Title :
Research on Threshold Denoising of FPRGA
Author :
Cui MingYi ; Zhang XinXiang ; Mi HuiChao
Author_Institution :
Henan Univ. of Finance & Econ., Zhengzhou
fDate :
July 30 2007-Aug. 1 2007
Abstract :
Genetic algorithm (GA) was widely used to engineering and optimization fields. The performance of GA and extension of its application fields were affected by the limitation of its code. Floating point representation (FPR) is super to other codes in function and constraint optimization fields. Noise was brought by the FPR in selection and crossover operation,its influence to the performance of GA was not noticed by re searchers. In this paper, the property of the noise in FPR is mostly analyzed in inherit operation. The mechanism of denoising in FPR is researched with wavelet threshold coefficient. Denoising is implemented by mutation. That wavelet theory used in floating point representation genetic algorithm (FPRGA) is credible for reducing noise level, the method is feasible, is indicated by its results of research and experiment.
Keywords :
floating point arithmetic; genetic algorithms; wavelet transforms; constraint optimization; floating point representation genetic algorithm; threshold denoising; wavelet theory; wavelet threshold coefficient; Additive white noise; Artificial intelligence; Constraint optimization; Distributed computing; Genetic algorithms; Genetic mutations; Noise level; Noise reduction; Random processes; Software engineering; Denoising Mutation; Genetic Algorithm; Threshold; Wavelet Coefficient;
Conference_Titel :
Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2007. SNPD 2007. Eighth ACIS International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-0-7695-2909-7
DOI :
10.1109/SNPD.2007.41