Abstract

In the era of big data and advanced analytics, the application of soft computing techniques has emerged as a powerful tool in solving complex business problems. This paper presents the use of hybrid genetic algorithms (HGAs) in business analytics to address challenges related to optimization, prediction, and decision-making processes. Traditional algorithms often struggle with large, nonlinear, and dynamic datasets typical of business environments. The incorporation of soft computing techniques such as genetic algorithms (GAs) and their hybridization with other methods like fuzzy logic and neural networks can help overcome these limitations. The problem addressed in this research is optimizing decision-making in marketing strategies, focusing on maximizing return on investment (ROI). Standard methods face difficulties in navigating through vast datasets and discovering optimal solutions. The hybrid genetic algorithm proposed in this study combines the exploration strength of GAs with the exploitative precision of local search techniques. The model was tested using a real-world dataset of marketing expenditures and revenues from a retail company. The HGA achieved an ROI improvement of 25%, significantly outperforming standard GAs and traditional optimization methods, which yielded only a 12% improvement. The flexibility and efficiency of this approach make it ideal for various business applications, including supply chain optimization, customer segmentation, and product pricing.