Whereas the back propagation (BP) neural network may easily go to the local minimum value in the optimization process

a genetic algorithm based BP(GABP) neural network was constructed by combining the genetic algorithm（GA）with the BP neural network. The characteristic of searching the group optimization for GA can prevent the GABP neural network from converging in local optimal solution and ensure that it finds the global optimum or second-best solution with good performance.The training of the GABP neural network was finished in two steps.The GA was firstly used to make a thorough searching in the global space for the weights and thresholds of the neural network

which can ensure they fall into the neighborhood of global optimal solution.Then

in order to improve the convergence precision

the gradient method was used to finely train the network and find the global optimum or second-best solution with good performance.The experimental data of an orthogonal design (temperature

pressure

concentration) for a micro-filtration device (1 μm hydrophilic polyvinylidene fluoride micro-filtration membrane for bovine serum albumin filtration) were used as the sample data for training the GABP neural network

so the well-trained network can be used to predict the flux of the micro-filtration devices.The results showed that this method had greatly improved the convergence speed and the prediction accuracy of the traditional BP network.