Design of an intelligent optimization framework for corporate financial management based on GA-FL-transformer

This article addresses lenges in enterprise financial management, including difficulties in processing multi-source data, limited 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 genetic algorithm-fuzzy logic-Transformer (GA-FL-Transformer), is proposed. First, the framework employs the Transformer architecture to achieve unified encoding and feature fusion across multiple sources of financial data, high-dimensional features with strong discriminative power. Subsequently, an attention-weight-guided co-evolutionary mechanism integrating genetic algorithm (GA) and fuzzy logic (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 via a bidirectional flow mechanism and supporting continuous learning and system-wide self-adjustment. Results on financial datasets from Compustat and CRSP show that the proposed method outperforms competing 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.