Enhancing Ad Targeting Optimization Using Reinforcement Learning and Genetic Algorithms in AI-Driven Systems

Abstract

This research paper investigates an innovative approach to ad targeting optimization by integrating reinforcement learning (RL) and genetic algorithms (GA) within AI-driven systems. As digital advertising continues to evolve, ensuring precise targeting has become paramount for maximizing return on investment. Traditional methods often grapple with balancing adaptation to user behavior changes and maintaining computational efficiency. Our proposed hybrid model leverages the adaptive capabilities of RL to dynamically learn and predict user preferences, while employing GA to efficiently explore and optimize the vast search space of ad placement strategies. Empirical evaluations were conducted using real-world ad interaction datasets, where the hybrid model's performance was measured against baseline methodologies, including standard RL, GA, and conventional rule-based systems. Results indicate that the integration of RL and GA significantly enhances the accuracy and relevance of ad delivery, achieving an average improvement of 18% in click-through rates (CTR) and a 12% reduction in computational overhead. The hybrid model also demonstrated enhanced adaptability in dynamic environments, effectively responding to shifts in user behavior and market trends. These findings suggest that the combination of RL and GA offers a robust framework for ad targeting optimization, providing both efficiency and scalability in AI-driven advertising systems. Future work will explore the model’s applicability across diverse digital platforms and its potential for real-time implementation.