Genetic Algorithm: Application to Scattered Data Problems using Lipschitz Interpolation

Author

Garbacik, Neil R. ; Zohdy, Mohammed A.

Author_Institution

Sch. of Eng. & Comput. Sci., Oakland Univ., Rochester Hills, MI

fYear

2008

fDate

16-18 July 2008

Firstpage

56

Lastpage

58

Abstract

In this paper, a low computational method of efficiently and quickly handling large multivariate scattered data sets with a genetic algorithm for design parameter optimization is presented. The method presented combines the use of a genetic algorithm and the linear interpolation technique identified as Lipschitz Interpolation. Using this method we have improved the performance of the algorithm in two ways, the variance of the solution and the total algorithm evaluation time (an improvement of magnitude 90%).

Keywords

genetic algorithms; interpolation; Lipschitz interpolation; design parameter optimization; genetic algorithm; linear interpolation; multivariate scattered data sets; scattered data problems; Algorithm design and analysis; Genetic algorithms; Genetic mutations; Interpolation; MATLAB; Mathematical model; Optimization methods; Particle scattering; Scattering parameters; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Aerospace and Electronics Conference, 2008. NAECON 2008. IEEE National

Conference_Location

Dayton, OH

ISSN

7964-0977

Print_ISBN

978-1-4244-2615-7

Electronic_ISBN

7964-0977

Type

conf

DOI

10.1109/NAECON.2008.4806516

Filename

4806516