Optimizing Structure and Connection Weights of Feedforward Neural Networks Using Genetic Algorithms
遗传算法优化前向神经网络结构和权重矢量

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.