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Efficiency enhancement of estimation of distribution algorithms by a compressed tournament selection

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2014年第5期26卷 2537-2545页

作者： Martins, Jean P. Delbem, Alexandre C. B. Univ Sao Paulo Inst Math & Comp Sci BR-13566590 Sao Carlos SP Brazil

estimation of distribution algorithms (EDA) are population-based meta-heuristics that use probabilistic models to describe the relationships between variables of a problem. From these models, new solutions can be sampled without disruption of Building Blocks, leading to a more effective search. The key aspect of EDAs is the model-building step and better models should enable a better exploration of the search space. However, high-quality models require computationally expensive algorithms, resulting in too costly EDAs. This paper proposes an efficient implementation of the tournament selection algorithm (TS), called Compressed Tournament Selection (CTS). CTS avoids the insertion of repeated solutions in the population during the TS. Therefore, model-building can be performed on a reduced population (equivalent to the original), improving the EDAs' performance as a whole. In the experiments the method was applied to the Extended Compact Genetic Algorithm and the results showed that by using an appropriate tournament size, CTS can lead to high speed-ups without a decrease of model quality. Besides, CTS keeps the characteristics of TS and can be easily used to enhance the efficiency of any EDA based on the tournament selection algorithm.

关键词： Efficiency enhancement estimation of distribution algorithms tournament selection

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Negative Correlation Learning in the estimation of distribution algorithms for Combinatorial Optimization

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2013年第11期E96D卷 2397-2408页

作者： Wattanapornprom, Warin Chongstitvatana, Prabhas Chulalongkorn Univ Fac Engn Dept Comp Engn Bangkok Thailand

This article introduces the Coincidence Algorithm (COIN) to solve several multimodal puzzles. COIN is an algorithm in the category of estimation of distribution algorithms (EDAs) that makes use of probabilistic models to generate solutions. The model of COIN is a joint probability table of adjacent events (coincidence) derived from the population of candidate solutions. A unique characteristic of COIN is the ability to learn from a negative sample. Various experiments show that learning from a negative example helps to prevent premature convergence, promotes diversity and preserves good building blocks.

关键词： combinatorial optimization estimation of distribution algorithms negative correlation learning multimodal

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Copula estimation of distribution algorithms based on exchangeable Archimedean copula

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2012年第1期43卷 13-20页

作者： Wang, Lifang Guo, Xiaodong Zeng, Jianchao Hong, Yi Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou 730050 Peoples R China Taiyuan Univ Sci & Technol Complex Syst & Computat Intelligence Lab Taiyuan 030024 Peoples R China

The two key operators in estimation of distribution algorithms (EDAs) are estimating the distribution model according to the selected population and sampling new individuals from the estimated model. Copula EDA introduces the copula theory into EDA. The copula theory provides the theoretical basis and the way to separate the multivariate joint distribution probability function into a function called copula and the univariate margins. The estimation operator and the sampling operator in copula EDA are discussed in this paper, and three exchangeable Archimedean copulas are used in copula EDA. The experimental results show that the three copula EDAs perform equivalently to some classical EDAs.

关键词： evolutionary algorithms EAs estimation of distribution algorithms EDAs copula EDA copula theory exchangeable Archimedean copula

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A novel classification learning framework based on estimation of distribution algorithms

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2012年第4期3卷 353-366页

作者： Fan, Jiancong Xu, Qiang Liang, Yongquan Shandong Univ Sci & Technol Coll Informat Sci & Engn Qingdao Shandong Peoples R China Shandong Univ Sci & Technol Coll Informat Sci & Engn Comp Sci Qingdao Shandong Peoples R China

estimation of distribution algorithms (abbr. EDAs) is a relatively new branch of evolutionary algorithms. EDAs replace search operators with the estimation of the distribution of selected individuals + sampling from the population. In an EDAs, this explicit representation of the population is replaced with a probability distribution over the choices available at each position in the vector that represents a population member. In this paper, an estimation of distribution learning framework and the corresponding learning algorithm are proposed and the relevant properties of the framework are analysed on the basis of probability. The framework provides a basis and a principle criterion for designing and analysing evolutionary learning algorithms based on EDAs. The probability is the core tool of EDAs. EDA-based learning algorithms are required to estimate the population distribution by the sample distributions. The learning framework proposed can guide and regulate the design processes of learning algorithms and strategies based on EDAs. The framework involved in relevant learning problems is analysed from the perspectives of probability by properties analysis, proof and verification. The experiment results show that the framework proposed is feasible for realising learning from datasets and has better learning performances than some other relevant evolutionary learning methods.

关键词： evolutionary computation classification estimation of distribution algorithms EDAs machine learning

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Formalization Description of estimation of distribution algorithms Based on Intelligent Computation Framework

Formalization Description of Estimation of Distribution Algo...

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7th World Congress on Intelligent Control and Automation

作者： Wang, Lei Yan, Yong Lin, Xiaoqing Kang, Qi Wu, Qidi Tongji Univ Shanghai 200092 Peoples R China

ISBN: (纸本)9781424421138

Different computation modes of agents take on relative uniform characteristic in all kinds of intelligent algorithms, though they usually have different extrinsic forms. In this paper, formalization description of estimation of distribution algorithms is proposed;and the general framework description of estimation of distribution algorithms based on intelligent computation framework idea is discussed and validated based on the uniform framework description of intelligent computation abstracted from all kinds of intelligent computation modes.

关键词： estimation of distribution algorithms intelligent computation general framework model formalization description

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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

作者： Aizhe Bian Xi Chen Department of Automation Tsinghua University Beijing China

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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Analysis and Research Models of the estimation of distribution algorithms

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

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2011 International Conference on Computer Science and Network Technology(2011计算机科学与网络技术国际会议 ICCSNT 2011)

作者： Lihong Bi Shumin Zhang Shijiangzhuang Institute of Railway Technology Shijiazhuang Hebei China Shij iangzhuang Institute of Railway Technology Shijiazhuang Hebei China

ISBN: (纸本)9781457715860

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 *** this paper, the mechanism of the estimation of distribution algorithms is *** existing EDAs are surveyed and categorized according to the probabilistic model they used,then the strengths and weakness and the future perspective of EDAs are concluded.

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

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Formalization Description of estimation of distribution algorithms Based on Intelligent Computation Framework

Formalization Description of Estimation of Distribution Algo...

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7th World Congress on Intelligent Control and Automation (WCICA 2008), vol.2

作者： Lei Wang Yong Yan Xiaoqing Lin Qi Kang Qidi Wu University of Tongji Shanghai China Ministry of Education of China Beijing China

关键词： estimation of distribution algorithms Intelligent computation General framework model Formalization description

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An information geometry perspective on estimation of distribution algorithms: boundary analysis 08

An information geometry perspective on estimation of distrib...

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Proceedings of the 10th annual conference companion on Genetic and evolutionary computation

作者： Luigi Malagò Matteo Matteucci Bernardo Dal Seno Politecnico di Milano Milano Italy

ISBN: (纸本)9781605581316

estimation of distribution algorithms are a recent new meta-heuristic used in Genetics-Based Machine Learning to solve combinatorial and continuous optimization problems. One of the distinctive features of this family of algorithms is that the search for the optimum is performed within a candidate space of probability distributions associated to the problem rather than over the population of possible solutions. A framework based on Information Geometry [3] is applied in this paper to propose a geometrical interpretation of the different operators used in EDAs and provide a better understanding of the underlying behavior of this family of algorithms from a novel point of view. The analysis carried out and the simple examples introduced show the importance of the boundary of the statistical model w.r.t. the distributions and EDA may converge to.

关键词： boundary of a manifold estimation of distribution algorithms information geometry

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Adaptive Variance Scaling in Continuous Multi-Objective estimation-of-distribution algorithms 07

Adaptive Variance Scaling in Continuous Multi-Objective Esti...

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Annual Conference of Genetic and Evolutionary Computation Conference

作者： Bosman, Peter A. N. Thierens, Dirk Ctr Math & Comp Sci POB 94079 NL-1090 GB Amsterdam Netherlands Univ Utrecht Inst Informat & Comp Sci NL-3508 TB Utrecht Netherlands

ISBN: (纸本)9781595936974

Recent research into single-objective continuous estimation-of-distribution algorithms (EDAs) has shown that when 7 maximum-likelihood estimations are used for parametric distributions such as the normal distribution, the EDA can easily suffer from premature convergence. In this paper we argue that the same holds for multi-objective optimization. Our aim in this paper is to transfer a solution called Adaptive Variance Scaling (AVS) from the single-objective case to the multi-objective case. To this end, we zoom in on an existing EDA for continuous multi-objective optimization the MIDEA, which employs mixture distributions. We. propose a means to combine AVS with the normal mixture distribution, as opposed to the single normal distribution for which AVS was introduced. Tit addition, we improve the AVS scheme, using the Standard-Deviation Ratio (SDR.) trigger. Intuitively put, variance scaling is triggered by the SDR, trigger only if improvements are found to be far away front the mean. For the multi-objective case, this addition is important to keep the variance from bent, scaled to excessively, large values. From experiments performed oil five well-known benchmark problems. the addition of SDR and AVS is found to enlarge the class of problems that continuous multi-objective EDAs can solve reliably.

关键词： Evolutionary algorithms estimation of distribution algorithms Multi-Objective Optimization Adaptive Variance Scaling

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