Research on Radial Basis Function Neural Network Based on Improved Hierarchical Genetic Algorithm in Early Diagnosis of Alzheimer’s Disease

With the increasing aging level of China’s population, the probability of suffering from Alzheimer’s disease also increases. Genetic Algorithm-Back-Propagation (GA-BP) were introduced and compared, and the effectiveness of Genetic Algorithm-Radial Basis Function (GA-RBF) algorithm in diagnosis and prediction of Alzheimer’s disease was verified by simulation experiments. The results showed that GA-RBF modeling time increased by about 75%, It showed that GA-RBF algorithm can be used helpful to accurately predict the cognitive status of the elderly.

Parallel Hierarchical Genetic Algorithm for Scattered Data Fitting through B-Splines

Curve fitting to unorganized data points is a very challenging problem that arises in a wide variety of scientific and engineering applications. Given a set of scattered and noisy data points, the goal is to construct a curve that corresponds to the best estimate of the unknown underlying relationship between two variables. Although many papers have addressed the problem, this remains very challenging. In this paper we propose to solve the curve fitting problem to noisy scattered data using a parallel hierarchical genetic algorithm and B-splines. We use a novel hierarchical structure to represent both the model structure and the model parameters. The best B-spline model is searched using bi-objective fitness function. As a result, our method determines the number and locations of the knots, and the B-spline coefficients simultaneously and automatically. In addition, to accelerate the estimation of B-spline parameters the algorithm is implemented with two levels of parallelism, taking advantages of the new hardware platforms. Finally, to validate our approach, we fitted curves from scattered noisy points and results were compared through numerical simulations with several methods, which are widely used in fitting tasks. Results show a better performance on the reference methods.

Optimization design based on hierarchic genetic algorithm and particles swarm algorithm

For a lot of data, it is time-consuming and unpractical to get the best combination by manual tests. The genetic algorithm can make up for this shortcoming through the optimization of parameters. In this paper, the advantages of traditional similarity algorithm is studied, the time model and the trust model for further filtering are introduced, and the parameters with the combination of hierarchical genetic algorithm and particle swarm algorithm are optimized. In the collaborative filtering algorithm, genetic algorithm is improved with hierarchical algorithm, and the user model and the algorithm process are optimized using the fitness function of selection, crossover, and variation, along with the optimization of recommendation result set. In the algorithm, the global optimal parameters can be calculated with the optimization of the obtained initial data, and the accuracy of the similarity calculation can also be improved. This study does the recommendation and comparison experiment in the MovieLens Dataset, and the results show that, on the basis of obtaining the nearest neighbor user group, the mixing use of the hierarchical genetic algorithm and the particle swarm algorithm can make more improvement in the recommendation quality than that of the traditional similarity algorithm.