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estimation of distribution-based multiobjective design space exploration for energy and throughput-optimized MPSoCs

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2020年第1期28卷 540-555页

作者： Murad, Maryam Hussain, Ishfaq Ahmad, Ayaz Qadri, Muhammad Yasir Qadri, Nadia N. HITEC Univ Dept Elect Engn Taxila Pakistan COMSATS Univ Dept Elect Engn Wah Campus Islamabad Wah Cantonment Pakistan Univ Essex Sch Comp Sci & Elect Engn Colchester Essex England

Modern multicore architectures comprise a large set of components and parameters that require being matched to achieve the best balance between power consumption and throughput performance for a particular application domain. The exploration of design space for finding the best power-throughput trade-off is a combinatorial optimization problem with a large number of combinations, and. in general, its solution is prohibitively difficult to be explored exhaustively. However, fortunately, evolutionary algorithms (EAs) have the potential to efficiently solve this problem with reasonable computational complexity. In this paper, we consider a multiobjective design space exploration (DSE) problem with two conflicting objectives. The first objective corresponds to power consumption minimization while the second objective relates to throughput maximization. We approach this problem by employing the estimation of distribution algorithm (EDA), which belongs to the family of EAs. The proposed EDA-based DSE (EDA-DSE) scheme efficiently selects the design parameters (i.e. cache size, number of cores, and operating frequency) with an efficient power-throughput ratio. The proposed scheme is verified using cycle-accurate simulations over a set of benchmarks and the simulation results show a significant reduction in energy-delay product for all benchmark applications when compared to the default baseline configuration and genetic algorithm.

关键词： estimation of distribution algorithm multiobjective optimization design space exploration energy/throughput multiprocessal system on chip

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Optimization of Machining Parameters Using estimation of distribution algorithms

Optimization of Machining Parameters Using Estimation of Dis...

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2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(ICIS2009)

作者： Shu-tong Xie,Yin-biao Guo*,Hai-bin Huang,Jing Lin Department of Mechanical and Electrical Engineering Xiamen University Xiamen 361005,P.R.China

In computer numerical control(CNC) machining problems,it is important to reduce the production *** deal with the nonlinear optimization problem of machining parameters which aims to minimize the unit production cost(UC) in multi-pass turning operations,two estimation of distribution algorithms(EDAs) incorporated with gene repair method are proposed to search the optimal solution for machining *** simulation results show that the proposed algorithms are efficient in searching the optimal machining parameters,which significantly reduce the unit production cost.

关键词： Machining parameters Optimization estimation of distribution algorithm Turning

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Linkage Problem, distribution estimation, and Bayesian Networks

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EVOLUTIONARY COMPUTATION 2000年第3期8卷 311-340页

作者： Pelikan, Martin Goldberg, David E. Cantu-Paz, Erick Univ Illinois Dept Comp Sci Urbana IL 61801 USA Univ Illinois Illinois Genet Algorithms Lab Dept Gen Engn Urbana IL 61801 USA Lawrence Livermore Natl Lab Ctr Appl Sci Comp Livermore CA 94551 USA Comenius Univ Fac Math & Phys Inst Comp Sci Bratislava 84215 Slovakia Univ Illinois Dept Comp Sci Urbana IL 61801 USA

This paper proposes an algorithm that uses an estimation of the joint distribution of promising solutions in order to generate new candidate solutions. The algorithm is settled into the context of genetic and evolutionary computation and the algorithms based on the estimation of distributions. The proposed algorithm is called the Bayesian Optimization algorithm (BOA). To estimate the distribution of promising solutions, the techniques for modeling multivariate data by Bayesian networks are used. The BOA identifies, reproduces, and mixes building blocks up to a specified order. It is independent of the ordering of the variables in strings representing the solutions. Moreover, prior information about the problem can be incorporated into the algorithm, but it is not essential. First experiments were done with additively decomposable problems with both nonoverlapping as well as overlapping building blocks. The proposed algorithm is able to solve all but one of the tested problems in linear or close to linear time with respect to the problem size. Except for the maximal order of interactions to be covered, the algorithm does not use any prior knowledge about the problem. The BOA represents a step toward alleviating the problem of identifying and mixing building blocks correctly to obtain good solutions for problems with very limited domain information.

关键词： Genetic and evolutionary computation linkage learning estimation of distribution algorithm probabilistic modeling learning Bayesian networks genetic algorithm

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An Analysis of the Bias of Variation Operators of estimation of distribution Programming 18

An Analysis of the Bias of Variation Operators of Estimation...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Schweim, Dirk Rothlauf, Franz Johannes Gutenberg Univ Mainz Mainz Germany

ISBN: (纸本)9781450356183

estimation of distribution programming (EDP) replaces standard GP variation operators with sampling from a learned probability model. To ensure a minimum amount of variation in a population, EDP adds random noise to the probabilities of random variables. This paper studies the bias of EDP's variation operator by performing random walks. The results indicate that the complexity of the EDP model is high since the model is overfitting the parent solutions when no additional noise is being used. Adding only a low amount of noise leads to a strong bias towards small trees. The bias gets stronger with an increased amount of noise. Our findings do not support the hypothesis that sampling drift is the reason for the loss of diversity. Furthermore, we suggest using property vectors to study the bias of variation operators. Property vectors can represent the distribution of a population's relevant property, such as tree depth or tree size. The Bhattacharyya coefficient of two property vectors is a measure of the similarity of the two distributions of population properties. The results for EDP and standard GP illustrate that search bias can be assessed by representing distributions using property vectors and measuring their similarity using the Bhattacharyya coefficient.

关键词： Search Bias Genetic Programming estimation of distribution Programming estimation of distribution algorithm n-Gram Similarity

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Hybrid optimization for charge planning problem in twin strands continuous casting production

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Journal of Iron and Steel Research International 2021年第5期28卷 520-529页

作者： Jian Yi Shu-jin Jia Bin Du College of Information Science and Engineering Northeastern UniversityShenyang 110819LiaoningChina Institute of Intelligent Manufacturing Baosteel Central Research InstituteShanghai 201900China

Charge planning is one of batching problems for steelmaking and continuous casting production,and its optimization will be conducive to subsequent cast *** planning problem in the twin strands continuous casting production was studied,where casting width of the odd strand might be different from that of the even *** the different widths in the twin strands,the resulting counterweights and the constraints of steelmaking and continuous casting,a multiobjective optimization model was established to minimize the number of charges,the number of scale pairs,the surplus and the upgrading costs of steel ***,a hybrid optimization algorithm combined with heuristic and mutation-based estimation of distribution algorithm was proposed to solve the *** were conducted on several groups of test data collected from practical production orders of *** computational results demonstrate that the proposed algorithm can generate better solutions than the manual *** proposed model and algorithm proved to be effective and practical.

关键词： Steelmaking Continuous casting Production planning Charge Cast estimation of distribution algorithm

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Parameter Control of Genetic algorithms by Learning and Simulation of Bayesian Networks——A Case Study for the Optimal Ordering of Tables

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Journal of Computer Science & Technology 2013年第4期28卷 720-731页

作者： Concha Bielza Juan A. Fernndez del Pozo Pedro Larranaga Computational Intelligence Group Department of Artificial IntelligenceTechnical University of Madrid

Parameter setting for evolutionary algorithms is still an important issue in evolutionary computation. There are two main approaches to parameter setting： parameter tuning and parameter control. In this paper, we introduce self-adaptive parameter control of a genetic algorithm based on Bayesian network learning and simulation. The nodes of this Bayesian network are genetic algorithm parameters to be controlled. Its structure captures probabilistie conditional （in）dependence relationships between the parameters. They are learned from the best individuals, i.e., the best configurations of the genetic algorithm. Individuals are evaluated by running the genetic algorithm for the respective parameter configuration. Since all these runs are time-consuming tasks, each genetic algorithm uses a small-sized population and is stopped before convergence. In this way promising individuals should not be lost. Experiments with an optimal search problem for simultaneous row and column orderings yield the same optima as state-of-the-art methods but with a sharp reduction in computational time. Moreover, our approach can cope with as yet unsolved high-dimensional problems.

关键词： genetic algorithm estimation of distribution algorithm parameter control parameter setting Bayesian network

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A combination method using evolutionary algorithms in initial orbit determination for too short arc

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ADVANCES IN SPACE RESEARCH 2019年第2期63卷 999-1006页

作者： Li, Xin-Ran Wang, Xin Xiong, Yong-qing Chinese Acad Sci Purple Mt Observ Nanjing 210008 Jiangsu Peoples R China Univ Sci & Technol China Hefei 230026 Anhui Peoples R China Chinese Acad Sci Key Lab Space Object & Debris Observat Purple Mt Observ Nanjing 210008 Jiangsu Peoples R China

With the combination of two evolutionary algorithms EDA and DE, a new method of initial orbit determination for satellites based on ground-based too-short-arc is established. Compared with other algorithms, the proposed method focuses on the most densely populated region in the solution space rather than the individual with best fitness value. Both the global information and local information are well fused in the search of optimum. In the method (a, e, M) are treated as variables of the optimization, and the optimization procedure is carried out as a two-stage hierarchical optimization problem which has three variables for each stage. Kernel density estimation is applied to build the probability distribution model without any assumptions of the specified distribution, accompanied by handling semi-major axis and eccentricity as a pair of dependent variables in the construction of the probability for the correlation between them in the practice. Numerical experiments with real ground-based observations show that the proposed method is applicable to too-short-arc with even 3 s, and the result of bias in several kilometers can be achieved with 5 '' error added to angular measurements. (C) 2018 COSPAR. Published by Elsevier Ltd. All rights reserved.

关键词： Too short arc Initial orbit determination estimation of distribution algorithm Differential evolution Evolutionary algorithm

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Design of ultra-low and ultra-flattened dispersion single mode photonic crystal fiber by DE/EDA algorithm

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JOURNAL OF MODERN OPTICS 2009年第12期56卷 1348-1357页

作者： Pourmahayabadi, M. Nejad, S. Mohammad Iran Univ Sci & Technol Dept Elect Engn Nanoptron Res Lab Tehran Iran

This paper proposes a combination of differential evolution (DE) and estimation of distribution algorithm (EDA) to design photonic crystal fiber structures with desired properties over the C communication band. In order to determine the effective index of propagation of the mode and then, the other properties of structure, a finite difference frequency domain (FDFD) solver is applied. The results revealed that the proposed method is a powerful tool for solving this optimization problem. The optimized PCF exhibits a dispersion of 0.22 ps nm-1 km-1 at 1.55 mu m wavelength with a variance of +/- 0.4 ps nm-1 km-1 over the C communication band and a nearly zero dispersion slope.

关键词： differential evolution estimation of distribution algorithm dispersion dispersion slope genetic algorithm photonic crystal fiber

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Transfer weight functions for injecting problem information in the multi-objective CMA-ES

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MEMETIC COMPUTING 2017年第2期9卷 153-180页

作者： Castro, Olacir R., Jr. Pozo, Aurora Lozano, Jose A. Santana, Roberto Univ Fed Parana Comp Sci Dept Ave Coronel Francisco Heraclito dos Santos 210 BR-81531970 Curitiba Parana Brazil Univ Basque Country UPV EHU Intelligent Syst Grp Dept Comp Sci & Artificial Intelligence Paseo Manuel de Lardizabal 1 San Sebastian 20080 Donostia Spain

The covariance matrix adaptation evolution strategy (CMA-ES) is one of the state-of-the-art evolutionary algorithms for optimization problems with continuous representation. It has been extensively applied to single-objective optimization problems, and different variants of CMA-ES have also been proposed for multi-objective optimization problems (MOPs). When applied to MOPs, the traditional steps of CMA-ES have to be modified to accommodate for multiple objectives. This fact is particularly evident when the number of objectives is higher than 3 and, with a high probability, all the solutions produced become non-dominated. An open question is to what extent information about the objective values of the non-dominated solutions can be injected in the CMA-ESmodel for a more effective search. In this paper, we investigate this general question using severalmetrics that describe the quality of the solutions already evaluated, different transfer weight functions, and a set of difficult benchmark instances including many-objective problems. We introduce a number of new strategies that modify how the probabilistic model is learned in CMA-ES. By conducting an exhaustive empirical analysis on two difficult benchmarks of many-objective functions we show that the proposed strategies to infuse information about the quality indicators into the learned models can achieve consistent improvements in the quality of the Pareto fronts obtained and enhance the convergence rate of the algorithm. Moreover, we conducted a comparison with a state-of-the-art algorithm from the literature, and achieved competitive results in problems with irregular Pareto fronts.

关键词： Many-objective Covariance matrix adaptation Optimization Probabilistic modeling estimation of distribution algorithm

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A multi-objective optimization model and its evolution-based solutions for the fingertip localization problem

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PATTERN RECOGNITION 2018年 74卷 385-405页

作者： Gong, Dunwei Liu, Ke China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China

Exact fingertip positions are of particular importance to the fingertip-based human-computer interaction. We build a multi-objective optimization model for the problem of fingertip localization, and present a method to solve the above model based on evolutionary algorithms. When building the model, we take the positions of a series of pixels as the decision variable, the shape of the hand-edge curve corresponding to each of the pixels as one objective function, and the distance between each of the pixels and the gravity center of the palm as the other objective function. In addition, based on the correlation among the positions of pixels of the fingertip regions, we present a multi-objective estimation of distribution algorithm to solve the model so as to obtain the best pixel set, thus gaining the fingertip positions. The experimental results demonstrate the effectiveness of the proposed model and algorithm. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Fingertip localization Multi-objective optimization estimation of distribution algorithm Probability distribution model

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