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.

Concerning that the existing methods cannot well detect the salient object boundary and entire region, a new method based on super-pixel segmentation was proposed. Firstly, the bilateral filtering was employed on original images to reduce the local color difference and make the image smoother and more homogeneous; at the same time, the information of salient object edge was retained. The initial detection of salient object's edge was implemented by calculating the pixel' difference within the local window; super-pixel segmentation was adopted to filtered image so that the pixels with the same or similar color were divided into the same super-pixel block, based on this, the local contrast, global contrast and spatial distribution of super-pixel block were considered synthetically to calculate the salient value of each super-pixel block. Finally, the results of the above two parts were fused and optimized by guided filtering. The experiments were conducted on the international open data set MSRA-1000 compared with other seven methods. The average accuracy rate, average recall, and F-measure value of the proposed method are 81.57%, 77.13% and 80.50% respectively. The experimental results show that the proposed method can exact salient object in images effectively and robustly.