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.