Concerning the problem that the interpretable mapping relationship between data features and data categories do not be revealed by general feature selection algorithms. on the basis of Gene Expression Programming (GEP),by introducing the initialization methods, mutation strategies and fitness evaluation methods,an improved Feature Selection classification algorithm based on Layer Distance for GEP(FSLDGEP) was proposed. Firstly,the selection probability was defined to initialize the individuals in the population directionally, so as to increase the number of effective individuals in the population. Secondly, the layer neighborhood of the individual was proposed, so that each individual in the population would mutate based on its layer neighborhood, and the blind and unguided problem in the process of mutation was solved。Finally, the dimension reduction rate and classification accuracy were combined as the fitness value of the individual, which changed the population evolutionary mode of single optimization goal and balanced the relationship between the above two. The 5-fold and 10-fold verifications were performed on 7 datasets, the functional mapping relationship between data features and their categories was given by the proposed algorithm, and the obtained mapping function was used for data classification. Compared with Feature Selection based on Forest Optimization Algorithm (FSFOA), feature evaluation and selection based on Neighborhood Soft Margin (NSM), Feature Selection based on Neighborhood Effective Information Ratio (FS-NEIR)and other comparison algorithms, the proposed algorithm has obtained the best results of the dimension reduction rate on Hepatitis, Wisconsin Prognostic Breast Cancer (WPBC), Sonar and Wisconsin Diagnostic Breast Cancer (WDBC) datasets, and has the best average classification accuracy on Hepatitis, Ionosphere, Musk1, WPBC, Heart-Statlog and WDBC datasets. Experimental results shows that the feasibility, effectiveness and superiority of the proposed algorithm in feature selection and classification are verified.

To solve the Split Delivery Vehicle Routing Problem (SDVRP) more reasonably, overcome the shortcoming that the traditional two-stage solution method of first route and then optimization is easy to fall into local optimization, and handle the problem that the intelligent optimization algorithm fails to integrate competition and cooperation organically in the optimization stage, an Improved Pyramid Evolution Strategy (IPES) was proposed with the shortest delivery path and the least delivery vehicles as the optimization objectives. Firstly, based on the pyramid, the encoding and decoding methods and hierarchical cooperation strategy were proposed to solve SDVRP. Secondly, according to the characteristics such as the random of genetic algorithm, high parallelism of "survival of the fittest" and self-adaption, as well as the different labor division of different layers of pyramid structure, an adaptive neighborhood operator suitable for SDVRP was designed to make the algorithm converge fast to the optimum. Finally, the optimal solution was obtained. Compared with the piecewise solving algorithm, clustering algorithm, particle swarm algorithm, artificial bee colony algorithm, taboo search algorithm,the results of four simulation experiments show that, when solving the optimal path of each case, the proposed IPES has the solution accuracy improved by at least 0.92%, 0.35%, 3.07%, 9.40% respectively, which verifies the good performance of IPES in solving SDVRP.

Analysis and research on interbank market based on multi-layer network structure is conducive to avoiding or weakening the risk impact on financial market. Based on test data simulated by credit lending business scenario, combined with the multi-layer network structure and complex network analysis method of interbank market, the important nodes in interbank market were judged and identified from different angles, meanwhile Jaccard similarity coefficient between the layers and inter-institution Pearson similarity coefficient were calculated and the infectousness of risk contagion of interbank market was measured from macroscopic and microscopic perspectives. The experimental results show that large-scale state-owned financial institutions such as Bank of China and China Development Bank are of high importance in the system, and the greater the similarity between institutions, the greater the infectiousness of risk contagion. Therefore, by calculating the important node measure index in the network layer, comprehensive and complete analysis of the risk contagion of the entire system can help the regulators to achieve accurate monitoring of important institutions in the system. At the same time, from the perspectives of inter-layer analysis and intra-layer analysis, comprehensive measurement of the infectious degree of risk contagion between institutions after financial shock provides policy advice to regulators.

In order to solve the influence of low Signal-to-Noise Ratio (SNR) and large frequency offset on signal acquisition in the capture of direct sequence spread spectrum signal, a new capture method of direct sequence spread spectrum signal based on cascade stochastic resonance was proposed. Firstly, the input signal was filtered by a partial matched filter, when the local pseudo code and the pseudo code phase of the input signal were aligned, only the residual Doppler frequency offset remained in the output signal, and then the signal was processed by the cascaded stochastic resonance to increase the SNR of the input signal. Finally, Fast Fourier Transformation (FFT) spectrum analysis was used to obtain the spectral peak on the frequency spectrum and then calculate Doppler frequency deviation. Theoretical analysis and experimental simulation shows that:the proposed algorithm can improve the acquisition sensitivity of direct sequence spread signals when the input SNR is -26 dB, and the output SNR is improved by 15 dB after two-stage cascade stochastic resonance system; at the same time, the correct detection probability of this algorithm is improved by about 4 dB compared with the traditional capture algorithm. The proposed method not only can suppress noise, but also convert some noise energy into signal energy; meanwhile it can decrease the influence of large Doppler frequency offset. Therefore, it has great advantages in capturing weak signals.

The failure intensity function will be misjudged in software reliability growth model(SRGM), because of the difference between the testing environment and the user environment. A logarithmic poisson model considering environment factor is proposed, which is based on the M-O logarithmic poisson execution time model, the representative of Musa execution-time models. It can represents the change law of the failure intensity function better, and the parameter estimating equations have been provided. The experimental results based on failure data sets, show that the proposed model has better curve fit than some of other models.