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A novel univariate marginal distribution algorithm based discretization algorithm

STATISTICS & PROBABILITY LETTERS 2012年第11期82卷 2001-2007页

作者： Zhao Jing Han ChongZhao Wei Bin Han DeQiang Xi An Jiao Tong Univ Key Lab Intelligent Networks & Network Secur State Key Lab Mfg Syst EngnMinist Educ Inst Integrated AutomatSch Elect & Informat Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Inst Syst Engn Sch Elect & Informat Engn Xian 710049 Peoples R China

Many data mining algorithms can only deal with discrete data or have a better performance on discrete data;however, for some technological reasons often we can only obtain the continuous value in the real world. Therefore, discretization has played an important role in data mining. Discretization is defined as the process of mapping the continuous attribute space into the discrete space, namely, using integer values or symbols to represent the continuous spaces. In this paper, we proposed a discretization method on the basis of a univariate marginal distribution algorithm (UMDA). The UMDA is a combination of statistics learning theory and Evolution algorithms. The fitness function of the UMDA not only took the accuracy of the classifier into account, but also the number of breakpoints. Experimental results showed that the algorithm proposed in this paper could effectively reduce the number of breakpoints, and at the same time, improve the accuracy of the classifier. (C) 2012 Published by Elsevier B.V.

关键词： univariate marginal distribution algorithm Breakpoints Discretization Estimation of distribution algorithms Data mining

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Application of Chaotic univariate marginal distribution algorithm to economic dispatch control of Cascade Hydropower Plants

Application of Chaotic Univariate Marginal Distribution Algo...

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1st International Conference on Energy and Environmental Protection (ICEEP 2012)

作者： Gu, Wei Wu, Yonggang Wu, Jincheng Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China

ISBN: (纸本)9783037854150

The economic dispatch control of cascade hydropower plants is a large scale non-linear constrained optimization problem, which plays an important role in cascade reservoirs daily optimal. This paper proposes a chaotic univariate marginal distribution algorithm (CUMDA) to solve the economic dispatch problem of cascade hydropower plants. In the proposed method, a chaotic search is integrated with univariate marginal distribution algorithm (UMDA) to effectively avoid premature convergence, chaotic sequences combine with adaptive approach are applied to help algorithm escape from local optimal trap. The feasibility of the proposed method is demonstrated for economic dispatch control of a test cascade hydro system. The simulation results show that the proposed method can obtain higher quality solution.

关键词： Economic dispatch control cascade hydropower plants univariate marginal distribution algorithm Chaotic

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A co-evolutionary approach to interpretable reinforcement learning in environments with continuous action spaces

A co-evolutionary approach to interpretable reinforcement le...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Custode, Leonardo Lucio Iacca, Giovanni Univ Trento Dept Informat Engn & Comp Sci Trento Italy

ISBN: (纸本)9781728190488

Machine learning (ML) has lately achieved impressive breakthroughs in several fields, enabling a plethora of exciting applications. However, mainstream ML techniques often have an undesirable property: they are not directly understandable by humans, thus humans cannot trust them in high-stakes or life-critical scenarios. A subfield of AI called interpretable AI (IAI) addresses this problem: generating models that are easy to understand for humans and, consequently, trustworthy. While several approaches apply IAI techniques to reinforcement learning problems, addressing the case in which an agent has to act in a continuous action space is still an open question. In this work, we propose a cooperative co-evolutionary approach based on grammatical evolution, Q-learning, and the univariate marginal distribution algorithm, specifically designed to train IAI agents (in the form of binary decision trees) capable of acting in environments with continuous action spaces. The experimental results show that our method is able to solve two well-known OpenAI Gym test cases reaching state-of-the-art performance. Moreover, a quantitative post hoc analysis reveals that the obtained solutions are more interpretable than those reported in the literature.

关键词： Grammatical Evolution univariate marginal distribution algorithm Q-Learning Interpretability OpenAI Gym

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univariate marginal distribution algorithm dynamics for a class of parametric functions with unitation constraints

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INFORMATION SCIENCES 2011年第11期181卷 2340-2355页

作者： Lozada-Chang, Li-Vang Santana, Roberto Univ Basque Country Intelligent Syst Grp Dept Comp Sci & Artificial Intelligence San Sebastian Spain Univ Politecn Madrid E-28660 Madrid Spain Univ Havana Fac Math & Computat Havana Cuba

In this paper, we introduce a mathematical model for analyzing the dynamics of the univariate marginal distribution algorithm (UMDA) for a class of parametric functions with isolated global optima. We prove a number of results that are used to model the evolution of UMDA probability distributions for this class of functions. We show that a theoretical analysis can assess the effect of the function parameters on the convergence and rate of convergence of UMDA. We also introduce for the first time a long string limit analysis of UMDA. Finally, we relate the results to ongoing research on the application of the estimation of distribution algorithms for problems with unitation constraints. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Estimation of distribution algorithms univariate marginal distribution algorithm Convergence analysis Evolutionary algorithms Long string limit analysis

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univariate marginal distribution algorithm in Combination with Extremal Optimization (EO, GEO)

Univariate Marginal Distribution Algorithm in Combination wi...

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18th International Conference on Neural Information Processing (ICONIP)

作者： Hashemi, Mitra Meybodi, Mohammad Reza Islamic Azad Univ Dept Comp Engn & Informat Technol Qazvin Branch Qazvin Iran Amirkabir Univ Technol Dept Comp Engn & Informat Technol Tehran Iran

ISBN: (纸本)9783642249570;9783642249587

The UMDA algorithm is a type of Estimation of distribution algorithms. This algorithm has better performance compared to others such as genetic algorithm in terms of speed, memory consumption and accuracy of solutions. It can explore unknown parts of search space well. It uses a probability vector and individuals of the population are created through the sampling. Furthermore. EO algorithm is suitable for local search of near global best solution in search space. and it dose not stuck in local optimum. Hence, combining these two algorithms is able to create interaction between two fundamental concepts in evolutionary algorithms, exploration and exploitation. and achieve better results of this paper represent the performance of the proposed algorithm on two NP-hard problems, multi processor scheduling problem and graph hi-partitioning problem.

关键词： univariate marginal distribution algorithm Extremal Optimization Generalized Extremal Optimization Estimation of distribution algorithm

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Hybrid estimation of distribution algorithms for solving a keyboard layout problem

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2018年第6期35卷 352-367页

作者： Pradeepmon, T. G. Panicker, Vinay V. Sridharan, R. Natl Inst Technol Dept Mech Engn Calicut Kerala India

The use of smartphones and handheld devices in our daily activities has sharply increased. The added features of wireless technology and related applications on these devices make it possible to write emails, notes, and long text. Even though the most commonly used electronic input device is a keyboard, very little work has been dedicated for finding an optimal layout for this device. In this research, the aim is to propose a better layout for the single-finger keyboard in terms of rapid typing. The keyboard layout problem can be formulated as a quadratic assignment problem, which is one of the hardest combinatorial optimization problems. Some well-known literary works in English are chosen for estimating the keying-in-time. A variant of genetic algorithm, namely, the estimation of distribution algorithms is used to find a better layout. The new layout is found to be efficient compared with some of the existing prominent keyboard layouts.

关键词： Single-finger keyboard estimation of distribution algorithm quadratic assignment problem keyboard layout problem population based incremental learning univariate marginal distribution algorithm

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An application of univariate marginal distribution algorithm in MIMO communication systems

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2010年第1期23卷 109-124页

作者： Bashir, Sajid Naeem, Muhammad Khan, Adnan Ahmed Shah, Syed Ismail CASE Dept Comp Engn Islamabad Pakistan Simon Fraser Univ Burnaby BC V5A 1S6 Canada NUST Islamabad Pakistan Iqra Univ Dept Comp Islamabad Pakistan

The paper discusses a sequence detector based on univariate marginal distribution algorithm (UMDA) that jointly estimates the symbols transmitted in a multiple input multiple output (MIMO) communication system. While an optimal maximum likelihood detection using an exhaustive search method is prohibitively complex, it has been shown that sphere decoder (SD) achieves the optimal bit error rate (BER) performance with polynomial time complexity for smaller array sizes. However, the worst-case complexity of SD is exponential in the problem dimensions, this brings in question its practical implementation for larger number of spatial layers and for higher-order signal constellation. The proposed detector shows promising results for this overly difficult and complicated operating environment, confirmed through simulation results. A performance comparison of the UMDA detector with SD is presented for higher-order complex MIMO architectures with limited average transmit power. The proposed detector achieves substantial performance gain for higher-order systems attaining a near optimal BER performance with reduced computational complexity as compared with SD. Copyright (C) 2009 John Wiley & Sons, Ltd.

关键词： computational complexity maximum likelihood detection multiple input multiple output systems sphere decoder univariate marginal distribution algorithm vertical bell labs layered space time architecture

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A novel Gaussian matched filter based on entropy minimization for automatic segmentation of coronary angiograms

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COMPUTERS & ELECTRICAL ENGINEERING 2016年 53卷 263-275页

作者： Cruz-Aceves, Ivan Cervantes-Sanchez, Fernando Hernandez-Aguirre, Arturo Perez-Rodriguez, Ricardo Ochoa-Zezzatti, Alberto Ctr Invest Matemat CIMAT AC Jalisco S-N Guanajuato 36000 Gto Mexico Juarez City Univ Av Hero Colegio Mil S-N Ciudad Juarez 88700 Chihuahua Mexico

This paper presents a new method for automatic detection and segmentation of coronary arteries in X-ray angiograms. In the vessel detection stage, a novel Gaussian matched filter (GMF) based on an entropy minimization fitness function is used to detect blood vessels in angiographic images. The detection results of the proposed Gaussian matched filter are compared with those obtained by five state-of-the-art GMF-based methods using the area (A(z)) under the receiver operating characteristic (ROC) curve. In the second stage, the interclass variance thresholding method has proven to be the most efficient compared with six different methods in order to classify vessel and non vessel pixels from the Gaussian filter response using the accuracy measure and the ground-truth angiograms drawn by a specialist. Finally, the proposed method is compared with eight state-of-the-art vessel segmentation methods. Due to the high rating of similarity (0.97) between the highest A(z) value and the A(z) value acquired by the fitness function over the whole dataset of angiograms, the result of vessel detection using, the proposed GMF demonstrated high performance achieving A(z) = 0.945 with a test set of 45 angiograms. In addition, the results of vessel segmentation with the inter-class variance thresholding method provided an accuracy of 0.961 with the test set of angiograms. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Automatic segmentation Coronary arteries Entropy Gaussian matched filters univariate marginal distribution algorithm Vessel detection

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Evolutionary Synthesis of Cube Root Computational Circuit Using Graph Hybrid Estimation of distribution algorithm

Evolutionary Synthesis of Cube Root Computational Circuit Us...

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作者： Slezak, J. Brno University of Technology

The paper is focused on evolutionary synthesis of analog circuit realization of cube root function using proposed Graph Hybrid Estimation of distribution algorithm. The problem of cube root function circuit realization was adopted to demonstrate synthesis capability of the proposed method. Individuals of the population of the proposed method which represent promising topologies are encoded using graphs and hypergraphs. Hybridization with local search algorithm was used. The proposed method employs univariate probabilistic model.

关键词： Automated analog circuit synthesis evolutionary algorithm analog circuit design estimation of distribution algorithm computational circuit univariate marginal distribution algorithm

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Identification of Deleterious Single Amino Acid Polymorphism Using Sequence Information Based on Feature Selection and Parameter Optimization

Identification of Deleterious Single Amino Acid Polymorphism...

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The 7th International Conference on Management and Service Science(MASS 2013)

作者： Xiao Chen Qinke Peng Jia Lv Systems Engineering Institute of Electronic and Information Engineering School Xi'an Jiaotong University

Most of the human genetic variations are single nucleotide polymorphisms(SNPs),and among them,non-synonymous SNPs,also known as SAPs,attract extensive *** can be neural or disease *** studies have been done to distinguish deleterious SAPs from neutral *** many previous studies were based on both structural and sequence features of the SAP,these methods are not applicable when protein structures are not *** the current paper,we developed a method based on UMDA and SVM using protein sequence information to predict SAP's disease *** extracted a set of features that are independent of protein structure for each *** a SVM-based machine-learning classifier that used grid search to tune parameters was applied to predict the possible disease association of *** SVM method reaches good prediction *** the input data of SVM contain irrelevant and noisy features and parameters of SVM also affect the prediction performance,we introduced UMDA-based wrapper approach to search for the 'best' *** UMDA-based method greatly improved prediction *** with current method,our method achieved better performance.

关键词： Single Amino Acid Polymorphisms Support Vector Machine univariate marginal distribution algorithm

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