A Data Allocation Strategy Algorithm for Large Databases Based on Genetic Algorithm

Xiao Feng Li; Yuan Xin Tang; Cui Cui Gong

doi:10.4028/www.scientific.net/AMR.268-270.898

Paper Titles

Design and Simulation Analysis of Tamper System for Multifunction-Paver
p.875

First-Principle Calculation on Lithium Insertion of NiSn₃Sb₄ Alloy
p.881

First-Principles Calculations of Elastic and Thermodynamic Properties of Cubic CdTe
p.886

Circumferential SH Wave in the Inhomogeneous Magneto-Electro-Elastic Cylindrical Curved Plates
p.892

A Data Allocation Strategy Algorithm for Large Databases Based on Genetic Algorithm
p.898

A Class of Gradient-Type Methods with Perturbations
p.904

H¹ -Galerkin Expanded Mixed Finite Element Methods for Pseudo-Hyperbolicequations
p.908

Numerical Simulation of Sea Ice Compressional Strength with Discrete Element Model
p.913

Application of USB High-Speed Signal Lines in the Analysis of Electromagnetic Interference
p.919

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 268-270A Data Allocation Strategy Algorithm for Large...

A Data Allocation Strategy Algorithm for Large Databases Based on Genetic Algorithm

Abstract:

The distributed database system is the product that the database system combines with the computer network system. The data distribution problem has great influence on distributed database application system improvement, data availability, the efficiency and reliability of the distributed database. The allocation strategies in this paper have used some excellent properties in genetic algorithms, including higher parallelism and robustness, the realization of standard way, and to maintain good balance between the depth prior search and breadth prior search, etc, so the allocation strategies in this article's have high execution efficiency, with stronger ability in seeking the best global solution and easy to realize.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 268-270)

Pages:

898-903

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.268-270.898

Citation:

Cite this paper

Online since:

July 2011

Authors:

Xiao Feng Li, Yuan Xin Tang, Cui Cui Gong

Keywords:

Data Allocation, Database, Genetic Algorithm (GA)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Sungoc, Sokeul, A New Approach for Optimizing Parameters of Controller Using Genetic Algorithms, International Sciences, vol. 41, supp. 1, (2004).

Google Scholar

[2] Rajinderb, Kaare, Research on Automatic Pressure Force Loading System for Shell of Spacecraft, International Journal Sciences, vol. 35, no. 1, (1998).

Google Scholar

[3] Timebleby, H. W. A method of parametric optimization in a nonlinear control system based on genetic algorithm, ACM Transactions on Computer-Human , (2004).

Google Scholar

[4] Sucrow, B. E. Using Genetic Algorithms to Optimize PID Controller Parameters, Journal of Integrated Design & Process Science, 5(1), 2000, pp.87-114.

Google Scholar

[5] JIANG Le-tian, XU Guo-zhi, PID Control Based on GA-BP Neural Network, Journal of System Simulation (in Chinese), vol. 14(6), 2002, pp.796-799.

Google Scholar

[6] Timebleby, H. W. and Gow J, Application of Genetic Algorithms in Structure Vibration Control, Proceedings of the 9th International Conference on Intelligent User Interface, Island of Madeira, 2004, pp.366-367.

Google Scholar

[7] R.G. Yan, Application of Fuzzy-PID Control to the Control of the Electric Heater Based on Genetic Algorithm, Mining and Metallurgical engineering, vol. 23, no. 2, 2003, pp.7-10.

Google Scholar

[8] Brad A. Myers, Rosson, Controller design and optimization using genetic programming algorithm, Proceedings of the SIGCHI conference on Human factors in computing systems, (2000).

Google Scholar

[9] Philip J. A. Scown, Barbara Mcmanns, Parameters optimization of fuzzy controller based on genetic algorithm", report on conference web, 1995. Lee D. and Yannakakis M. "Multi-Population Parallel Genetic Algorithm for Parametric Optimization in No-Linear System, IEEE Transactions on Software Engineering, 84(8), pp.1090-1123, (1996).

Google Scholar