文献类型：会议论文

英文题名：Research on Floating Point Representation Denoising Mutation Based on GFMRA

作者：Cui, Mingyi[1];Zhang, Xinxiang[1];Su, Baiyun[2]

第一作者：崔明义

通讯作者：Cui, MY[1]

机构：[1]Henan Univ Finance & Econ, Sch Comp & Informat Engn, Zhengzhou 450002, Peoples R China;[2]Henan Univ Finance & Econ, Dept Math & Informat Sci, Zhengzhou 450002, Peoples R China

第一机构：河南财经政法大学计算机与信息工程学院

通讯机构：[1]corresponding author), Henan Univ Finance & Econ, Sch Comp & Informat Engn, Zhengzhou 450002, Peoples R China.|[1048412]河南财经政法大学计算机与信息工程学院;[10484]河南财经政法大学;

会议论文集：International Conference on Artificial Intelligence and Computational Intelligence

会议日期：NOV 07-08, 2009

会议地点：Shanghai, PEOPLES R CHINA

语种：英文

外文关键词：Artificial intelligence - Computation theory - Genetic algorithms - Multiresolution analysis

摘要：Multiresolution analysis (MRA) was an important method of constructing wavelet. Generalized frame multiresolution analysis (GFMRA) could construct any orthonormal wavelet based on single mother function. Floating point representation (FPR) was superior to other representation in function optimization and restriction optimization. The noise that FPR brings about influenced badly the performance of genetic algorithm in genetic operation environment. This paper was dependent on theoretical analysis. It presented floating point repreaentation genetic algorithm (FPRGA) based on GFMRA (FPRGAG). FPRGAG was a method of FPR denoising mutation by orthonormal wavelet. The experiments were made on FPRGAG. The results of the theoretical research and the experiments in it indicate which FPRGAG is superior to other used algorithms, in convergence efficiency and precision. The method is reliable in theory, is feasible in technique.