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检索条件"主题词=parallel genetic algorithm"

共 259 条记录，以下是211-220 订阅

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parallelized genetic Operations for SBST using Hadoop MapReduce Framework

Parallelized Genetic Operations for SBST using Hadoop MapRed...

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International Conference on Advanced Communication Control and Computing Technologies*

作者： Mayandi, Geethapriya Arumugam, Chamundeswari SSN Coll Engn Dept Comp Sci & Engn Software Engn Madras Tamil Nadu India SSN Coll Engn Dept Comp Sci & Engn Madras Tamil Nadu India

ISBN: (纸本)9781479939145

Search Based Software Testing (SBST) is one of the most explored fields in Software Testing. It suffers from the optimization problem to execute the Software Under Test (SUT). This problem is addressed mostly using genetic algorithm (GA) and it involves three operations namely selection, crossover and mutation to accomplish a global search to yield fitness solution to run the SUT successfully. In existing work, GA is combined with Hadoop MapReduce to give parallel genetic algorithm (PGA). Here, mapper function performs parallel fitness computation and reducer function performs the GA. This PGA generates test suite that makes the entire SUT to get executed. This paper makes an attempt to existing by parallelizing fitness calculation and GA operations to generate search test data for the SUT based on branch coverage criteria.

关键词： genetic algorithm parallel genetic algorithm Search Based Software Testing Test Data Software Under Test

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A Dynamic Multi Agent-Based Approach to parallelizing genetic algorithm

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13th International-Computer-Society-of-Iran-Computer Conference

作者： Momeni, Mohsen Zamanifar, Kamran Univ Isfahan Dept Comp Engn Esfahan Iran

ISBN: (纸本)9783540899846

Multi agent systems have been widely used to parallelizing processes in network-based computation environments. parallelization of computational load could improve the performance of genetic algorithms and decrease their computational time. In this paper, a new distributed multi-agent system has been proposed for parallelizing genetic algorithin in usual networks. This dynamic approach could improve the performance of parallel genetic algorithm in a network-based context. We also provide a new migration policy for proposed architecture which leads us to attain a better performance for parallel genetic algorithm. Experimental results show the efficiency of proposed method comparing to previous studies.

关键词： Distributed Multi Agent System parallel genetic algorithm Mobility Evolutionary Computing Distributed System

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Dynamic Strategy Based parallel GA Coordinated with FACTS devices to Enhance the Power System Security

Dynamic Strategy Based Parallel GA Coordinated with FACTS de...

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General Meeting of the IEEE-Power-and-Energy-Society

作者： Mahdad, B. Srairi, K. Bouktir, T. Univ Biskra Algeria Dept Elect Engn Biskra Algeria Oum Elbouaghi Univ Dept Elect Engn Oum El Bouaghi Algeria

ISBN: (纸本)9781424442409

Under critical situation the main preoccupation of expert engineers is to assure power system security and to deliver power to the consumer within the desired index power quality. The total generation cost taken as a secondary strategy. This paper presents an efficient decomposed GA to enhance the solution of the Optimal Power Flow (OPF) under severe loading conditions. At the decomposed stage the length of the original chromosome is reduced successively and adapted to the topology of the new partition. Two sub problems are proposed to coordinate the OPF problem under different loading conditions: the first sub problem related to the active power planning under different loading factor to minimize the total fuel cost, and the second sub problem is a reactive power planning to make fine corrections to the voltage deviation and reactive power violation using a specified number of shunt dynamic compensators named Static Var Compensators (SVC). To validate the robustness of the proposed approach, the algorithm proposed tested on IEEE 30-Bus under different loading conditions and compared with global optimization methods (GA, EGA, FGA, ACO) and with two robust simulation packages: PSAT and MATPOWER. The results show that the approach proposed can converge to the near solution and obtain a competitive solution at critical situation and with a reasonable time.

关键词： Dynamic control parallel genetic algorithm System loadability FACTS SVC Optimal power flow System security Planning and control

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BFD-NN: Best Fit Decreasing-Neural Network for Online Energy-Aware Virtual Machine Allocation Problems 16

BFD-NN: Best Fit Decreasing-Neural Network for Online Energy...

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7th Symposium on Information and Communication Technology (SoICT)

作者： Thanh Nguyen-Duc Nguyen Quang-Hung Nam Thoai HCMC Univ Technol VNU HCM Fac Comp Sci & Engn Ho Chi Minh City Vietnam

ISBN: (纸本)9781450348157

High performance computing (HPC) clouds consume a lot of energy (kWh);therefore reducing energy consumption is a high priority for any cloud provider. This paper studies the applications of vector bin packing heuristic and Neural Network (NN) to allocate virtual machines (VMs) onto physical machines (PMs) that minimizes total energy consumption of the physical machines. In our scenario, a list of virtual machines from request queue needs to assign to system for every interval time (T) and minimize total energy consumption. We proposed Best Fit Decreasing Neural Network (BFD-NN), which contains an evaluation function (f) that finds the most efficient physical machine for each VM from requests in system. We also proposed a process to optimize weight of coefficients in f for every request which users submit to the system based on information of users' requests in the past by using parallel genetic algorithm (PGA) and Neural Network. Our method is a new approach because it not only uses knowledge from requests of users but also considers time dimension of virtual machines. Two job parallel workload models in parallel Workloads Archive are used to evaluate our approach. The simulation results illustrate that BFD-NN could reduce up to 15% total energy consumption compared with state-of-the-art heuristics (such as Best Fit and First Fit Decreasing) in online allocation virtual machines.

关键词： Cloud Computing Resource Management Energy efficiency Virtual Machine Allocation Vector Bin Packing Neural Network Application parallel genetic algorithm BFD-NN

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Automatic Arabic Text Summarization for Large Scale Multiple Documents Using genetic algorithm and MapReduce 2

Automatic Arabic Text Summarization for Large Scale Multiple...

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2nd Palestinian International Conference on Information and Communication Technology (PICICT)

作者： Al Breem, Sulaiman N. Baraka, Rebhi S. Islamic Univ Gaza Informat Technol Dept Gaza Palestine Islamic Univ Gaza Fac Informat Technol Gaza Palestine

ISBN: (纸本)9781509065387

Multi document summarization focuses on extracting the most significant information from a collection of textual documents. Most summarization techniques require the data to be centralized, which may not be feasible in many cases due to computational and storage limitations. The huge increase of data emerging by the progress of technology and the various sources makes automatic text summarization of large scale of data a challenging task. We propose an approach for automatic text summarization of large scale Arabic multiple documents using genetic algorithm and MapReduce parallel programming model. The approach insures scalability, speed and accuracy in summary generation. It eliminates sentence redundancy and increases readability and cohesion factors between the sentences of summaries. The experiments resulted in acceptable precision and recall scores. This indicates that the system successfully identifies the most important sentences. In Addition to all to that, the approach provided up to 10 x speedup score, which is faster than on a single machine. Therefore, it can deal with large-scale datasets successfully. Finally, the efficiency score of the proposed approach indicates that the large data set utilizes the available resources up to 62%.

关键词： Text Summarization parallel genetic algorithm MapReduce Hadoop Text Mining

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Interval-based Initialization Method for Permutation-based Problems

Interval-based Initialization Method for Permutation-based P...

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2010 IEEE World Congress on Computational Intelligence

作者： Mehdi, Malika Melab, Nouredine Talbi, El-Ghazali Bouvry, Pascal Univ Luxembourg Fac Comp Sci & Commun 6 Rue Richard Coudenhove L-1220 Kalergi Luxembourg INRIA Futurs Parc Sci Haute Borne 40 F-59650 Villeneuve Dascq France

ISBN: (纸本)9781424481262

When dealing with exponential search spaces and when no special knowledge is available on global optima, initial populations for population-based meta-heuristics should be uniformly distributed on the search space in order to sample basins of attraction of all local optima. In this paper, we propose a new initialization strategy for permutation problems. The new method is based on an original tree representation of the search space. Such representation was previously used for exact methods but never for meta-heuristics. The proposed method has been tested using a parallel genetic algorithm implemented in the ParadisEO framework and experimented on the Nationwide Grid5000 experimental grid using the Q3AP (3D QAP) permutation problem. The preliminary results are promising.

关键词： ParadisEO framework exponential search spaces genetic algorithms interval-based initialization method nationwide Grid5000 experimental grid parallel genetic algorithm permutation-based problems population-based metaheuristics search space representation statistical analysis tree searching

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Characterizing large text corpora using a maximum variation sampling genetic algorithm 06

Characterizing large text corpora using a maximum variation ...

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8th Annual genetic and Evolutionary Computation Conference

作者： Patton, Robert M. Potok, Thomas E. Oak Ridge Natl Lab POB 2008MS 6085 Oak Ridge TN 37831 USA

ISBN: (纸本)9781595931863

There exists an enormous amount of information available via the Internet. Much of this data is in the form of text-based documents. These documents cover a variety of topics that are vitally important to the scientific, business, and defense/security communities. Currently, there are a many techniques for processing and analyzing such data. However, the ability to quickly characterize a large set of documents still proves challenging. Previous work has successfully demonstrated the use of a genetic algorithm for providing a representative subset for text documents via adaptive sampling. In this work, we further expand and explore this approach on much larger data sets using a parallel genetic algorithm (GA) with adaptive parameter control. Experimental results are presented and discussed.

关键词： text analysis parallel genetic algorithm intelligent agents

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A Reliable Weighted Feature Selection for Auto Medical Diagnosis 15

A Reliable Weighted Feature Selection for Auto Medical Diagn...

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15th IEEE International Conference on Industrial Informatics (INDIN)

作者： Sahebi, Golnaz Majd, Amin Ebrahimi, Masoumeh Plosila, Juha Tenhunen, Hannu Univ Turku Dept Future Technol Turku Finland Abo Akad Univ Dept Informat Technol Turku Finland KTH Royal Inst Technol Stockholm Sweden Univ Turku Turku Finland

ISBN: (纸本)9781538608371

Feature selection is a key step in data analysis. However, most of the existing feature selection techniques are serial and inefficient to be applied to massive data sets. We propose a feature selection method based on a multi-population weighted intelligent genetic algorithm to enhance the reliability of diagnoses in e-Health applications. The proposed approach, called PIGAS, utilizes a weighted intelligent genetic algorithm to select a proper subset of features that leads to a high classification accuracy. In addition, PIGAS takes advantage of multi-population implementation to further enhance accuracy. To evaluate the subsets of the selected features, the KNN classifier is utilized and assessed on UCI Arrhythmia dataset. To guarantee valid results, leave-one-out validation technique is employed The experimental results show that the proposed approach outperforms other methods in terms of accuracy and efficiency. The results of the 16-class classification problem indicate an increase in the overall accuracy when using the optimal feature subset. Accuracy achieved being 99.70% indicating the potential of the algorithm to be utilized in a practical auto-diagnosis system. This accuracy was obtained using only half of features, as against an accuracy of 66.76% using all the features.

关键词： Data Analysis Feature Selection K-Nearest Neighbor Classification Optimization parallel genetic algorithm E-Health

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Biclustering of Gene Expression Microarray data using Dynamic deme parallelized genetic algorithm (DdPGA)

Biclustering of Gene Expression Microarray data using Dynami...

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IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

作者： Mishra, Shreya Vipsita, Swati IIIT Bhubaneswar Dept Comp Sc Engn Bhubaneswar 751003 Odisha India

ISBN: (纸本)9781467389884

Biclustering deals with creating a sub-matrix that shows a high similarity across both genes and conditions. Bi-clustering targets at identifying several biclusters that reveal potential local patterns from a microarray matrix. In this paper, initially sequential evolutionary algorithm (SEBI) is implemented and few drawbacks of the approach were identified. To overcome the drawbacks, parallel strategies such as condition based evolutionary biclustering (CBEB) and coarse grained parallel genetic algorithm (CgPGA) were implemented. To further improve the performance, a new parallel genetic algorithm using dynamic demes strategy is implemented. This method uses global parallelization (master-slave model) with coarse-grained GA with overlapping subpopulation model. The primary objective is to find biclusters with minimum overlapping, large row variance, low mean square residue (MSR) and covering almost every element of expression matrix, thus minimizing the overall fitness value. Sequential EA and condition based EA (CBEB) is implemented but it was observed that both took too much time to meet the stopping criteria. So, to improve the efficiency of the genetic algorithm (GA), parallel GA has been implemented with dynamic deme strategy to reduce the execution time of GA and find good quality biclusters. DdPGA yielded good quality biclusters and search space could be increased by implementing this strategy. This experiment was implemented on yeast Saccharamyces dataset.

关键词： Biclustering genetic algorithm parallel genetic algorithm Mean squared residue Volume Row variance

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Computation of Molecular Electronic Structure by genetic algorithm

Computation of Molecular Electronic Structure by Genetic Alg...

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2nd International Conference on Neural Network and genetic algorithms in Materials Science and Engineering

作者： Sharma, Rahul Saha, Rajendra Nandy, Subhajit Bhattacharyya, Shankar Prasad Chaudhury, Pinaki Indian Assoc Cultivat Sci Dept Phys Chem Kolkata 700032 India Univ Calcutta Dept Chem Kolkata India

Several strategies based on genetic algorithms have been explored to locate the ground state energy and structure of atoms and molecules. A variational recipe in a finite basis has been invoked in one of the strategies leading to a matrix eigenvalue problem that has been solved by GA with simultaneous optimization of the basis. In the second approach, the molecular electronic Schrodinger equation has been solved by genetic algorithm by directly optimizing the electronic probability amplitude distribution in space. In the third approach, the single-particle density matrix in atomic orbital basis has been treated as the basic unknown quantity. The search for the optimal ground state density has been simplified by a unitary transformation on the trial density which has been optimized by GA.

关键词： Basis optimization Born-Oppenheimer method Constrained variation Density matrix Density-GA method Direct density method Electronic structure calculation genetic algorithm Nonlinear optimization Optimization parallel genetic algorithm Polythiophene oligomers Rayleigh Quotient minimization Soft computing Su-Schrieffer-Heeger Hamiltonian

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