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共 216 条记录，以下是171-180 订阅

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A quantum-inspired evolutionary algorithm for multi-attribute combinatorial auctions

A quantum-inspired evolutionary algorithm for multi-attribut...

引用

international conference on System theory, Control, and Computing (ICSTCC)

作者： Florin Leon Automatic Control and Computer Engineering Faculty Technical University of Gheorghe Asachi IaşI Iasi Romania

Due to the recent research advances on quantum computing, ideas from this field have been increasingly used as a source of inspiration for new variants of evolutionary algorithms. In this paper, the QIEA-SSEHC algorithm is proposed for solving multi-attribute combinatorial auction problems in multi-agent systems, characterized by an evolutionary hill-climbing phase, a steady state model and a repair procedure to keep all the individuals feasible. the results are compared to those of NSGA-II, a well-known multi-objective evolutionary algorithm, and convergence and diversity metrics are used to assess the quality of multidimensional solutions.

关键词： Evolutionary computation Convergence Optimization Measurement Vectors Sociology Statistics

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Fast computation of R-dimensional DHT with size Q(L/sub 1/)/spl times/Q(L/sub 2/)/spl times/.../spl times/Q(L/sub R/)

Fast computation of R-dimensional DHT with size Q(L/sub 1/)/...

引用

international conference on Signal Processing Proceedings (ICSP)

作者： Yonghong Zeng Guoan Bi A.C. Kot National University of Defense Technology China School of EEE Nanyang Technological University Singapore

Fast algorithms are presented for multi-dimensional discrete Hartley transform (MD-DHT) with size q(l/sub 1/)/spl times/q(l/sub 2/)/spl times/.../spl times/q(l/sub r/), where q is an odd prime number, and r>1 is the number of dimensions. By using the multi-dimensional polynomial transform, the MD-DHT can be converted into a series of reduced one-dimensional DHTs. Compared to other fast algorithms, the proposed one substantially reduces the overall computational complexity and has a simple computational structure.

关键词： Signal processing algorithms Discrete transforms Optical signal processing Multidimensional signal processing Video signal processing Polynomials Content addressable storage Bismuth Image converters Kernel

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Mining frequent serial episodes over uncertain sequence data

Mining frequent serial episodes over uncertain sequence data

引用

16th international conference on Extending Database Technology, EDBT 2013

作者： Wan, Li Chen, Ling Zhang, Chengqi Computer Science and Technology College Chongqing University China Centre for Quantum Computation and Intelligent Systems University of Technology Sydney Australia UTS/QCIS Australia State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China

ISBN: (纸本)9781450315975

Data uncertainty has posed many unique challenges to nearly all types of data mining tasks, creating a need for uncertain data mining. In this paper, we focus on the particular task of mining probabilistic frequent serial episodes (P-FSEs) from uncertain sequence data, which applies to many real applications including sensor readings as well as customer purchase sequences. We first define the notion of P-FSEs, based on the frequentness probabilities of serial episodes under possible world semantics. To discover P-FSEs over an uncertain sequence, we propose: 1) an exact approach that computes the accurate frequentness probabilities of episodes;2) an approximate approach that approximates the frequency of episodes using probability models;3) an optimized approach that efficiently prunes a candidate episode by estimating an upper bound of its frequentness probability using approximation techniques. We conduct extensive experiments to evaluate the performance of the developed data mining algorithms. Our experimental results show that: 1) while existing research demonstrates that approximate approaches are orders of magnitudes faster than exact approaches, for P-FSE mining, the efficiency improvement of the approximate approach over the exact approach is marginal;2) although it has been recognized that the normal distribution based approximation approach is fairly accurate when the data set is large enough, for P-FSE mining, the binomial distribution based approximation achieves higher accuracy when the the number of episode occurrences is limited;3) the optimized approach clearly outperforms the other two approaches in terms of the runtime, and achieves very high accuracy. © 2013 ACM.

关键词： Normal distribution

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A convergent solution to two dimensional linear discriminant analysis

A convergent solution to two dimensional linear discriminant...

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IEEE international conference on Image Processing

作者： Wei Chen Kaiqi Huang Tieniu Tan Dacheng Tao National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China School of Computer Engineering Nanyang Technological University Singapore

the matrix based data representation has been recognized to be effective for face recognition because it can deal with the undersampled problem. One of the most popular algorithms, the two dimensional linear discriminant analysis (2DLDA), has been identified to be effective to encode the discriminative information for training matrix represented samples. However, 2DLDA does not converge in the training stage. this paper presents an evolutionary computation based solution, referred to as E-2DLDA, to provide a convergent training stage for 2DLDA. In E-2DLDA, every randomly generated candidate projection matrices are first normalized. the evolutionary computation method optimizes the projection matrices to best separate different classes. Experimental results show E-2DLDA is convergent and outperforms 2DLDA.

关键词： Linear discriminant analysis Evolutionary computation Tensile stress Pattern recognition Face recognition Error analysis Laboratories Automation Content addressable storage Optimization methods

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Riccati Equation Solution Method for the computation of the Extreme Solutions of X+A*X-1A=Q and X-A*X-1A=Q

Riccati Equation Solution Method for the Computation of the ...

引用

international conference on Systems, Signals and Image Processing, IWSSIP

作者： Maria Adam Francesca Sanida Nicholas Assimakis Stamatis Voliotis Department of Computer Science and Biomedical Informatics University of Central Greece Lamia Greece Department of Electronics Technological Educational Institute of Lamia Lamia Greece Department of Electrical Engineering Technological Educational Institute of Chalkida Psachna Greece

Algebraic and recursive methods for computing the extreme solutions of the matrix equations X + A*X -1 A = Q and X - A*X -1 A = Q are presented in this paper. the proposed algorithms are based on the solution of the c... 详细信息

关键词： Riccati equations Biomedical computing Educational technology Computer science Biomedical informatics Control theory Dynamic programming Stochastic processes Filtering theory Statistics

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Study on Potability Water Quality Classification Based on Integrated Learning

Study on Potability Water Quality Classification Based on In...

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international conference on Intelligent System and Knowledge Engineering, ISKE

作者： FenFen Li Lei Zhou Ting Chen Faculty of Computer and Software Engineering Research Center for logic and Intelligent Computation National-Local Joint Engineering Lab of System Credibility Automatic Verification Huaiyin Institute of Technology Huaian China

ISBN: (纸本)9781665405546

Aiming at the problem of low accuracy of simple classification model, based on the drinking water data set of kaggle official website, integrated learning models is proposed, which improves the precision and accuracy of binary classification of drinking water quality. K-fold cross validation was performed to measure model consistency. the effect of set classifier is higher than that of simple classifier.

关键词： Training Knowledge engineering Machine learning algorithms Fitting Water quality Machine learning Predictive models

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Efficient Fitness Function computation of Genetic Algorithm in Virtual Machine Placement for Greener Data Centers

Efficient Fitness Function Computation of Genetic Algorithm ...

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IEEE international conference on Industrial Informatics (INDIN)

作者： Zhe Ding Yu-Chu Tian Maolin Tang School of Electrical Engineering and Computer Science Queensland University of Technology GPO Box 2434 Brisbane QLD Australia

Energy efficiency is a critical issue in the management and operation of data centers, which form the backbone of cloud computing. Virtual machine (VM) placement has a significant impact on energy efficiency improvement for data centers. Among various methods to solve the VM placement problem, genetic algorithm (GA) has been well accepted for its quality of solutions. However, GA is also computationally demanding, particularly in its fitness, limiting further improvement in energy efficiency of data centers in the scenarios where a fast solution is required. To address this issue, this paper formulates the VM placement problem for energy efficiency as a constrained optimization problem. then, employing GA to solve the optimization, it presents an approach for efficient computation of GA fitness function. the improved computational efficiency is achieved through a new data structure design, which reduces the complexity of the computation from quadratic to linear, to the input size of the problem. Experimental studies show a huge computation time saving from our approach over the existing technique, which is basically Brute-force.

关键词： Genetic algorithms Data centers Mathematical model Energy consumption Data structures Virtual machining Optimization

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Multi-objective Scatter Search Algorithm for Combinatorial Optimisation

Multi-objective Scatter Search Algorithm for Combinatorial O...

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international conference on Advanced Computing and Communications (ADCOM)

作者： A. Rama Mohan Rao K. Lakshmi Structural Engineering Research Centre CSIR Campus Chennai India

In this paper we propose a new meta-heuristic algorithm for solving combinatorial optimization problems. the proposed algorithm follows the scatter search structure developed earlier for single objective combinatorial optimisation, but uses crossover operator borrowed from the field of evolutionary algorithms. the resulting hybrid algorithm is built with typical features like Pareto dominance, density estimation, and an external archive to store the non-dominated solutions in order to handle multiple objectives. the performance of the proposed multi-objective scatter search algorithm is demonstrated by solving a laminate composite cylindrical shell subjected to both combinatorial as well as design constraints. Further, the proposed algorithm is compared with four state-of-the-art multi-objective optimizers: Non-dominated sorting Genetic Algorithm (NSGA-II), Pareto archived evolutionary strategy (PAES) and Micro GA. the studies presented in this paper indicate that proposed algorithm produces very competitive Pareto fronts according to the applied convergence metric and it clearly outperforms the other three algorithms.

关键词： Scattering Laminates Evolutionary computation Pareto optimization Algorithm design and analysis Structural engineering Art Sorting Genetic algorithms Convergence

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A Differential Evolution Algorithm with Adaptive Strategies for Constrained Optimization Problem

A Differential Evolution Algorithm with Adaptive Strategies ...

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international conference on computational Intelligence and Security

作者： Cuo Wanma Hecheng Li Erping Song School of Mathematics and Statistics Qinghai Normal University Xining China School of Computer Science and Technology Qinghai Normal University Xining China

ISBN: (纸本)9781665404457;9781665447119

Constndned optimization problems are widely used in real-world applications as optimization models. Due to the complexity of the objective itself as well as too tight constraints, it is difficult to obtain the global optimal solution to these problems. In this manuscript, an improved differential evolutionary algorithm is proposed from the perspective of operator design and constraint handling. Firstly, in order to enhance the exploration ability of the algorithm, a heuristic mutation operator with better point information is constructed. Secondly, an improved dynamic epsilon constraint handling method is developed, in which the value of the epsilon decreases as the iteration number increases. the method can increase effectively the feasible individual in populations. Finally, the simulation results on 10 benchmark functions show that the proposed algorithm is effective and robust when compared with similar algorithms.

关键词： Constraint handling Heuristic algorithms Simulation Sociology Evolutionary computation Benchmark testing Complexity theory

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A Temporal Logic Extension of the RISCAL Model Checker

A Temporal Logic Extension of the RISCAL Model Checker

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IEEE international Scientific conference on Informatics

作者： Wolfgang Schreiner Ágoston Sütö Research Institute for Symbolic Computation (RISC) Johannes Kepler University Linz Austria Johannes Kepler University Linz Austria

We report on a new extension of the RISCAL model checker that allows to specify nondeterministic transition systems by formulas in linear temporal logic (LTL) and to verify them under fairness constraints. this extension is based on an automata-theoretic approach to explicit state model checking; the performance of its implementation is in some representative examples competitive with (in fact mostly superior to) that of TLA +, a widely known tool for system modeling and analysis. thus, while RISCAL was originally developed for describing and analyzing mathematical theories and algorithms over discrete structures, these extensions make RISCAL also a competent checker for formal models of concurrent systems.

关键词： Analytical models Semantics Education Systems modeling Model checking Mathematical models Software

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