Design of an intelligent optimization framework for corporate financial management based on GA-FL-Transformer
Abstract
This paper addresses the challenges in enterprise financial management, such as difficulties in processing multi-source heterogeneous data, poor adaptability to dynamic environments, and a lack of systematic integration in the decision-making process. To tackle these issues, a new intelligent optimization framework, named g enetic a lgorithm -f uzzy l ogic - Transformer ( GA-FL-Transformer ) , is proposed. First , the framework employs the Transformer architecture to achieve unified encoding and feature fusion of multi-source financial data, extracting multi-dimensional features with high discriminative power. Subsequently, an attention-weight-guided co-evolutionary mechanism integrating g enetic a lgorithm (GA) and f uzzy l ogic (FL) is designed. This mechanism incorporates the features and attention weights into chromosome encoding, fitness function formulation, and genetic operations, thereby enabling dynamic optimization of fuzzy rules and membership functions. Finally, an intelligent optimization framework that integrates perception, optimization, and decision-making is constructed, achieving closed-loop optimization from data to decision-making through a bidirectional flow mechanism and supporting continuous learning and self-adjustment of the system in dynamic market environments. R esults on financial datasets from Compustat and CRSP illustrate that the proposed method surpasses comparative models in financial optimization. Ablation experiments further validate the contributions of the Transformer-based feature extraction, genetic algorithm optimization, and fuzzy reasoning mechanism to the system's performance. This study provides a crucial theoretical foundation for enterprises to construct intelligent financial decision-making systems.