Abstract. The genetic algorithms (GAs) are broadly applicable stochastic search and optimization techniques. However, there exists premature convergence phenomenon in some GAs. To overcome the deficiency, two improved genetic algorithms are proposed in this study. The first one is a hybrid algorithm of the genetic algorithm and downhill simplex method, while the second one is the combination of genetic algorithm and conjugate gradient method. Then, the mathematical optimization model of the 10 bar truss is built and both of the improved algorithms are identified by the numerical example and compared with the simple genetic algorithm. The simulation results indicate that the two purposed techniques show stronger robustness in finding feasible optimum designs than the simple genetic algorithm.

How to correctly evaluate the quality of image translation is an important research topic. In this paper, the effects of the hyperparameters and algorithm optimization methods of Pix2Pix model on image translation quality are studied experimentally, and the model parameters and algorithm optimization methods are determined. The average subjective score, peak signal-to-noise ratio and structural similarity of image translation effects are proposed. And other subjective and objective indicators. On the CUFS face database, the image is translated based on the Pix2Pix model and the image translation results are evaluated. Analyze the results of image translation evaluation. The optimization method of image translation algorithm chooses Adam. When the learning rate is 0.001, the subjective index of image translation averages above 4 points, PSNR reaches 14.35, SSIM reaches 0.58, L1 loss reaches 29975251 and above, and cosin is above 0.97, are better than other methods. Finally, the validity of the image quality assessment indicators in this paper is verified on the conditional generation confrontation network model.

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Using the theory of grey system, DM technology and radial basis function (RBF) neural network method, a new model, the combined model of grey system and RBF neural network is setup, which aims at solving the user's received data safety of machine learning. The results show that, in shortterm prediction of data safety of machine learning, GM is an effective way and RBF has perfect ability to study and map. The combined model of grey system and neural network, to a large extent, has the dual properties of trend and fluctuation under the condition of combining with the time-dependent sequence data. It is concluded that great improvement comparing with any method of trend prediction and simple factor in combined grey neural network (CGNN) comparing with the any model of grey system and RBF neural network in data safety of machine learning of machine learning.