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The Convergence of Floating Genetic Algorithms and Its Application in Model Parameter Extraction

SHE Chun feng 1,YANG Hua zhong 2,HU Guan zhang 1,WANG Hui 2(1 Dept.of Applied Mathematics,Tsinghua University,Beijing 100084,China; 2 Dept.of Electronic Engineering,Tsinghua University,Beijing 100084,China)  
Floating genetic algorithms (FGAs) are optimization methods simulating the natural evolution mechanism.FGAs have been widely used in science and technology by virtue of their simplicity,robustness,freedom of calculating the gradient of the objective function,high precision and the ability of solving multi dimensional numerical problems.With the convergence analysis of FGAs,it is proved in this paper that FGAs with the fittest individual holding in each generation can converge to the global optimum while simple FGAs can not.In the light of the theoretical convergence analysis,improved FGAs with the fittest individual holding and the continuous mutation are proposed,which overcome the incompatibility between the high precision and low computational cost.The improved FGAs have been applied to extracting the semiconductor device model parameters,and have gained about 27% reduction to the computational cost.
【Fund】: 国家自然科学基金!( 699730 2 7)资助课题 ;;“九五”攻关课题!( 96 738 0 1 )
【CateGory Index】: TP18
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