Title :
Denoising Mutation of FPRGA Based on Wavelet Decomposition
Author :
Cui, Mingyi ; Cui, Wei
Author_Institution :
Sch. of Comput. & Inf. Eng., Henan Univ. of Finance & Econ., Zhengzhou, China
Abstract :
In order to solve the problem of noises that were generated by operation on floating point representation (FPR) in genetic environment, and its influence on performance of genetic algorithm (GA), FPR genetic algorithm (FPRGA) denoising mutation based on wavelet decomposition (FPRGAWD) is presented by this paper. FPR noises were decomposed with Haar wavelet. The noises are mapped to Haar basis. The algorithm is degined with denoising mutation, and the algorithm is implemented by programming. The experiments were carried out by it. The results of the research and the experiments indicate which the method is superior to other algorithms. Wavelet can be used to GA for improving algorithm performance. This method is reliable.
Keywords :
Haar transforms; genetic algorithms; wavelet transforms; Haar wavelet; denoising mutation; floating point representation; genetic algorithm; wavelet decomposition; Additive white noise; Environmental economics; Finance; Genetic algorithms; Genetic engineering; Genetic mutations; Noise generators; Noise reduction; Stochastic processes; Working environment noise; Denoising mutation; Floating point representation; Genetic algorithm; Wavelet decomposition;
Conference_Titel :
Computational Science and Optimization (CSO), 2010 Third International Joint Conference on
Print_ISBN :
978-1-4244-6812-6
Electronic_ISBN :
978-1-4244-6813-3
DOI :
10.1109/CSO.2010.141