, Alexander Lozano, Jose A. Univ Basque Country UPV EHU Dept Comp Sci & Artificial Intelligence Donostia San Sebastian 20018 Spain Univ Basque Country UPV EHU Dept Comp Architecture & Technol Donostia San Sebastian 20018 Spain

ISBN: (纸本)9781450334723

Recently, distance-based exponential probability models, such as Mallows and Generalized Mallows, have demonstrated their validity in the context of estimation of distribution algorithms (EDAs) for solving permutation problems. However, despite their successful performance, these models are unimodal, and therefore, they are not flexible enough to accurately model populations with solutions that are very sparse with regard to the distance metric considered under the model. In this paper, we propose using kernels of Mallows models under the Kendall's-tau and Cayley distances within EDAs. In order to demonstrate the validity of this new algorithm, Mallows Kernel EDA, we compare its performance with the classical Mallows and Generalized Mallows EDAs, on a benchmark of 90 instances of two different types of permutation problems: the quadratic assignment problem and the permutation flowshop scheduling problem. Experimental results reveal that, in most cases, Mallows Kernel EDA outperforms the Mallows and Generalized Mallows EDAs under the same distance. Moreover, the new algorithm under the Cayley distance obtains the best results for the two problems in terms of average fitness and computational time.

关键词： Permutation problems estimation of distribution algorithms Mallows model Kernels

来源：评论

学校读者我要写书评

暂无评论

On the Runtime Dynamics of the Compact Genetic Algorithm on Jump Functions 18

On the Runtime Dynamics of the Compact Genetic Algorithm on ...

引用

Genetic and Evolutionary Computation Conference (GECCO)

作者： Hasenohrl, Vaclav Sutton, Andrew M. Univ Minnesota Duluth MN 55812 USA

ISBN: (纸本)9781450356183

Jump functions were originally introduced as benchmarks on which recombinant evolutionary algorithms can provably outperform those that use mutation alone. To optimize a jump function, an algorithm must be able to execute an initial hill-climbing phase, after which a point across a large gap must be generated. Standard GAs mix mutation and crossover to achieve both behaviors. It seems likely that other techniques, such as estimation of distribution algorithms (EDAs) may exhibit such behavior, but an analysis is so far missing. We analyze an EDA called the compact Genetic Algorithm (cGA) on jump functions with gap k. We prove that the cGA initially exhibits a strong positive drift resulting in good hillclimbing behavior. Interpreting the diversity of the process as the variance of the underlying probabilistic model, we show the existence of a critical point beyond which progress slows and diversity vanishes. If k is not too large, the cGA generates with high probability an optimal solution in polynomial time before losing diversity. For k = Omega(logn), this yields a superpolynomial speedup over mutation-only approaches. We show a small modification that creates lambda > 2 offspring boosts the critical threshold and allows the cGA to solve functions with a larger gap within the same number of fitness evaluations.

关键词： estimation of distribution algorithms running time analysis theory

来源：评论

学校读者我要写书评

暂无评论

SGEGC: A Selfish Gene Theory Based Optimization Method by Exchanging Genetic Components

SGEGC: A Selfish Gene Theory Based Optimization Method by Ex...

引用

4th International Symposium on Intelligence Computation and Applications

作者： Yang, Cheng Li, Yuanxiang Lin, Zhiyi Wuhan Univ State Key Lab Software Engn Wuhan 430072 Peoples R China

ISBN: (纸本)9783642048425

In this paper, a new algorithm named SGEGC was proposed. Inspired by selfish gene theory, SGEGC uses a vector of survival rate to model the condition distribution, which serves as a virtual population that is used to generate new individuals. While the present estimation of distribution algorithms (EDAs) require much time to learn the complex relationships among variables, SGEGC employs an approach that exchanges the relevant genetic components. Experimental results show that the proposed approach is more efficient in convergent reliability and convergent velocity in comparison with BMDA, COMIT and MIMIC in the test functions.

关键词： estimation of distribution algorithms Selfish Gene Theory Nonlinearity Check

来源：评论

学校读者我要写书评

暂无评论

A Probabilistic Linear Genetic Programming with Stochastic Context-Free Grammar for solving Symbolic Regression problems 17

A Probabilistic Linear Genetic Programming with Stochastic C...

引用

Genetic and Evolutionary Computation Conference (GECCO)

作者： Dal Piccol Sotto, Leo Francoso de Melo, Vinicius Veloso Fed Univ Sao Paulo UNIFESP Inst Sci & Technol ICT Sao Jose Dos Campos SP Brazil

ISBN: (纸本)9781450349208

Traditional Linear Genetic Programming algorithms are based only on the selection mechanism to guide the search. Genetic operators combine or mutate random portions of the individuals, without knowing if the result will lead to a fitter individual. Probabilistic Model Building Genetic Programming was proposed to overcome this issue through a probability model that captures the structure of the fit individuals and use it to sample new individuals. This work proposes the use of LGP with a Stochastic Context-Free Grammar, that has a probability distribution that is updated according to selected individuals. We proposed a method for adapting the grammar into the linear representation of LGP. Tests performed with the proposed probabilistic method, and with two hybrid approaches, on several symbolic regression benchmark problems show that the results are statistically better than the obtained by the traditional LGP.

关键词： estimation of distribution algorithms Linear Genetic Programming Symbolic Regression

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共31页 << < 22 23 24 25 26 27 28 29 30 31 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：