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

共 309 条记录，以下是61-70 订阅

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Enhanced Interactive estimation of distribution algorithms with Attention Mechanism and Restricted Boltzmann Machine

Enhanced Interactive Estimation of Distribution Algorithms w...

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IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Bao, Lin Sun, Xiaoyan Gong, Dunwei Zhang, Yong Xu, Biao China Univ Min & Technol Sch Informat & Control Engn Xuzhou Jiangsu Peoples R China Jiangsu Univ Sci & Technol Sch Elect & Informat Zhenjiang Jiangsu Peoples R China Shantou Univ Dept Elect Engn Shantou Peoples R China

ISBN: (纸本)9781728169293

Interactive estimation of distribution Algorithm (IEDA), by integrating users interactions with estimation of distribution Algorithm, is powerful for efficient personalized search when the probability model and fitness function are well designed. We here propose an improved IEDA by using attention mechanism strengthened Restricted Boltzmann Machine (RBM). An attention mechanism assisted RBM model is constructed to approximate the user preferences by inputting item features and user generated contents. Then the attention-enhanced probability model of EDA and the fitness function are developed based on the RBM. In the evolutionary process, the attention-based RBM together with the probability model and fitness function are managed according to new interactions and corresponding information. The proposed algorithm is applied to real-world Amazon data sets usually used in the personalized search or recommendation, and its performance is experimentally demonstrated in better predicting the user preferences to improve the searching efficiency and accuracy.

关键词： Interactive Evolutionary Computations estimation of distribution algorithms Personalized Search Restricted Boltzmann Machine Attention Mechanism

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Affinity Propagation Enhanced by estimation of distribution algorithms 11

Affinity Propagation Enhanced by Estimation of Distribution ...

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13th Annual Genetic and Evolutionary Computation Conference (GECCO)

作者： Santana, Roberto Bielza, Concha Larranaga, Pedro Univ Politecn Madrid Computat Intelligence Grp Madrid Spain

ISBN: (纸本)9781450305570

Tumor classification based on gene expression data can be applied to set appropriate medical treatment according to the specific tumor characteristics. In this paper we propose the use of estimation of distribution algorithms (EDAs) to enhance the performance of affinity propagation (AP) in classification problems. AP is an efficient clustering algorithm based on message-passing methods and which automatically identifies exemplars of each cluster. We introduce an EDA-based procedure to compute the preferences used by the AP algorithm. Our results show that AP performance can be notably improved by using the introduced approach. Furthermore, we present evidence that classification of new data is improved by employing previously identified exemplars with only minor decrease in classification accuracy.

关键词： Clustering classification estimation of distribution algorithms cancer gene expression profiles

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Research and New Development of Application of the estimation of distribution algorithms

Research and New Development of Application of the Estimatio...

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2nd International Conference on Information and Computing Science

作者： Wang, Dandan Jiang, Yong Li, Jianliang Nanjing Univ Sci & Technol Dept Informat & Comp Sci Nanjing 210094 Peoples R China

ISBN: (纸本)9780769536347

The estimation of distribution algorithms (EDAs) is a novel class of evolutionary algorithms which is motivated by the idea of building probabilistic graphical model of promising solutions to represent linkage information between variables in chromosome. Through learning of and sampling from probabilistic graphical model, new population is generated and optimization procedure is repeated until the stopping criteria are met. In this paper, the mechanism of the estimation of distribution algorithms is analyzed. Currently existing EDAs are surveyed and categorized according to the probabilistic model they used.

关键词： genetic algorithm evolutionary programming estimation of distribution algorithms

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Towards Automated Selection of estimation of distribution algorithms 10

Towards Automated Selection of Estimation of Distribution Al...

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12th Annual Genetic and Evolutionary Computation Conference (GECCO)

作者： Lobo, Fernando G. Lima, Claudio F. Univ Algarve DEEI FCT Campus Gambelas P-8005139 Faro Portugal Univ Nottingham Sch Biosci Ctr Plant Intergrat Biol Nottingham NG7 2RD England

This paper discusses automated selection of estimation of distribution algorithms for problem solving. A specific method inspired in the parameter-less GA is proposed. Other alternatives are also briefly mentioned as ... 详细信息

ISBN: (纸本)9781450300735

关键词： estimation of distribution algorithms Parameter control Automated selection of EDAs

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Analysis and Research Models of the estimation of distribution algorithms

Analysis and Research Models of the Estimation of Distributi...

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International Conference on Computer Science and Network Technology (ICCSNT)

作者： Bi, Lihong Zhang, Shumin Shijiangzhuang Inst Railway Technol Shijiazhuang Hebei Peoples R China

ISBN: (纸本)9781457715846

关键词： estimation of distribution algorithms Genetic algorithms Probabilistic Graphical Model

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Towards the Geometry of estimation of distribution algorithms Based on the Exponential Family 11

Towards the Geometry of Estimation of Distribution Algorithm...

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11th Foundations of Genetic algorithms Workshop

作者： Malago, Luigi Matteucci, Matteo Pistone, Giovanni Politecn Milan I-20133 Milan Italy

ISBN: (纸本)9781450306331

In this paper we present a geometrical framework for the analysis of estimation of distribution algorithms (EDAs) based on the exponential family. From a theoretical point of view, an EDA can be modeled as a sequence of densities in a statistical model that converges towards distributions with reduced support. Under this framework, at each iteration the empirical mean of the fitness function decreases in probability, until convergence of the population. This is the context of stochastic relaxation, i.e., the idea of looking for the minima of a function by minimizing its expected value over a set of probability densities. Our main interest is in the study of the gradient of the expected value of the function to be minimized, and in particular on how its landscape changes according to the fitness function and the statistical model used in the relaxation. After introducing some properties of the exponential family, such as the description of its topological closure and of its tangent space, we provide a characterization of the stationary points of the relaxed problem, together with a study of the minimizing sequences with reduced support. The analysis developed in the paper aims to provide a theoretical understanding of the behavior of EDAs, and in particular their ability to converge to the global minimum of the fitness function. The theoretical results of this paper, beside providing a formal framework for the analysis of EDAs, lead to the definition of a new class algorithms for binary functions optimization based on Stochastic Natural Gradient Descent (SNGD), where the estimation of the parameters of the distribution is replaced by the direct update of the model parameters by estimating the natural gradient of the expected value of the fitness function.

关键词： estimation of distribution algorithms Stochastic Natural Gradient Descent Exponential Family Stochastic Relaxation

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Model-based template-recombination in Markov network estimation of distribution algorithms for problems with discrete representation 3

Model-based template-recombination in Markov network estimat...

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Third World Congress on Information and Communication Technologies (WICT)

作者： Santana, Roberto Mendiburu, Alexander Univ Basque Country Intelligent Syst Grp San Sebastian Spain

ISBN: (纸本)9781479932306

While estimation of distribution algorithms (EDAs) based on Markov networks usually incorporate efficient methods to learn undirected probabilistic graphical models (PGMs) from data, the methods they use for sampling the PGMs are computationally costly. In addition, methods for generating solutions in Markov network based EDAs frequently discard information contained in the model to gain in efficiency. In this paper we propose a new method for generating solutions that uses the Markov network structure as a template for crossover. The new algorithm is evaluated on discrete deceptive functions of various degrees of difficulty and Ising instances.

关键词： estimation of distribution algorithms Markov networks recombination combinatorial problems probabilistic modeling

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Population Sizing for Entropy-based Model Building in Discrete estimation of distribution algorithms 07

Population Sizing for Entropy-based Model Building in Discre...

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Annual Conference of Genetic and Evolutionary Computation Conference

作者： Yu, Tian-Li Sastry, Kumara Goldberg, David E. Pelikan, Martin Natl Taiwan Univ Dept Elect Engn 1Sec 4Roosevelt Rd Taipei 10764 Taiwan

ISBN: (纸本)9781595936974

This paper proposes a population-sizing model for entropy-based model building in discrete estimation of distribution algorithms. Specifically, the population size required for building an accurate model is investigated. The effect of selection pressure on population sizing is also preliminarily incorporated. The proposed model indicates that the population size required for building ail accurate model scales as Theta(m log m), where m is the number of substructures of the given problem and is proportional to the problem size. Experiments are conducted to verify the derivations, and the results agree with the proposed model.

关键词： estimation of distribution algorithms Genetic algorithms Population Sizing Model Building Entropy Mutual Information

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Classification Scheme of Multi-objective estimation of distribution algorithms

Classification Scheme of Multi-objective Estimation of Distr...

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IEEE Congress on Evolutionary Computation

作者： Mendoza-Gonzalez, Alfredo Ponce-de-Leon, Eunice Diaz-Diaz, Elva Autonomous Univ Aguascalientes Intelligent Comp Dept Aguascalientes Ags Mexico

ISBN: (纸本)9781479904549;9781479904532

A variety of estimation of distribution algorithms for multi-objective optimization (MOEDAs) has been reported, each of them with its own characteristics and techniques in their optimization process. In this research we present a classification scheme for these algorithms, based on ten characteristics: domain of the variables, relationships between the variables, probabilistic graphical model, estimation approach, restriction support, problem handling, sorting method, individuals' handling, selection approach, and replacement approach. These characteristics were extracted by analyzing all the 24 MOEDAs reported in the literature. The scheme presented here helps to identify the methods and techniques used in each algorithm, also, a useful method for the analysis of the optimization process of an EDA is proposed. This paper includes a brief analysis of the influence in the results of applying different selection/replacement percentages.

关键词： estimation of distribution algorithms Evolutionary algorithms Multi-objective optimization

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Does overfitting affect performance in estimation of distribution algorithms 06

Does overfitting affect performance in estimation of distrib...

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8th Annual Genetic and Evolutionary Computation Conference

作者： Wu, Hao Shapiro, Jonathan L. Univ Manchester Sch Comp Sci Manchester M13 9PL Lancs England

ISBN: (纸本)9781595931863

estimation of distribution algorithms (EDAs) are a class of evolutionary algorithms that use machine learning techniques to solve optimization problems. Machine learning is used to learn probabilistic models of the selected population. This model is then used to generate next population via sampling. An important phenomenon in machine learning from data is called overfitting. This occurs when the model is overly adapted to the specifics of the training data so well that even noise is encoded. The purpose of this paper is to investigate. whether overfitting happens in EDAs, and to discover its consequences. What is found is: overfitting does occur in EDAs;overfitting correlates to EDAs performance;reduction of overfitting using early stopping can improve EDAs performance.

关键词： overfitting estimation of distribution algorithms Bayesian optimization algorithm random 3-SAT

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