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检索条件"主题词=Parallel Genetic Algorithm"

共 262 条记录，以下是71-80 订阅

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Application of parallel genetic algorithm with two-step fitness function forthe design of multi-band single-layer frequency selective surface

Application of parallel genetic algorithm with two-step fitn...

引用

IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications

作者： Zhou, R. Zhuang, W. Ding, D. Z. Chen, R. S. Nanjing Univ Sci & Technol Dept Commun Engn Nanjing 210094 Peoples R China

ISBN: (纸本)9781424410446

The parallel genetic algorithm (PGA) with two-step fitness function is proposed to design the multi-band single-layer frequency selective surface (FSS). The inner-outer flexible generalized minimal residual algorithm combined with fast Fourier transform (FGMRES-FFT) method is used to accelerate the convergence of the system equation of method of moment. An example of the tri-band single-layer FSS structure is given to demonstrate the validity of the present method.

关键词： frequency selective surface parallel genetic algorithm fitness function FGMRES-FFT

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An Enhanced MapReduce Framework for Solving Protein Folding Problem Using a parallel genetic algorithm

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48th Annual Convention of Computer Society of India (CSI)

作者： Narayanan, A. G. Hari Krishnakumar, U. Judy, M. V. Amrita Vishwa Vidyapeetham Amrita Sch Arts & Sci Dept Comp Sci & IT Kochi Kerala India

ISBN: (纸本)9783319031071

parallel genetic algorithms have proved to be a successful method for solving the protein folding problem. In this paper we propose a simple genetic algorithm with optimum population size, mutation rate and selection strategy which is parallelized with MapReduce architecture for finding the optimal conformation of a protein using the two dimensional square HP model. We have used an enhanced framework for map Reduce which increased the performance of the private clouds in distributed environment. The proposed genetic algorithm was tested several bench mark of synthetic sequences. The result shows that GA converges to the optimum state faster than the traditional

关键词： Protein Folding Problem Hadoop MapReduce parallel genetic algorithm

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Distributed/parallel genetic algorithm for Road Traffic Network Division using Step parallelization 4

Distributed/Parallel Genetic Algorithm for Road Traffic Netw...

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Eastern European Regional Conference on the Engineering of Computer-Based Systems ECBS-EERC

作者： Potuzak, Tomas Univ West Bohemia Dept Comp Sci & Engn Fac Sci Appl NTIS European Ctr Excellence Plzen Czech Republic

ISBN: (纸本)9781467379670

In this paper, we present the step parallelization of a dividing genetic algorithm (DGA) for the parallel, distributed, and distributed/parallel computing environments. The DGA is employed in a method for road traffic network division, which we developed. The step parallelization, which performs all steps of the genetic algorithm at least partially concurrently, is an alternative to the commonly used island model for the parallelization of genetic algorithms. All three non-sequential executions of the DGA were thoroughly tested and compared to the sequential DGA. The results are also part of the paper.

关键词： traffic network division distributed genetic algorithm parallel genetic algorithm step paralellization

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Anomalies detection approach using projection pursuit in hyperspectral image based on parallel genetic algorithm

Anomalies detection approach using projection pursuit in hyp...

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7th International Symposium on Instrumentation and Control Technology

作者： Wang, Wei Zhao, Huijie Dong, Chao Beijing Univ Aeronaut & Astronaut Sch Instrument Sci & Optoelect Engn Beijing 100083 Peoples R China

ISBN: (纸本)9780819473639

This anomalies detection approach seeks the directions that maximize the projection index, so as to gain the anomalies structure information. Using genetic algorithm in this approach can search accurate optimal projection directions, but it's a computation-intensive task. So, a parallel algorithm under distributed memory system was presented. The projection directions were searched efficiently by parallel genetic algorithm model, and the projection directions' precision was guaranteed by using a strengthened terminal qualification. Then, the detected anomaly components were wiped off by projecting the data onto the subspace orthogonal to the previous projection directions, and the other anomalies were searched in the residual space. The final task of projection and objects segmentation was also completed in parallel. Using an OMIS hyperspectral data to test the parallel algorithm's performance under an eight-node cluster, the process time reduced from 15 minutes to 2.8 minutes. The results show the validity and comparative good parallel efficiency.

关键词： hyperspectral remote sensing image anomalies detection parallel processing projection pursuit parallel genetic algorithm cluster

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A Fast parallel genetic algorithm for Graph Coloring Problem Based on CUDA 7

A Fast Parallel Genetic Algorithm for Graph Coloring Problem...

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6th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)

作者： Chen, Buhua Chen, Bo Liu, Hongwei Zhang, Xuefeng Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

ISBN: (纸本)9781467392006

Graph coloring problem (GCP) has proven to be an NP hard problem, until now there is no way to solve it in polynomial time. In this paper, a novel parallel genetic algorithm is presented to solve the GCP based on Compute Unified Device Architecture (CUDA). The initialization, crossover, mutation and selection operators are designed parallel in threads. Moreover, the performance of our algorithm is compared with the other graph coloring algorithms using benchmark graphs, and experimental results show that our algorithm converges much more quickly than other algorithms and achieves competitive performance for solving graph coloring problem.

关键词： graph coloring problem best individual preservation method parallel genetic algorithm CUDA

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Improving Clustering via a Fine-Grained parallel genetic algorithm with Information Sharing 17th

Improving Clustering via a Fine-Grained Parallel Genetic Alg...

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17th Australasian Data Mining Conference (AusDM)

作者： Bartlett, Storm Islam, Md Zahidul Charles Sturt Univ Sch Comp & Math Bathurst 2795 Australia

ISBN: (纸本)9789811516993;9789811516986

Clustering is a very common unsupervised machine learning task, used to organise datasets into groups that can provide useful insight. genetic algorithms (GAs) are often applied to the task of clustering as they are effective at finding viable solutions to optimization problems. parallel genetic algorithms (PGAs) are an existing approach that maximizes the effectiveness of GAs by making them run in parallel with multiple independent subpopulations. Each subpopulation can also communicate by exchanging information throughout the genetic process, enhancing their overall effectiveness. PGAs offer greater performance by mitigating some of the weaknesses of GAs. Firstly, having multiple subpopulations enable the algorithm to more widely explore the solution space. This can reduce the probability of converging to poor-quality local optima, while increasing the chance of finding high-quality local optima. Secondly, PGAs offer improved execution time, as each subpopulation is processed in parallel on separate threads. Our technique advances an existing GA-based method called GenClust++, by employing a PGA along with a novel information sharing technique. We also compare our technique with 2 alternative information sharing functions, as well with no information sharing. On 5 commonly researched datasets, our approach consistently yields improved cluster quality and a markedly reduced runtime compared to GenClust++.

关键词： genetic algorithm Clustering K-Means parallel genetic algorithm Data mining Machine learning

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A parallel genetic algorithm with Region Division Strategy to Solve Taxi-Passenger Matching Problem

A Parallel Genetic Algorithm with Region Division Strategy t...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Liu, Yi-Wen Zhang, Xin-Yuan Gong, Yue-Jiao Chen, Wei-Neng Zhang, Jun South China Univ Technol Sch Comp Sci & Engn Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538627266

Taxi-passenger matching plays a crucial role in modern taxi systems. However, currently, the greedy mechanisms are widely adopted, which may limit the quality of services provided by the systems. In this paper, we first formulate the taxi-passenger matching as a global optimization problem by considering the pickup rate and average waiting time of passengers. Then, we propose a parallel genetic algorithm to solve the problem. New operators, including initialization, crossover, and mutation, are designed specifically for the problem. In addition, we use a divide-and-conquer strategy for dimension reduction. The problem is divided into a number of sub-problems according to the geographical locations of passengers and taxis. Each sub-problem is then solved in a parallel way by a sub-component of our proposed algorithm. Experimental results validate the effectiveness and efficiency of the proposed algorithm. It is able to greatly enhance the quality of services provided by the taxi systems.

关键词： Dispatch algorithm taxi system global optimization divide-and-conquer parallel genetic algorithm

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A parallel genetic algorithm Approach For Monitoring Devices Placement

A Parallel Genetic Algorithm Approach For Monitoring Devices...

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2017 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON)

作者： Kseniya, Nechunaeva Alexey, Rodionov ICM & MG SB RAS Novosibirsk Russia

ISBN: (纸本)9781538615966

In this paper, we compare the performance of a simple genetic algorithm with a parallel island model GA for solving the monitoring devices placement problem. We have found that in addition to providing a speeding up through the use of parallel processing the island model GA finds better quality solutions in comparison with the simple GA.

关键词： graph theory parallel genetic algorithm island model gentic algorithm

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A parallel genetic algorithm for Solving the Probabilistic Minimum Spanning Tree Problem

A Parallel Genetic Algorithm for Solving the Probabilistic M...

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9th International Conference on Computational Intelligence and Security (CIS)

作者： Wang, Zhurong Yu, Changqing Hei, Xinhong Zhang, Bin Xian Univ Technol Sch Engn & Comp Sci Xian Peoples R China

ISBN: (纸本)9781479925483

The probabilistic minimum spanning tree (PMST) problem is NP-complete and is hard to solve. However, it has important theoretical significance and wide application prospect. A parallel genetic algorithm based on coarse-grained model is proposed to solve PMST problem in this paper. Firstly, we discuss several problems of determinant factorization encoding, and develop repairing method for illegal individuals. Secondly, a coarsegrained parallel genetic algorithm, which combines message passing interface (MPI) and genetic algorithm, is designed to solve probabilistic minimum spanning tree problems. Finally, the proposed algorithm is used to test several probabilistic minimum spanning tree problems which are generated by the method introduced in the literature. The statistical data of the test results show that the expectation best solution and average best solution obtained by the proposed algorithm are better than those provided in the literature.

关键词： parallel genetic algorithm Probabilistic minimum spanning tree Determinant factorization encoding Message passing interface (MPI)

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The Implementation and Comparison of Two Kinds of parallel genetic algorithm Using Matlab

The Implementation and Comparison of Two Kinds of Parallel G...

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9th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES 2010)

作者： Li Nan Gao Pengdong Lu Yongquan Yu Wenhua Commun Univ China Ctr High Performance Comp Beijing 100024 Peoples R China

ISBN: (纸本)9780769541105

Two kinds of parallel genetic algorithm (PGA) are implemented in this paper based on the MATLAB (R) parallel Computing Toolbox (TM) and Distributed Computing Server T software. parallel for-loops, SPMD (Single Program Multiple Data) block and co-distributed arrays, three basic parallel programming modes in MATLAB are employed to accomplish the global and coarse-grained PGAs. To validate and compare our implementation, both PGAs are applied to run the problem of range image registration. A set of experiments have illustrated that it is convenient and effective to use MATLAB to parallelize the existing algorithms. At the same time, a higher speed-up and performance enhancement can be obtained obviously.

关键词： parallel genetic algorithm MATLAB distributed computing parallel programming

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