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Binary-coded Nonlinear Genetic Algorithm

机译:二进制编码的非线性遗传算法

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摘要

Through mechanism analysis of simple genetic algorithm (SGA), every genetic operator can be considered as a linear transform to the corresponding individuals. S0, some disadvantages of SGA may be solved if genetic operators are modified to nonlinear functions. Thus nonlinear genetic algorithm is introduced In this paper, a new nonlinear genetic crossover and mutation operators combined with Metropolis rules are introduced. This algorithm isn't a hybrid algorithm such as SGA and simulation annealing (SA), so the premature convergence and calculation efficiency can be avoided. The optimization computing of some examples is made to show that the new genetic algorithm is useful and simple.
机译:通过简单遗传算法(SGA)的机制分析,可以将每个遗传算子视为对相应个体的线性变换。 S0,如果将遗传算子修改为非线性函数,则可以解决SGA的一些缺点。因此,引入了非线性遗传算法。本文介绍了一种新的结合Metropolis规则的非线性遗传交叉和变异算子。该算法不是SGA和模拟退火(SA)之类的混合算法,因此可以避免过早的收敛和计算效率。进行了一些实例的优化计算,表明该新遗传算法既实用又简单。

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