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Non-dominated sorting genetic algorithm II is a classical multi-objective optimization algorithm but it suffers from poor diversity and the tendency to fall into a local optimum. In this paper, we propose an improved non-dominated sorting genetic algorithm, which aims to address the issues of poor global optimization ability and poor convergence ability. The improved NSGA-II algorithm not only uses Levy distribution for global search, which enables the algorithm to search a wider range, but also improves the local search capability by using the relatively concentrated search property of random walk. Moreover, an adaptive balance parameter is designed to adjust the respective contributions of the exploration and exploitation abilities, which lead to a faster search of the algorithm. It helps to expand the search area, which increases the diversity of the population and avoids getting trapped in a local optimum. The superiority of the improved NSGA-II algorithm is demonstrated through benchmark test functions and a practical application. It is shown that the improved strategy provides an effective improvement in the convergence and diversity of the traditional algorithm.

Cross-domain classification is a challenging problem, in which, how to learn domain invariant features is critical. Recently, significant improvements to this problem have emerged with the wide application of deep learning models, which have been proposed to learn higher level and robust feature representation. Marginalized stacked denoising autoencoder model (mSDA) has proved to be effective to address this problem. However, the performance of mSDA is sensitive to the noise probability. In previous works, the noise probability is usually set as a constant value by cross-validation in the source domain. There is few work focus on the relationship between the noise probability and cross-domain task. In this paper, we try to compute the value of noise probability adaptively. Thus, an approach called Marginalized Stacked Denoising Autoencoders with Adaptive noise Probability (mSDA-AP) is proposed. Firstly, we extract an informative feature space by an improved index, weighted log-likehood ratio (IWLLR), then aggregate these informative features by weighting. Secondly, we compute the value of noise probability adaptively according to the distance between source domain and target domain, and then with the adaptive noise probability, we disturb the input data to learn a stronger feature space with mSDA. Finally, experimental results show the effectiveness of our proposed approach.

The group sparse representation (GSR) model combines local sparsity and nonlocal similarity in image processing, and achieves excellent results. However, the traditional GSR model and all subsequent improved GSR models convert the RGB space of the image to YCbCr space, and only extract the Y (luminance) channel of YCbCr space to change the color image to a gray image for processing. As a result, the image processing process cannot be loyal to each color channel, so the repair effect is not ideal. A new group sparse representation model based on multi-color channels is proposed in this paper. The model processes R, G and B color channels simultaneously when processing color images rather than processing a single color channel and then combining the results of different channels. The proposed multi-color-channels-based GSR model is compared with state-of-the-art methods. The experimental contrast results show that the proposed model is an effective method and can obtain good results in terms of objective quantitative metrics and subjective visual effects.