Application of genetic algorithms in digital financial resource allocation

Abstract In the wave of digitalization, the financial resource allocation of enterprises faces severe challenges. More than 80% of enterprises have inefficiency problems, and about 60% of them waste resources and suffer huge losses due to traditional methods. Focusing on this background, this paper deeply studies the application of genetic algorithms in digital financial resource allocation. By constructing an innovative model, a coding system combining dynamic scaling real number coding and hierarchical associative symbol coding is adopted, and personalized fitness functions of factors such as income, risk, and flexibility are integrated, as well as genetic operation strategies based on elite retention of competitive selection, resource category and correlation crossover, and adaptive mutation. Experiments are conducted using 5 years of financial data of 50 listed companies to compare financial resource allocation models such as linear programming, traditional genetic algorithms, support vector machines, and decision trees. The results show that the average value of the proposed model in terms of income indicators is 15.6 million yuan, which is significantly higher than other models; the average risk coefficient is 0.35, which is lower than the comparison model; the average resource utilization rate is 85.6%, and the average score of fit with corporate strategy is 8.2 points, both of which are leading. This study provides a more efficient and accurate resource allocation method for corporate financial decision-making and enhances corporate competitiveness.