提出了新的遗传算法优化设计前向神经网络的结构和权重矢量。这种新方法的创新在于：二值码串和实值码串的混合编码方法即保留了传统遗传法的优点，又具有遗传编程和跗策略的优点。

A new genetic algorithm is proposed to optimize the topology and connection weights for neural networks. The mixed encoding schema of binary and real value code not only retains the advantages of traditional genetic method but also gains the advantages of evolutionary programming and evolution strategies. The offspring generation method which combines the genetic operators and Solis and Wets operator diversifys the search space and speeds up the convergence of genetic search. And the dynamic parameter encoding method for the mixed code can obtain more precise connection weights.