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上传时间: 2009-04-20
详细说明： Genetic Algorithms have been used to solve difficult ptimization problems in a number of fields. However, order to solve a problem with GA, the user has to ecify a number of parameters. This parameter tuning a difficult task as different genetic operators are itable in different application areas. This paper oposes a scheme for genetic algorithms where the netic operators are changed randomly. The oposed approach aims to mimic the nature more osely. In this approach, inhomogeneous crossover nd selection techniques are used. A gendered production is also applied where the number of ildren is produced depending on the fertility rate. In ddition, parents may adopt a new child. The age and ath age are added to balance between exploration nd exploitation of the search space. Using these mple approaches, the diversity of the population can maintained efficiently. The experimental results of e proposed algorithm based on a mechanical design oblem show promising results. ...展开收缩
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