Synthetic Personas: Enhancing Demographic Response Simulation through Large Language Models and Genetic Algorithms

Understanding diverse demographic groups presents a significant challenge in market research. In this paper, we introduce a novel system that integrates large language models with genetic algorithms to create synthetic personas capable of generating feedback that approximates real-world human responses. Our experimental evaluation demonstrates that synthetic personas not only exhibit age-differentiated technology usage patterns consistent with documented trends but also benefit from genetic algorithm optimization, which improves response accuracy from 60.4% to 78.5% on training questions and from 62.6% to 68.8% on hidden questions—outperforming human estimators. Moreover, the optimized personas achieve a 51.1% better correspondence with actual income distributions compared to random profiles. This approach makes it possible to rapidly generate feedback without requiring participants, facilitates iterative follow-ups, and systematically enhances demographic representativeness.