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Wrapper discretization by means of estimation of distribution algorithms

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

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estimation of distribution algorithms for OLAP Data Cubes

Estimation of Distribution Algorithms for OLAP Data Cubes

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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

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TO ENHANCE CONTINUOUS estimation of distribution algorithms BY DENSITY ENSEMBLES

TO ENHANCE CONTINUOUS ESTIMATION OF DISTRIBUTION ALGORITHMS ...

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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

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

Runtime Analysis of Estimation of Distribution Algorithms fo...

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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

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Variable Screening for Reduced Dependency Modelling in Gaussian-based Continuous estimation of distribution algorithms

Variable Screening for Reduced Dependency Modelling in Gauss...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Mishra, Krishna Manjari Gallagher, Marcus Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld 4072 Australia

ISBN: (纸本)9781467315098

estimation of distribution algorithms (EDAs) focus on explicitly modelling dependencies between solution variables. A Gaussian distribution over continuous variables is commonly used, with several different covariance matrix structures ranging from diagonal i.e. Univariate Marginal distribution Algorithm (UMDA(c)) to full i.e. estimation of Multivariate Normal density Algorithm (EMNA). A diagonal covariance model is simple but is unable to directly represent covariances between problem variables. On the other hand, a full covariance model requires estimation of (more) parameters from the selected population. In practice, numerical issues can arise with this estimation problem. In addition, the performance of the model has been shown to be sometimes undesirable. In this paper, a modified Gaussian-based continuous EDA is proposed, called sEDA, that provides a mechanism to control the amount of covariance parameters estimated within the Gaussian model. To achieve this, a simple variable screening technique from experimental design is adapted and combined with an idea inspired by the Pareto-front in multi-objective optimization. Compared to EMNA(global), the algorithm provides improved numerical stability and can use a smaller selected population. Experimental results are presented to evaluate and compare the performance of the algorithm to UMDA(c) and EMNA(global)

关键词： estimation of distribution algorithms Optimization problems Screening Technique

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An Improved estimation of distribution algorithms based on the Minimal Free Energy

An Improved Estimation of Distribution Algorithms based on t...

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International Conference on Mechatronics, Robotics and Automation (ICMRA 2013)

作者： Yu, Fahong Chen, Meijia Liao, Weizhi Jiaxing Univ Coll Math & Informat Engn Jiaxing Peoples R China Jiaxing Univ Ctr Econom Managing Experiment Jiaxing Jiaxing Peoples R China Guangxi Teachers Educ Univ Coll Comp & Informat Engn Nanning Peoples R China

ISBN: (纸本)9783037858066

To systematically harmonize the conflict between selective pressure and population diversity in estimation of distribution algorithms, an improved estimation of distribution algorithms based on the minimal free energy (IEDA) is proposed in this paper. IEDA conforms to the principle of minimal free energy in simulating the competitive mechanism between energy and entropy in annealing process, in which population diversity is measured by similarity entropy and the minimum free energy is simulated with an efficient and effective competition by free energy component. Through solving some typical numerical optimization problems, satisfactory results were achieved, which showed that IEDA was a preferable algorithm to avoid the premature convergence effectively and reduce the cost in search to some extent.

关键词： estimation of distribution algorithms minimum free energy similarity entropy

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Cooperative Coevolution and Univariate estimation of distribution algorithms 09

Cooperative Coevolution and Univariate Estimation of Distrib...

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10th Conference on Foundations of Genetic algorithms

作者： Vo, Christopher Panait, Liviu Luke, Sean George Mason Univ Dept Comp Sci Fairfax VA 22030 USA

ISBN: (纸本)9781605584140

In this paper, we discuss a curious relationship between Cooperative Coevolutionary algorithms (CCEAs) and univariate estimation of distribution algorithms (EDAs). Specifically, the distribution model for univariate EDAs is equivalent to the infinite population EGT model common in the analysis of CCEAs. This relationship may permit cross-pollination between these two disparate fields. As an example, we derive a new EDA based on a known CCEA from the literature, and provide some preliminary experimental analysis of the algorithm.

关键词： Cooperative Coevolution estimation of distribution algorithms Evolutionary Game Theory

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Introducing the Mallows Model on estimation of distribution algorithms

Introducing the Mallows Model on Estimation of Distribution ...

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18th International Conference on Neural Information Processing (ICONIP)

作者： Ceberio, Josu Mendiburu, Alexander Lozano, Jose A. Univ Basque Country Intelligent Syst Grp Fac Comp Sci Donostia San Sebastian 20018 Spain

ISBN: (纸本)9783642249570;9783642249587

estimation of distribution algorithms are a set of algorithms that belong to the field of Evolutionary Computation. Characterized by the use of probabilistic models to learn the (in)dependencies between the variables of the optimization problem, these algorithms have been applied to a wide set of academic and real-world optimization problems, achieving competitive results in most scenarios. However, they have not been extensively developed for permutation-based problems. In this paper we introduce a. new EDA approach specifically designed to deal with permutation-based problems. In this paper, our proposal estimates a. probability distribution over permutations by means of a distance-based exponential model called the Mallows model. In order to analyze the performance of the Mallows model in EDAs, we carry out some experiments over the Permutation Flowshop Scheduling Problem (PFSP), and compare the results with those obtained by two state-of-the-art EDAs for permutation-based problems.

关键词： estimation of distribution algorithms Probabilistic Models Mallows Model Permutations Flow Shop Scheduling Problem

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Research topics in discrete estimation of distribution algorithms based on factorizations

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Memetic Computing 2009年第1期1卷 35-54页

作者： Santana, Roberto Larrañaga, Pedro Lozano, Jose A. Intelligent Systems Group Department of Computer Science and Artificial Intelligence University of the Basque Country San Sebastian-Donostia 20018 Paseo Manuel de Lardizábal 1 Spain Departamento de Inteligencia Artificial Universidad Politécnica de Madrid Madrid 28660 Boadilla del Monte Spain

In this paper, we identify a number of topics relevant for the improvement and development of discrete estimation of distribution algorithms. Focusing on the role of probability distributions and factorizations in estimation of distribution algorithms, we present a survey of current challenges where further research must provide answers that extend the potential and applicability of these algorithms. In each case we state the research topic and elaborate on the reasons that make it relevant for estimation of distribution algorithms. In some cases current work or possible alternatives for the solution of the problem are discussed. © 2008 Springer-Verlag.

关键词： Bayesian networks estimation of distribution algorithms Factorizations Fitness function Hybrid EDAs Macroscopic-macroscopic algorithms Probability distributions Problem structure

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Critical Issues in Model-Based Surrogate Functions in estimation of distribution algorithms

Critical Issues in Model-Based Surrogate Functions in Estima...

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4th International Conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO)

作者： Santana, Roberto Mendiburu, Alexander Lozano, Jose A. Univ Basque Country UPV EHU Dept Comp Sci & Artificial Intelligence Intelligent Syst Grp San Sebastian 20080 Guipuzcoa Spain

ISBN: (纸本)9783319037554;9783319037561

In many optimization domains the solution of the problem can be made more efficient by the construction of a surrogate fitness model. estimation of distribution algorithms (EDAs) are a class of evolutionary algorithms particularly suitable for the conception of model-based surrogate techniques. Since EDAs generate probabilistic models, it is natural to use these models as surrogates. However, there exist many types of models and methods to learn them. The issues involved in the conception of model-based surrogates for EDAs are various and some of them have received scarce attention in the literature. In this position paper, we propose a unified view for model-based surrogates in EDAs and identify a number of critical issues that should be dealt with in order to advance the research in this area.

关键词： estimation of distribution algorithms surrogate functions function approximation probabilistic modeling most probable configuration abductive inference

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