Research on Extreme Points Optimizing of Nonlinear Multi-Peak Function Based on Genetic Algorithm

Abstract:

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In the application of Genetic Algorithm (GA) to solve the function optimization problem, different encoding methods have different effect on performance of GA. Aiming at the global optimization problem of a class of nonlinear multi-peak function, the paper utilized binary coding and floating coding methods for genetic optimization and analyzed their performance. The experimental result of four kinds of typical nonlinear multi-peak function showed that under the precondition of given genetic operator, the optimizing performance of floating coding method to optimize nonlinear multi-peak function with isolated extreme points is less that the binary coding. The tuning ability of floating coding is stronger. As to the ordinary multi-peak function, the search affect is better than binary coding.

Info:

Periodical:

Advanced Materials Research (Volumes 121-122)

Edited by:

Donald C. Wunsch II, Honghua Tan, Dehuai Zeng, Qi Luo

Pages:

304-308

DOI:

10.4028/www.scientific.net/AMR.121-122.304

Citation:

L. G. Yang "Research on Extreme Points Optimizing of Nonlinear Multi-Peak Function Based on Genetic Algorithm", Advanced Materials Research, Vols. 121-122, pp. 304-308, 2010

Online since:

June 2010

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Price:

$35.00

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