Research of Dynamic Parameters Design on Genetic Algorithm

Shu Jie Li; Wei Jia Guo; Zhi Gang Li

doi:10.4028/www.scientific.net/AMM.263-266.2282

Paper Titles

Uncertain Reliability of a Series System via Non-Linear Programming
p.2263

A Mallat Based Wavelet ECG De-Noising Algorithm
p.2267

Research of P2P Searching Technology Based on Distributed Hash Table
p.2271

Study on the Application of Improved Artificial Fish Swarm Algorithm in the Initial Alignment of the SINS
p.2277

Research of Dynamic Parameters Design on Genetic Algorithm
p.2282

An Improvement Research of SWOT Method Based on Analytic Hierarchy Process
p.2287

Spare Parts Support Model Optimization Algorithm
p.2291

A Combinatorial Algorithm for Generalized Maximum Flow Problem in Lossy Network
p.2295

A New Alternating Group Explicit Algorithm for Solving 2D Diffusion Equation
p.2301

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 263-266Research of Dynamic Parameters Design on Genetic...

Research of Dynamic Parameters Design on Genetic Algorithm

Abstract:

To quickly get the global optimum by genetic algorithm, calculated the average and standard deviation of the adaptabilities of chromosomes of one generation, designed the selection operator and the mutation operator by the average and the standard deviation, dynamically adjusted the probability of selection operator and mutation operator and decreased the blindness of genetic algorithm, increased the multiplicity of chromosomes by multiple-point crossover and repetitiveness check. The test shows that the algorithm offered in the article is feasible and effective.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 263-266)

Pages:

2282-2286

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.263-266.2282

Citation:

Cite this paper

Online since:

December 2012

Authors:

Shu Jie Li*, Wei Jia Guo, Zhi Gang Li

Keywords:

Average, Dynamic, Multiple-Point Crossover, Repetitiveness Check, Standard Deviation

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] X.X. Cui: Multiple-Target Evolution Algorithm and Its Application (National Defense Industry Press, CHINA 2006) (In Chinese).

Google Scholar

[2] M. Srinivas L.M. Patnaik: Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms, IEEE Transaction on Systems, Vol. 24(1994), pp.656-667.

DOI: 10.1109/21.286385

Google Scholar

[3] M. Hu: Application of Genetic Algorithm in Query's Optimization of Relational Database[D]. College of Software, Tongji University, 2008 (In Chinese).

Google Scholar

[4] F. Cai: Investment Combination Research Based on Improved Genetic Algorithm[D], College of Computer, Inner Mongolia University, 2009 (In Chinese).

Google Scholar

[5] Z.G. Li,W.J. Guo, and S.S. Zhang, Genetic Algorithm Improved by Checking Repetition,. in Proceedings of the First International Conference on Intelligent Human-Machine Systems and Cybernetics, HangZhou, China, pp.466-469, (2009).

DOI: 10.1109/ihmsc.2009.239

Google Scholar