Leveraging Reinforcement Learning and Genetic Algorithms for Real-Time Ad Campaign Optimization

Abstract

This research paper explores an innovative approach to optimizing real-time advertising campaigns by integrating reinforcement learning (RL) and genetic algorithms (GA). The study addresses the challenges faced by marketers in dynamically adjusting ad parameters to enhance engagement and return on investment (ROI) amidst rapidly changing consumer behaviors and competitive landscapes. We propose a hybrid model that leverages the adaptive decision-making capabilities of RL and the evolutionary optimization strengths of GA. In this model, RL agents learn from historical ad performance data to make real-time decisions, while GA is utilized to optimize the hyperparameters of the RL algorithms and the selection of ad creatives and targeting strategies. The paper details the architecture of the model, including a reward mechanism tailored for maximizing ROI while adhering to budget constraints and brand safety requirements. We conduct extensive experiments using data from multiple real-world ad platforms. Our results demonstrate significant improvements in click-through rates (CTR) and conversion rates compared to traditional A/B testing methods and standalone RL or GA approaches. Furthermore, the hybrid model shows robustness in adapting to unforeseen shifts in ad performance metrics. This research lays the groundwork for future advancements in automated ad optimization, providing a scalable solution that could be applied across various digital advertising ecosystems.