To solve the arrearage problem that puzzled most of the mobile corporations, we propose an approach to forecast and evaluate the credits for mobile clients, devising a method that is of the coalescence of genetic algo...To solve the arrearage problem that puzzled most of the mobile corporations, we propose an approach to forecast and evaluate the credits for mobile clients, devising a method that is of the coalescence of genetic algorithm and multidimensional distinguishing model. In the end of this paper, a result of a testing application in Zhuhai Branch, GMCC was provided. The precision of the forecasting and evaluation of the client’s credit is near 90%. This study is very significant to the mobile communication corporation at all levels. The popularization of the techniques and the result would produce great benefits of both society and economy.展开更多
The Tiny Encryption Algorithm (TEA) is a Feistel block cipher well known for its simple implementation, small memory footprint, and fast execution speed. In two previous studies, genetic algorithms (GAs) were employed...The Tiny Encryption Algorithm (TEA) is a Feistel block cipher well known for its simple implementation, small memory footprint, and fast execution speed. In two previous studies, genetic algorithms (GAs) were employed to investigate the randomness of TEA output, based on which distinguishers for TEA could be designed. In this study, we used quan-tum-inspired genetic algorithms (QGAs) in the cryptanalysis of TEA. Quantum chromosomes in QGAs have the advan-tage of containing more information than the binary counterpart of the same length in GAs, and therefore generate a more diverse solution pool. We showed that QGAs could discover distinguishers for reduced cycle TEA that are more efficient than those found by classical GAs in two earlier studies. Furthermore, we applied QGAs to break four-cycle and five-cycle TEAs, a considerably harder problem, which the prior GA approach failed to solve.展开更多
基金Guangdong Mobile Communication Company Limited Key Item(2001 and 2002)
文摘To solve the arrearage problem that puzzled most of the mobile corporations, we propose an approach to forecast and evaluate the credits for mobile clients, devising a method that is of the coalescence of genetic algorithm and multidimensional distinguishing model. In the end of this paper, a result of a testing application in Zhuhai Branch, GMCC was provided. The precision of the forecasting and evaluation of the client’s credit is near 90%. This study is very significant to the mobile communication corporation at all levels. The popularization of the techniques and the result would produce great benefits of both society and economy.
文摘The Tiny Encryption Algorithm (TEA) is a Feistel block cipher well known for its simple implementation, small memory footprint, and fast execution speed. In two previous studies, genetic algorithms (GAs) were employed to investigate the randomness of TEA output, based on which distinguishers for TEA could be designed. In this study, we used quan-tum-inspired genetic algorithms (QGAs) in the cryptanalysis of TEA. Quantum chromosomes in QGAs have the advan-tage of containing more information than the binary counterpart of the same length in GAs, and therefore generate a more diverse solution pool. We showed that QGAs could discover distinguishers for reduced cycle TEA that are more efficient than those found by classical GAs in two earlier studies. Furthermore, we applied QGAs to break four-cycle and five-cycle TEAs, a considerably harder problem, which the prior GA approach failed to solve.
