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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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MBMEDA: An Application of estimation of distribution algorithms to the Problem of Finding Biological Motifs

MBMEDA: An Application of Estimation of Distribution Algorit...

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5th International Work-Conference on the Interplay Between Natural and Artificial Computation (IWINAC)

作者： Jordan, Carlos I. Jordan, Carlos. J. Escuela Super Politecn Litoral ESPOL Fac Ingn Elect & Comp Guayaquil Ecuador

ISBN: (纸本)9783319189147;9783319189130

In this work we examine the problem of finding biological motifs in DNA databases. The problem was solved by applying MBMEDA, which is a evolutionary method based on the estimation of distribution Algorithm (EDA). Though it assumes statistical independence between the main variables of the problem, results were quite satisfactory when compared with those obtained by other methods;in some cases even better. Its performance was measured by using two metrics: precision and recall, both taken from the field of information retrieval. The comparison involved searching a motif on two types of DNA datasets: synthetic and real. On a set a five real databases the average values of precision and recall were 0.866 and 0.798, respectively.

关键词： DNA dataset estimation of distribution algorithms Molecular biology Transcription factor Motifs

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Sub-structural niching in estimation of distribution algorithms 05

Sub-structural niching in estimation of distribution algorit...

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Genetic and Evolutionary Computation Conference

作者： Sastry, Kumara. Abbass, Hussein A. Goldberg, David E. Johnson, D. D. Univ Illinois Dept Gen Engn IlliGAL Urbana IL 61801 USA

ISBN: (纸本)1595930108

We propose a sub-structural niching method that fully exploits the problem decomposition capability of linkage-learning methods such as the estimation distribution algorithms and concentrate on maintaining diversity at the sub-structural level. The proposed method consists of three key components: (1) Problem decomposition and sub-structure identification, (2) sub-structure fitness estimation, and (3) sub-structural niche preservation. The substructural niching method is compared to restricted tournament selection (RTS)-a niching method used in hierarchical Bayesian optimization algorithm-with special emphasis on sustained preservation of multiple global solutions of a class of boundedly-difficult, additively-separable multimodal problems. The results show that sub-structural niching successfully maintains multiple global optima over large number of generations and does so with significantly less population than RTS. Additionally, the market share of each of the niche is much closer to the expected level in sub-structural niching when compared to RTS.

关键词： genetic algorithms estimation of distribution algorithms multimodal multiobjective niching probabilistic models fitness estimation fitness approximation building blocks eCGA

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Decentralized estimation of distribution algorithms for Large Scale Networked Systems

Decentralized Estimation of Distribution Algorithms for Larg...

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Chinese Automation Congress (CAC)

作者： Bian, Aizhe Chen, Xi Tsinghua Univ Dept Automat Ctr Intelligent & Networked Syst Beijing Peoples R China

ISBN: (纸本)9781728113128

In order to solve optimization problems in large scale networked systems, this paper proposes a method to implement estimation of distribution algorithms (EDA) in a decentralized way. The main point of decentralized EDA is that each subsystem solves its own optimization problems based on local and its neighbors' information. Numerical examples illustrate the effectiveness of the algorithm.

关键词： Large scale networked systems decentralized optimization estimation of distribution algorithms

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Online Learning in estimation of distribution algorithms for Dynamic Environments

Online Learning in Estimation of Distribution Algorithms for...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Goncalves, Andre R. Von Zuben, Fernando J. Univ Campinas Unicamp Sch Elect & Comp Engn Campinas SP Brazil

ISBN: (纸本)9781424478354

In this paper, we propose an estimation of distribution algorithm based on an inexpensive Gaussian mixture model with online learning, which will be employed in dynamic optimization. Here, the mixture model stores a vector of sufficient statistics of the best solutions, which is subsequently used to obtain the parameters of the Gaussian components. This approach is able to incorporate into the current mixture model potentially relevant information of the previous and current iterations. The online nature of the proposal is desirable in the context of dynamic optimization, where prompt reaction to new scenarios should be promoted. To analyze the performance of our proposal, a set of dynamic optimization problems in continuous domains was considered with distinct levels of complexity, and the obtained results were compared to the results produced by other existing algorithms in the dynamic optimization literature.

关键词： Online learning mixture model estimation of distribution algorithms optimization in dynamic environments

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