Enhancing Advertising Creative Optimization through AI: Leveraging Genetic Algorithms and Reinforcement Learning Techniques

Abstract

This research paper delves into the transformative potential of artificial intelligence (AI) in optimizing advertising creatives by integrating genetic algorithms and reinforcement learning techniques. In an era where digital advertising is increasingly data-driven, the demand for personalized and impactful ad creatives poses a significant challenge. We propose a novel framework that leverages genetic algorithms to explore a diverse set of creative designs and content variations, optimizing for specific performance metrics such as engagement rates and conversion rates. Genetic algorithms facilitate the evolution of creative elements by simulating the natural selection process, thus identifying high-performing designs through iterative refinement. Concurrently, reinforcement learning techniques are employed to dynamically adapt the selection strategy based on real-time interaction data, allowing for continuous learning and improvement of ad performance. Our experimental results, conducted across multiple digital advertising campaigns, demonstrate a notable increase in engagement metrics and return on investment compared to traditional A/B testing methods. The study highlights how AI-driven optimization not only enhances creative performance but also reduces the cost and time associated with manual iterative processes. This research contributes to the field by providing a comprehensive methodology for deploying AI in creative optimization and underscores the strategic importance of adaptive learning systems in the future of digital marketing.