Research on Neural Network Based on the Improved Adaptive Genetic Algorithm
the adaptive genetic algorithm (AGA), this paper presents an improved adaptive genetic algorithm ( IAGA) which can optimize the weights and thresholds of the neural network. A stock Prediction system based on neural networks and Fuzzy theory is designed. According to the analysis of the history data of the stock, the system predicts this stock's market trend of the following days and makes the decision support for the investors on the stock's market. The experimental results show that the proposed approach has high accuracy, strong stability and improved confidence. Neural network has very strong learning ability, and has been applied widely in artificial intelligence, pattern recognition, and automatic control and etc. Back propagation (BP) algorithm is currently one of the most widely used and mature neural network models. But due to the gradient search techniques adopted in BP algorithm, there is bound to slow convergence, local minimum and so on. The design of the structure of BP neural network is primarily based on the experience of designers. Experiments are repeated in large sample space to select parameters of the structure. There is no theoretical guidance and their weights are usually determined by the gradient method. Thus, it is often difficult to find the optimal network structure and weights through repetitious experiments. But the genetic algorithm (GA) is a optimal search method based on natural selection and principles of genetics, with the characteristic of parallel computing. It can improve the calculation speed and achieve the global optimization. Therefore, the proposal of genetic algorithms to optimize the BP network weights provides a new way for neural network design and training. In this paper, we mainly focus on the improved adaptive genetic algorithm (IAGA) and apply it on stock prediction to analyze its performance. II. GA-BP ALGORITHM BP algorithm has the characteristic of precise optimization, while the genetic algorithm has a strong global search capability and good performance of global optimization. Therefore GA and BP network are combined. GA is adopted to optimize in training; and when the search rang becomes narrow, the BP network is applied for accurate optimizing. It can achieve the purpose of optimizing quickly and efficiently. In this paper, we first determine the threshold and weight values of the BP network, which are used to encode the genes of GA. The population is initialed and then be handled with selection, crossover and mutation operation. The elite individuals are selected to evolve, until the algebra of evolution reaches preset parameters or the individual of the largest fitness is found. The optimal individual, namely, a set of neural network weight and threshold values, are then passed to the BP neural network, which is retrained by the historical data. Finally, the neural network is used to generate predictive values. The flow chart of the algorithm is shown in Figure 1: