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Best Articles 2011




Intelligent Data Acquisition and Information Process Technologies
and Their Applications. Part II.




Vol. 19, Special Issue, February 2013, pp.7-12



Application of an Improved Genetic Algorithm in Network Information Filtering


Min Ren, Baoya Song, Jirong Jiang

School of Mathematic and Quantitative Economics, Shandong University of Finance and Economics, 40 Shungeng Road, Jinan, 250014, China

E-mail: rm_sd@163.com



Received: 4 December 2012   /Accepted: 15 January 2013   /Published: 19 February 2013

Digital Sensors and Sensor Sysstems


Abstract: In order to improve shortcomings of traditional Genetic Algorithm in solving such complicated problems as premature convergence, low search efficiency during late period, and so forth, the paper puts forward a Fuzzy Genetic Algorithm that can be used for text classification and information filtering. According to uncertain factors in network information filtering system, the fuzzy method is used to adjust parameters of traditional Genetic Algorithm, including crossover probability, mutation probability, and feature weights, which are made to change according to the system environment. It is shown that the global optimization capability and convergence speed of Fuzzy Genetic Algorithm are better than those of traditional Genetic Algorithm by experimental results.


Keywords: Genetic Algorithm, Information filtering, Crossover probability, Mutation probability


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