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The Studies on Some Improvements of the GA and Their Applications in SVM

Author: DuanYang
Tutor: LiLei
School: Nanjing University of Posts and Telecommunications
Course: Applied Mathematics
Keywords: Genetic Algorithms Support Vector Machine Adaptive Parameter optimization
Type: Master's thesis
Year: 2012
Downloads: 60
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The genetic algorithm is an optimization method to mimic the process of biological evolution . In recent years, not only in theory form a more perfect system algorithm , and its scope of application has also been considerable development . Genetic algorithm constantly improved , some improved method for the treatment of premature phenomenon . In this paper, these improved methods for research base , put forward their own innovation . Do the following work: 1 , put forward a new crossover and mutation probability adaptive iterative formula . The crossover probability to replace the current generation of crossover probability to adaptive iterative formula in the first generation of crossover probability preset , a new iterative formula , get the same mutation rate formula . New adaptive iterative formula to search the global optimum solution efficiency . Algebraic adaptive adjustment of previously established with the evolution of nonlinear fitness function , there is an estimate of the maximum algebraic replace the current algebra , set up with the evolution of a new algebraic nonlinear adaptive adjustment adapt function . Improved genetic algorithm to improve the operating efficiency of the genetic algorithm . 3 , this paper improved genetic algorithm support vector machine and its taken mixed and function of the parameter optimization , optimization method based on improved genetic algorithm take the hybrid kernel function parameters of support vector machine , and the method applied to software which the amount of pre-assessment to find the actual value closer to the exact value .

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