## 1 Introduction
In 1943, [PERSON] and [PERSON] first proposed the concept of artificial neural networks [10]. Neuroscientist [PERSON] successively completed the simulation of the perceptron and the construction of a neural computer based on the perceptron in 1957 and 1960 [11]. In 1986, [PERSON] et al. described a new learning process, backpropagation, for networks of neuron-like units. This process repeatedly adjusts the weights of the connections in the network to minimize the measure of the difference between the actual output vector and the desired output vector of the network. Later, in 2006, the concept of deep learning was proposed [12; 13]. Microsoft used the residual learning method based on deep neural networks to increase the classification accuracy of network images to 96.43%, which is already higher than the accuracy of human eye recognition. In 2016, \"AlphaGo\" defeating the South Korean Go champion [PERSON] was a highlight moment for the application of deep learning achievements. Professor [PERSON] pointed out in the keynote report \"Thoughts on Evolutionary Optimization and Deep Learning\" at the 2021 China Computer Congress \"The Development Trend of the Next Generation of Evolutionary Computing\" forum that the key to evolutionary optimization and brain-inspired deep learning is to develop artificial intelligence technology with the idea of processing knowledge with the brain and natural evolution. Among them, thanks to the theory of natural immunology, evolutionary optimization algorithms have the characteristics of being able to utilize experience, having high fault tolerance and robustness, and the combination of the algorithm and practical problems is closer [14]. In addition, data mining methods based on neural network learning are also widely used in the electromagnetic field. [PERSON] et al. proposed an aircraft target recognition algorithm based on the BP neural network with a recognition rate of 90% [15]. [PERSON] et al. invented a Radar Cross Section (RCS) near-field and far-field transformation method based on deep neural networks, providing a new perspective on the calculation of the target radar scattering area [16]. [PERSON] et al. used deep neural networks for target RCS sequence feature recognition, proposed to extract the fractional-dimensional features of the RCS sequence using fractal analysis, and selected traditional features using the Fisher discrimination rate [17]. However, the abovementioned deep learning models have problems, such as the need for a large amount of data for neural network training, being prone to overfitting of training weights, and high technical requirements for equipment. In 2017, [PERSON] proposed the capsule network, referred to as CapsNet, which may replace the traditional CNN network, bringing new opportunities to the field of deep learning [18]. For example, the CapsE model proposed by [PERSON] [19] performed particularly well. Assuming the existence of position information in the triplet and using the sensitivity of the capsule neural network to position information to extract the potential position information within the triplet, and many subsequent models, [PERSON] et al. 's proposed capsule network knowledge graph embedding model based on relational memory pointed out the drawbacks of the capsule neural network, ignoring the positional interdependence of entities in the knowledge graph. Based on the abovementioned background, this paper proposes a multi-parameter optimization method for electromagnetic characteristic fitting based on deep learning, aiming to overcome the shortcomings of existing technologies and improve the accuracy and efficiency of electromagnetic characteristic fitting. The rest of this paper is arranged as follows: In Section 2, we focus on the joint optimization algorithm based on the genetic optimization algorithm and the capsule network. The variables to be optimized include three basic elements: the size of the combination, the combination method of the combination, and the relative placement position of the combination.