# Product Evaluation Prediction Model Based on Multi-Level Deep Feature Fusion
## 3 Model Parameter Optimization Based on Genetic Algorithm  
Due to the prediction model of BiLSTM, DSCNN and Dense layers proposed in this paper, too many adjustable parameters make the problem very complicated. This complexity is not only related to the form of the incoming data at each layer, but also to the choice of the precise structure of the neural network. Consequently, we use the GA to optimize the model structure (as shown in Figure 8), which is evolved according to the theory of biological evolution in nature [25; 26; 27; 28; 29; 30]. Following work needs to be done to ensure the normal execution of the genetic algorithm. Figure 6: CAM structure diagram. Figure 7: SAM structure diagram. 1. First, determine the model structure that needs to be optimized, including the number of deep convolutional layers, the number of BiLSTM layers, the number of fully connected layers, and the number of neurons in each of the above layers. 2. Secondly, set the parameters of the genetic algorithm (as shown in Table 1), where population represents the number of individuals in the population, \(n_{c}\) is the probability of parental chromosome gene recombination, \(n_{m}\) is the probability of gene mutation, and \(E_{p}\) represents the evolutionary generation of the genetic algorithm. 3. The next step is to create the first population of the neural network, each of which is initialized with a random combination of descriptive attributes. In this paper, parameters to be optimized are put into the list and encoded with integers. There are 12 attributes to be optimized in the model, so the chromosomes of individuals in the population can be instantiated into a list containing 12 integers. When the above coding is completed, an individual genotype is formed, which is a possible value for the optimal solution of the model. The size of population in the genetic algorithm represents the number of individuals in the population. Random value is used to randomly assign value to each chromosome of all individuals. At this time, there are 10 types of individuals in the first-generation population, namely, there are 10 models with different structures, and each individual represents a possible optimal solution. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline population & 10 \\ \(n_{c}\) & 0.5 \\ \(n_{m}\) & 0.2 \\ \(E_{p}\) & 20 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Genetic algorithm parameter list. Figure 8: Genetic Algorithm optimization flow chart. After initialising the first generation population, the next step is to train the ten models using the dataset in this paper. The loss function during training is the mean square error (MSE) and the optimiser is Adam, with the goal of minimising the MSE. Other metrics used to evaluate the performance of the models were used to evaluate the performance of the models on the test set after training was completed, with one of the performance metrics used as a function of the fitness of the genetic algorithm to evaluate the merit of the models. 5. When all individuals in the population are evaluated, the higher the fitness, the greater the probability of being selected for retention as a parent. The parent generation left from the previous generation is crossed by two chromosomes with \(\mathrm{n_{c}}\) probability, and gene mutation and generation of the next generation occur with \(\mathrm{n_{m}}\) probability, finally forming a new population. When twenty populations are generated, the iteration of genetic algorithm ends, and the individuals with the highest fitness are selected as the optimal model structure, so as to output the prediction results. The structure of the model optimized by GA in this paper is shown in Figure 9. In Figure 9, 1 represents the DSCNN,2 represents the CBAM attention mechanism, 3 represents the BiLSTM layer, and 4 represents the fully connected layer. The final output is made by the fully connected layer.