An Efficient Parameter Optimization Approach Based on Real-Coded Genetic Algorithm

Zhi Qiang Chen

doi:10.4028/www.scientific.net/AMR.479-481.1835

Paper Titles

Research on Application of Damping Vibration Reduction Technology in Marine Gearbox
p.1809

Optimal Sensor Placement for CNC Machine Tools
p.1813

3D-Multi Constrained Heterogeneous Packing Using Genetic Approach
p.1825

Preshaving Hob CAD and the Software Development
p.1831

An Efficient Parameter Optimization Approach Based on Real-Coded Genetic Algorithm
p.1835

Optimization of the Vertex Angle of Metal Saw Tooth Based on Static Analysis
p.1841

Design and Optimization on Connecting Rod Parts of Engineering Clamp Hanger Based on Metamodel
p.1845

A New Method of the Constraints Expression and Handling for Excavator Boom Structural Optimization
p.1851

Applications of Cultural Ant Colony Optimization in Optimal Excavator Mechanisms Design
p.1857

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 479-481An Efficient Parameter Optimization Approach Based...

An Efficient Parameter Optimization Approach Based on Real-Coded Genetic Algorithm

Abstract:

In this paper, we present an efficient parameter optimization approach based on real-coded genetic algorithm, which breeds selectively offspring based on difference between individuals. A new crossover operator is proposed which generates offspring around the center of mass of parents with Laplace distribution. A set of 14 test problems available in the global optimization literature is used to evaluate the performance of proposed genetic algorithm. The comparative study shows that the proposed genetic algorithm performs quite well for parameter optimization problem.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 479-481)

Pages:

1835-1840

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.479-481.1835

Citation:

Cite this paper

Online since:

February 2012

Authors:

Zhi Qiang Chen

Keywords:

Function Optimization, Genetic Algorithm (GA), Intelligent Computing

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] L.J. Eshelman, J.D. Schaffer, in: Real-coded genetic algorithms and interval schemata, Foundation of Genetic Algorithms II, Morgan Kaufmann, San Mateo, CA(1993), pp.187-202.

DOI: 10.1016/b978-0-08-094832-4.50018-0

Google Scholar

[2] K. Deb, R.B. Agrawal, Complex Systems 9 (1995), pp.115-148.

Google Scholar

[3] I. Ono, S. Kobayashi, in: T. Back (Ed.), Proceedings of the Seventh International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, CA(1997), pp.246-253.

Google Scholar

[4] S. Tsutsui, M. Yamamura, T. Higuchi, in: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-1 1999), pp.657-664.

Google Scholar

[5] K. Deb, A. Anand, D. Joshi, Evolutionary Computation Journal 10 (4) (2002), pp.371-395.

Google Scholar

[6] K. Deep, M. Thakur, Applied Mathematics and Computation 188 (2007), pp.895-911.

Google Scholar

[7] S. kobayash, Journal of Japanese Society for Artificial Intelligence, Vol.24, No.1(2009), pp.128-143, In Japanese.

Google Scholar

[8] Z.Q.CHEN, R.L. Wang, International Journal of Innovative Computing, Information and Control, Vol.7, No.8, August (2011), pp.4871-4883.

Google Scholar

[9] Z.Q.CHEN, R.L. Wang, IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E94.A (2011) , No. 6 pp.1417-1425.

Google Scholar

[10] Z.Q.CHEN, R.L. Wang, IEEJ Transactions on Electrical and Electronic Engineering, Vol.4 (2009), pp.663-667.

Google Scholar

[11] Z. Michalewicz, "Genetic Algorithms + Data Structures = Evolution Programs", Springer -Verlag, New York, 1992.

Google Scholar