# Real-time task parameter selection method of accounting system based on multi-objective optimization and genetic algorithm

## TL;DR

This paper introduces a new method for improving accounting systems using multi-objective optimization and genetic algorithms, achieving high performance in evaluations.

## Contribution

A novel parameter selection method for accounting systems using multi-objective optimization and genetic algorithms is proposed.

## Key findings

- The proposed method achieved an 87.6% F-value in experiments.
- The method also attained 83.5% mAP and 83.4% accuracy.
- The approach is positioned at an advanced global level for enhancing accounting systems.

## Abstract

The progress of the digital economy has promoted the enterprise accounting system. To accelerate the update and evolution of accounting systems, we propose a parameter selection method based on multi-objective optimization and genetic algorithm. Firstly, this article proposes an accounting feature extraction method based on multimodal information embedding. The dual-branch structure and feature pyramid network are used to realize the feature extraction of the information involved in accounting. Then, this article proposes a multi-objective parameter selection method based on a parallel genetic algorithm. By embedding a genetic algorithm in the process of dual-branch model training, the model’s ability to sense accounting information is improved. Finally, using the above two methods, an accounting system evaluation method upon recurrent Transformer is proposed to improve the financial situation of enterprises. Our experiments have proven that our approach attains a remarkable performance with an 87.6% F-value, 83.5% mAP value, and 83.4% accuracy. These results position our method at an advanced level globally, showcasing its capability to enhance accounting systems.