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 o...
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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 distributionalgorithms for problems with unitation constraints. (C) 2011 Elsevier Inc. All rights reserved.
The UMDA algorithm is a type of Estimation of distributionalgorithms. This algorithm has better performance compared to others such as genetic algorithm in terms of speed, memory consumption and accuracy of solutions...
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ISBN:
(纸本)9783642249570;9783642249587
The UMDA algorithm is a type of Estimation of distributionalgorithms. 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.
In this paper, a univariate marginal distribution algorithm in continuous domain (UMDA (C) ) based on extreme elitism (EEUMDA (C) ) is proposed for solving the inverse displacement problem (IDP) of robotic manipulator...
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In this paper, a univariate marginal distribution algorithm in continuous domain (UMDA (C) ) based on extreme elitism (EEUMDA (C) ) is proposed for solving the inverse displacement problem (IDP) of robotic manipulators. This algorithm highlights the effect of a few top best solutions to form a primary evolution direction and obtains a fast convergence rate. Then it is implemented to determine the IDP of a 4-degree-of-freedom (DOF) Barrett WAM robotic arm. After that, the algorithm is combined with differential evolution (EEUMDA (C) -DE) to solve the IDP of a 7-DOF Barrett WAM robotic arm. In addition, three other heuristic optimization algorithms (enhanced leader particle swarm optimization, intersect mutation differential evolution, and evolution strategies) are applied to find the IDP solution of the 7-DOF arm and their performance is compared with that of EEUMDA (C) -DE.
This paper presents a new approach for dynamic economic dispatch (DED) problem in power system by using a hybrid univariate marginal distribution algorithm (HUMDA). The DED problem with valve-point effects and ramp ra...
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This paper presents a new approach for dynamic economic dispatch (DED) problem in power system by using a hybrid univariate marginal distribution algorithm (HUMDA). The DED problem with valve-point effects and ramp rate limits is a nonliner constrained optimization problem with non-convex and non-smooth characteristics. In the proposed method, a two-stage adaptive mechanism is devised to control parameters of the univariate marginal distribution algorithm in continuous domains (UMDAc) dynamically and lead the algorithm with better search efficiency;a chaotic local search operator is integrated with UMDAc to effectively avoid premature convergence. Moreover, a constraint handle according to the two-stage adaptive mechanism is proposed, and the results show that the strategy can handle constraints effectively. Finally, the efficiency of the proposed method is validated on two test systems consisting of 5, 10 and 30 thermal units. The results show the superiority of the proposed method while it is compared with other works in the area. Copyright (c) 2013 John Wiley & Sons, Ltd.
In their recent work, Lehre and Nguyen (2019) show that the univariate marginal distribution algorithm (UMDA) needs time exponential in the parent populations size to optimize the DeceptiveLeadingBlocks (DLB) problem....
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In their recent work, Lehre and Nguyen (2019) show that the univariate marginal distribution algorithm (UMDA) needs time exponential in the parent populations size to optimize the DeceptiveLeadingBlocks (DLB) problem. They conclude from this result that univariate EDAs have difficulties with deception and epistasis. In this work, we show that this negative finding is caused by the choice of the parameters of the UMDA. When the population sizes are chosen large enough to prevent genetic drift, then the UMDA optimizes the DLB problem with high probability with at most lambda(n/2+2e ln n) fitness evaluations. Since an offspring population size lambda of order nlogn can prevent genetic drift, the UMDA can solve the DLB problem with O(n(2) log n) fitness evaluations. In contrast, for classic evolutionary algorithms no better runtime guarantee than O(n(3)) is known (which we prove to be tight for the (1+1) EA), so our result rather suggests that the UMDA can cope well with deception and epistatis. From a broader perspective, our result shows that the UMDA can cope better with local optima than many classic evolutionary algorithms;such a result was previously known only for the compact genetic algorithm. Together with the lower bound of Lehre and Nguyen, our result for the first time rigorously proves that running EDAs in the regime with genetic drift can lead to drastic performance losses.
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 ...
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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.
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. There...
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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.
Detecting diseases associated SNPs is the central goal of genetics and molecular biology. However, high throughput techniques often provide long lists of disease SNPs candidates, and the identification of disease SNPs...
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ISBN:
(纸本)9781538630136
Detecting diseases associated SNPs is the central goal of genetics and molecular biology. However, high throughput techniques often provide long lists of disease SNPs candidates, and the identification of disease SNPs among the candidates set remains timeconsuming and expensive. In addition, contrasting to the number of SNPs involved, the available datasets (samples) generally have fairly small sample size and the datasets used in these studies are often imbalanced (the number of cases and controls are not equal). Therefore, it is necessary to develop a robust tool to identify the disease associated SNPs. In this paper, we proposed a modified univariate marginal distribution algorithm (MUMDA) taking into account the imbalanced ratios of case/control for selecting the associated SNPs. In addition, the sparse vector encoding method was used in this paper. We illustrated the effectiveness of our algorithm using Crohn's disease (CD) dataset which includes 144 cases and 243 controls (each one has 103 SNPs). Our observations suggested that the proposed method could capture important features of the genetic architecture of CD and our algorithm outperformed other current methods.
We perform a rigorous runtime analysis for the univariate marginal distribution algorithm on the LEADINGONES function, a wellknown benchmark function in the theory community of evolutionary computation with a high cor...
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ISBN:
(纸本)9781450361118
We perform a rigorous runtime analysis for the univariate marginal distribution algorithm on the LEADINGONES function, a wellknown benchmark function in the theory community of evolutionary computation with a high correlation between decision variables. For a problem instance of size n, the currently best known upper bound on the expected runtime is O (n lambda log lambda + n(2)) (Dang and Lehre, GECCO 2015), while a lower bound necessary to understand how the algorithm copes with variable dependencies is still missing. Motivated by this, we show that the algorithm requires a e(Omega(mu)) runtime with high probability and in expectation if the selective pressure is low;otherwise, we obtain a lower bound of Omega/(n lambda/log(lambda-mu) on the expected runtime. Furthermore, we for the first time consider the algorithm on the function under a prior noise model and obtain an O (n(2)) expected runtime for the optimal parameter settings. In the end, our theoretical results are accompanied by empirical findings, not only matching with rigorous analyses but also providing new insights into the behaviour of the algorithm.
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...
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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.
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