  This paper explores the integration of strategic optimization methods in
search advertising, focusing on ad ranking and bidding mechanisms within
E-commerce platforms. By employing a combination of reinforcement learning and
evolutionary strategies, we propose a dynamic model that adjusts to varying
user interactions and optimizes the balance between advertiser cost, user
relevance, and platform revenue. Our results suggest significant improvements
in ad placement accuracy and cost efficiency, demonstrating the model's
applicability in real-world scenarios.
